Reviewerly/exHarmony huggingface dataset:
Comparative Analysis: Handwritten Character Recognition Via K-Nearest Neighbour And Naive Bayes Algorithms

The Study of Handwritten Character Prediction: Comparative Analysis Between K-Nearest Neighbour and Naive Bayes Algorithms This research aims to predict handwritten characters by employing the K-Nearest Neighbour (KNN) algorithm and comparing its feature extraction precision with the Naive Bayes (NB) algorithm. Both algorithms were evaluated using a sample size of n = 25 and iterated 15 times to enhance accuracy and reduce errors, with a statistical power of 80% (G power) and an alpha value of 0.05.The experimental results demonstrated a significant improvement in the accuracy of handwritten character recognition, achieving a significance value of 0.165 through the comparison of NB and KNN algorithms. Comparative analysis revealed that the NB algorithm (97.45) outperformed the KNN algorithm (97.3). These findings highlight the superiority of the NB algorithm in predicting handwritten characters over the KNN algorithm.
Digital vulnerabilities and online harassment of academics, consequences, and coping strategies. An exploratory analysis

Academic research is currently undergoing a wave of contestation, ranging from violent attacks and life-threatening situations to public undermining of their research and online threats and harassment. A variety of actors engage in hostile behaviors, including ordinary people as well as state institutions, and address scholars working in diverse disciplines and on diverse topics, such as climate change, vaccination, gender studies, colonialism and Islam studies. Recent research shows that female scholars, sexual and racialized minorities, and precarious researchers are the most likely target of online attacks. This contribution presents the results of a research project on academics facing online harassment in times of populism, drawing on data from an original survey to explore the ambivalences of academics’ public engagement, the public role of universities, and, more broadly, the impact of current attacks on academic knowledge.
Improving the accuracy of detecting and ranking favorable porphyry copper prospects in the east of Sarcheshmeh copper mine region using a two-step sequential Fuzzy - Fuzzy TOPSIS integration approach

The detection and prioritization of optimal favorable areas for the ground follow-up stage are among the most challenging issues in the early stages of any mineral exploration program. A common approach to identify the favorable mineralized zones is to create and integrate independent evidential predictor layers using knowledge or data driven approaches. The method proposed in current study is not only capable of detecting favorable zones, but also provides reliable ranking of the best favorable areas to focus in the next exploration stage. For this purpose, a two-step sequential Fuzzy-Fuzzy TOPSIS approach, which deploys the merits of both Multi-Criteria Decision Making (MCDM) and Fuzzy logic inference methodologies simultaneously, is proposed. In the first step, the favorable porphyry copper mineralizations in the east of the well-known Sarcheshmeh porphyry copper mine, are detected through combining evidential layers including geological, remote sensing data, geophysical and geochemical data using fuzzy logic integration approach. As a result of the first step, a number of twenty prospects with the highest porphyry copper favorability membership were selected and inputted into the TOPSIS and fuzzy-TOPSIS algorithms. Subsequently, the chosen prospects were prioritized and ranked according to their scores acquired by each technique of the aforementioned approaches separately. The performance of each approach was evaluated thorough comparison with the known ground porphyry copper mineralizations. The results indicated the capability of the proposed approach not only in detecting favorable porphyry copper mineralization prospects consistent with the previously detected porphyry Cu mineralization but also rank them based on their priorities.
PISA 2022 Financial Literacy Framework

The PISA 2022 study offers an optional assessment of financial literacy for the fourth time. The revised framework proposed in this chapter takes into account changes in the socio-demographic and financial landscape that are relevant for students&apos; financial literacy and decision making. It includes slight revisions to the PISA definition of financial literacy and to the description of the domain around the content, processes and contexts that are relevant for the assessment of 15-year-old students. As before, the framework discusses the relationship between financial literacy and non-cognitive skills, and between financial literacy and other domains of knowledge and skills.
Hand Cricket Game Application Using Computer Vision

Hand cricket is an attempt to experience the childhood entrainment of playing cricket using our hands. This paper presents an application built to play Hand cricket using a computer vision-based real-time 3D hand gesture recognition system. To play the game the user will give inputs using hand gestures. These hand gestures will be identified using a real-time computer vision system that runs based on a CNN model. Since in this game both players must play simultaneously the computer also comes up with a number from 1 to 6 when a hand gesture is being read and recognized. The core engine of building this app is an image classifier that classifies a picture of a hand into 1,2,3,4,5,6 and none. This paper shows an interactive simple game application developed, that tries to make the game feel more natural and as if playing with a real person.
Selection of node with editing rights and privacy protection mechanisms based on dual-blockchain

The inability to edit and modify the data on the blockchain limits its practical applications. Current editable blockchain schