Reviewerly/exHarmony huggingface dataset:
Experiments with a Number of Convolutional Neural Networks for Evaluating the Super-Resolution Technique

The work is devoted the study of the models of convolutional neural networks SRCNN, FSRCNN, and SubPixel CNN, which solve the Super-Resolution task, for finding the optimal parameters during the training stage. To evaluate the results, the standard PNSR metric was used, BSD was taken as the dataset. During analyzing activation functions and optimizers from the PyTorch library, the best combinations, that give a higher quality metric, were identified. Using the weights of the three best combinations obtained, a comparative characterization of the images belonging to the classes of text, face, night, nature, and the generated image was given. The conclusion is made about the evaluation of a particular architecture to obtain an image of a certain class. The finishing results can be used in further studies for improving the productivity and accuracy of SuperResolution methods.
On Dependence of Bert Embeddings on the Syntactic Structure of a Sentence

Experiments demonstrate that Bert embeddings are reflecting syntactic structure of a sentence. In this paper we conduct some experiments demonstrating dependency between cosine similarity of Bert embeddings and position of neighbouring words with consideration of a consistency structure of a sentence. Our experiments demonstrated that Bert embeddings are dependent on the number of started and finished consistencies between those words. The more the number of borders of consistencies, the less is the cosine similarity; finished borders take more influence than started ones. Increasing the distance between words up to 3 cosine similarity decreases. Moreover, we have found that Bert model assigns very close vectors which cosine similarity does not fall less than 0.6. The Word2Vec model, which takes not a context of a word into consideration, does not demonstrate such a behaviour.
Measuring the Phenomenon of Presence with a Break-in-Presence Counter

The paper presents a methodology of presence state estimation for virtual reality environments based on presence counter method. The presence counter is based on counting breaks in presence: moments when the subject just experienced presence, was subjectively inside virtual reality (state V), but this state was broken, and the subject felt himself in ordinary reality, in the laboratory (state R). A generalized mathematical model based on Markov random processes is given, which allows to calculate the probability of presence state on the basis of discrete data using continuous time. The description of a pilot experiment in virtual reality, conducted to verify the described methodology. As part of a pilot experiment, subjects move around a virtual Asian park and shoot at round vases they see. The hits are being counted. Also, sound distractors are turned on for the subjects - signals that return them from state V (the sence of being in virtual reality) to state R (the sence of being in ordinary reality), in fact, stimulating breaks in presence. The results of the pilot study and its prospects are discussed.
Development of a Mobile Application Testing Methodology Based on Eye-tracking Technology

Nowadays, there are numerous mobile applications that, for one reason or another, are used by people to solve everyday problems. From a design perspective, in this paper, 3 factors were identified as influencing the perception of information in the mobile app: color solution, composition solution and font. An experiment was developed, which was carried out using eye- tracking technologies in a special software module. It includes 24 incentives and 2 tasks that participants in the experiment need to solve. The study showed that in incentives in a blue-orange color scheme with a composition in the form of square tiles and a serif font, subjects solve the task faster than in stimuli with a different color scheme with a composition in the form of rectangular tiles and a sans-serif font. Approbation of the developed methodology showed a positive result, but additional research is required for further testing of mobile applications.
DINONAT: Exploring Self-Supervised training with Neighbourhood Attention Transformers

Data-driven methods achieved great progress in wide variety of machine vision and data analysis applications due to new possibilities for collecting, annotating and processing huge amounts of data, with supervised learning having the most impressive results. Unfortunately, the extremely time-consuming process of data annotation restricts wide applicability of deep learning in many applications. Several approaches, such as unsupervised learning or weakly supervised learning has been proposed recently to overcome this problem. Nowadays self-supervised learning demonstrates state-of-the-art performance and outperforms supervised one for many tasks. Another state-of-the-art neural network models are transformer networks, that can rich high performance due to flexibility of the model. Moreover, the quality of the annotation directly influences the quality of the network operating. From this point of view it is important to analyse what features the network uses during the training process. The study of the self attention mechanism allows to identify these features, and use it in annotation process. The current study addresses the problem of self-supervised learning of transformer networks as a promise approach for making a step forward in self-adapting of neural network models. Specifically, we study the the cross-modal applicability of self-supervised learning using Transformer network pretrained on color images for data distilling in thermal images datasets. The results of evaluation demonstrate that Transformer network based on self-attention mechanism identifies the same features both in color and in thermal image datasets.
Image Style Transfer With a Group of Similar Styles

Image style transfer is a widely used applied task of automatic redrawing of the source image (content) in the style of another image (setting the target style). Traditional style transfer methods provide only a single stylization result. If the user does not like it, for example, due to artifacts that appear during styling, then he has to choose another style. The paper proposes a modification of the styling algorithm, which gives a variety of styling results with one style, and also improves the average quality of styling by using not only style information from the original style image, but also information from images that have a similar style.
Investigating the Perception of Stylized Text in the Process of Professional Translation Using Eye-tracking

The purpose of this paper is to investigate the perception of a stylized text by professional interpreters when performing sight translation. Based on the results of the preliminary experiment, the methodology of this experimental study was refined by using new factors which were considered when developing the stimulus material and setting a new task for the participants of the experiment. The parametric data of the stimulus material viewing pattern obtained from the eye- tracker were analyzed using statistical analysis. The results of the experiment confirmed that the factors of contrast, stylization and typography had a statistical significance on the interpreters' perception of the text. The most and least effective combinations of the mentioned factors were also identified regarding the interpreters' text perception. The results of this experimental study can be used to design an application to improve the quality of interpreter training.
Using qr Codes to Transfer Geometric Models

Quick Response code encodes text information that can be specified in different formats, such as a link, contact information, payment details, etc. Such codes are widely used in the modern world, and the means of recognizing them is often a common smartphone. Such codes are widely used in the modern world, and the means of recognizing them is most often an ordinary smartphone. 3D models in turn require specialized software to read and interact with them. As a result of this work, the geometric model is transferred to the information system by means of qr-code, which is a link to this resource. The text description of the geometrical model in the obj file format is converted to the x-www-form-urlencoded format and is passed by GET-parameter. When loading the page by QR code in client scenarios data processing and subsequent visualization with the help of WebGL technology with the use of three.js library takes place. Applying this approach allows to transfer a geometric model for its visualization and interaction with it without specialized software, requiring only a browser pre-installed in every OS.
Strokes classification of handwritten text based on the Frechet distance

The paper aims to develop an interpretable similarity metric of the calligraphic handwritten elements, strokes. The need for such a metric often arises in text navigation tasks. To extract strokes, the text is converted into a binary image and a skeletal representation is constructed for all connected components. It provides accurate information about how the pen moved while writing the text. Next, the skeletal representation is sliced into a geometric graph. The strokes are formed as subgraphs. As a result, the new method is proposed which is based on the use of Frechet distance between plane curves. The presented metric has been applied to the tasks of stroke classification and letter recognition based on stroke bigrams and trigrams.
Detection of Hidden Subsurface Objects Based on the Processing of Multispectral Data Obtained from an Unmanned Aerial Vehicle

To analyze the visibility of remote monitoring objects based on the data of different-time images in the visible and infrared wave ranges, it is important to know the patterns of temperature changes both of the Earth's surface itself and of objects. The study of the dynamics of temperature changes using a mathematical model of heat transfer allows us to obtain additional unmasking feature. As an additional unmasking feature, numerical estimates of the thermophysical parameters of hidden subsurface objects can be calculated based on the application of a genetic optimization algorithm. Grouping thermophysical parameters into separate classes allows you to highlight the boundaries of objects and unmask the area of their occurrence under the ground. To solve the problem of correct processing of a large amount of data, reducing the processing time of aerial photographs, it is advisable to use the method of machine learning based on neural networks. The article presents the process of segmentation of objects using deep learning, a mathematical model of thermophysical processes is formed to obtain numerical estimates of the thermophysical parameters of hidden subsurface objects in the course of solving the coefficient inverse problem of thermal conductivity based on a genetic algorithm.
Gender-specific Effect on the Perception of Information in a Graphical Interface Under Physical Stress

This paper presents a study of the impact of gender differences on information perception features in graphical interface, in the field of peripheral vision, under physical activity. The study of information perception under stress can extend the field of information display in the graphical interface and have practical applications in various spheres. The study uses a technique using eye- tracking technology to record eye movements. The experiment examines the influence of different factors such as color, icon size, gender and physical activity. The results of the experiment were processed using mathematical statistical methods. The degree of physical activity was determined using a device that recorded the heart rate. The technique was tested on a limited group of subjects, and the data obtained revealed a statistically significant dependence of viewing parameters on factors of color, size, physical activity and gender.
Digital Earth and Metaverse Concepts: Correlation and Development Trends

The article is devoted to a comparative analysis of the changes that have occurred over the past two years in the content of two ideological concepts of web 3.0 - the metaverse and the Digital Earth, both of which were introduced to the world in 1992, but have been developed separately until now. The Digital Earth project was developed with the support of different states, while the meta-universe was conceptualized and promoted only by a small community of private actors until recently. The article analyzes the meaningful change of the term «metaverse» from the middle of 2022 to 2023, draws conclusions about the prospects of further development of this concept taking into account its synergetic development together with the concept of Digital Earth, as well as the correlation of the terms meta-universe and Virtual Earth at the current moment of time.
Detection, localization and Transformation of twoDimensional Graphic Code

The ROI of the article relates to computer vision. Mathematical foundations and algorithms for preparing two-dimensional graphic codes for data decoding are presented. The preparation stages include the detection, localization and transformation of the code to a decoding-friendly form. The brightness oscillation function proposed in the paper serves to reliably detect codes in the image, the Hough transform is the basis for solving the localization problem, and the formulas for the inverse transformation of a square into an arbitrary quadrilateral allow you to transform localized quadrangular areas into squares with a horizontally oriented base. The work of algorithms on a set of test and real images is studied, the practically permissible level of distortion during the transformation of detected and localized two-dimensional codes into the original square form is shown.
Blind Image Deblurring Using Multi-Task Convolutional Neural Network and Spatially Variant Recurrent Neural Network

We present a novel method of blind image deblurring, capable of handling spatially variant blur caused by various reasons such as camera shake and object motions. Due to the spatially variant nature of the complex blur, our deblurring method includes both spatially variant and spatially invariant components. Namely, we utilize hybrid neural network consisting of a convolutional neural network and a spatially variant recurrent neural network trained in end-to-end manner. Recurrent neural network is used as a deconvolution operator performed on feature maps extracted from the input image by the convolutional neural network, while per-pixel weights of the recurrent neural network are also produced by the same convolutional neural network, making it solve both tasks at the same time. Such hybrid and multi-tasking design allows us to achieve both low computational complexity and high deblurring quality. Quantitative and qualitative evaluations on the widely used GoPro dataset demonstrate that the proposed method provides a good trade-off in terms of complexity and quality against state-of-the-art algorithms.
Mixed Reality Surgical Navigation Method

The aim of the study is to present the features of our own new mixed reality surgical navigation method. The paper describes the workflow from the stage of preoperative patient scanning to postoperative patient scanning. Potential sources of positioning errors are identified, design and software approaches to reduce their impact are described: the implementation of the unity of the location of markers, the execution of markers. The applied approach for estimating the cumulative positioning error is described. As a result of the implementation of these approaches in practice, the value of the total navigation error was less than 2 mm, and more than 80 successful operations were performed in this way. This value of navigation error is sufficient for most surgical operations, but is unacceptable in neurosurgery. The study can be useful for improving similar systems, as well as for designing mixed reality systems in other scientific fields.
Graphical Neural Networks and Image Verification Problems

The paper offers an overview of modern text-to-image graphical neural networks and text-to-image image retrieval. The paper discusses a number of problems arising in the use of text-to-image networks and possible methods for their solution. One of the actual tasks related to the study of graphical neural network technologies becomes the study of neural network images and identification of images obtained with the help of neural networks among other graphical content. We propose approaches to solving problems aimed at solving the problem of verification of media materials and developing algorithms to detect artificial (neural network) origin of photo and video materials. The rapid development of neural network technologies in this area can have a significant impact on society, the market of professions and the media, which makes the task of identifying neural network images among other graphic content particularly relevant.
Counterfeit Currency Detection and Recognition for Blind People

Counterfeit currency detection and recognition technology is a crucial tool to empower blind and visually impaired individuals in managing their finances independently and securely. In an increasingly digital and cashless world, ensuring that these individuals can confidently identify genuine banknotes and protect themselves from counterfeit currency scams is of paramount importance. This abstract explores the challenges faced by blind users, the technology’s potential, and the ongoing efforts to enhance accessibility and usability. Blind and visually impaired individuals encounter significant hurdles when dealing with currency, from counting money to ensuring its authenticity.
AZERBAIJAN LIBRARIES AT THE LEVEL OF MODERN STANDARDS (ON THE BASE OF REPUBLIC YOUTH LIBRARY NAMED AFTER J. JABBARLI)

The main purpose of the article is to research Azerbaijan libraries at the level of modern standards on the base of Republic Youth Library named after J. Jabbarli. Today, the vast majority of people in the world widely use computers for various purposes. Especially for the younger generation, computer use has become a normal way of life for their activities. The integration of computers into the field of education and culture also reflects these trends. The successful application of computers in the field of education and culture depends not only on their availability, but also on how familiar the users are with them. The article covers technical and software and electronic service problems applied to modern library-information service. The application importance: the material can be used in the lectures and seminars in Higher Educaion Institutions. One who is interested in librarianship also can benefit form this research. Keywords: modern libraries, library standard, library service, information space.
Reclining Public Chair Behavior Detection Based on Improved YOLOv5

This study proposes an object detection algorithm based on the improved YOLOv5 network for the uncivilized behavior of reclining public chair, which often occurs in cities. The current object detection field is studied by a single object. For the behavior of a lying public chair, the object to be measured is composed of two parts: the chair and the human posture jointly. Furthermore, the features of the object will show a large variability under different shooting angles, so the model’s ability to extract features of the object is extremely important. This paper incorporates the Ghost module based on the YOLOv5 network to enable the model to learn more object features. The Ghost makes the neural network lighter by using linear convolution instead of nonlinear convolution, and its generated redundant features can help the model learn more object features and improve the model performance. In addition, this paper uses a new loss function EIoU to replace the original loss function CIoU. By comparison, EIoU solves the problem that CIoU fails in penalty terms under specific conditions. EIoU enables the model to converge faster and better. After experimental validation on the test set, it is shown that the improved YOLO network improves F1 by 3.5% and mAP by 4.2% compared to the original algorithm.
Real-Time Dynamic Gesture Recognition Algorithm Based on Adaptive Information Fusion and Multi-Scale Optimization Transformer

Gesture recognition is a popular technology in the field of computer vision and an important technical mean of achieving human-computer interaction. To address problems such as the limited long-range feature extraction capability of existing dynamic gesture recognition networks based on convolutional operators, we propose a dynamic gesture recognition algorithm based on spatial pyramid pooling Transformer and optical flow information fusion. We take advantage of Transformer’s large receptive field to reduce model computation while improving the model’s ability to extract features at different scales by embedding spatial pyramid pooling. We use the optical flow algorithm with the global motion aggregation module to obtain an optical flow map of hand motion, and to extract the key frames based on the similarity minimization principle. We also design an adaptive feature fusion method to fuse the spatial and temporal features of the dual channels. Finally, we demonstrate the effectiveness of model components on model recognition enhancement through ablation experiments. We conduct training and validation on the SCUT-DHGA dynamic gesture dataset and on a dataset we collected, and we perform real-time dynamic gesture recognition tests using the trained model. The results show that our algorithm achieves high accuracy even while keeping the parameters balanced. It also achieves fast and accurate recognition of dynamic gestures in real-time tests.
Optimization of Briquette Classification Using Deep Learning

Indonesia needs energy to meet its needs. Energy sourced from waste is called biomass briquettes. The manufacture of briquettes is still done in a traditional way, so there are product quality problems. A computational approach has been taken to base the quality of the product on certain characteristics so that the types of briquettes that are made can be classified or sorted. The research objective of this work is to determine the quality of briquettes. The approach uses deep learning methods and convolutional neural network (CNN) architecture. Classification is based on good and bad briquette products, and testing is based on the level of performance accuracy. The dataset formed consists of 5,280 images. As training data, 85% of data is used and 15% is used as test data using 300 epoch parameters, 32 batch sizes, and learning speed up to 0.001. The results of testing and evaluating the performance of briquette products with the general CNN architecture have a level of accuracy that is not optimal. The optimization results of testing the MobileNetV2 architecture with a ratio of 70:30 obtains predictions with 0.99 as the highest accuracy value and 0.73 as the lowest. The average predicted value of the model is 0.95. The computational approach is able to provide traditional communities with solutions for the process of making briquettes.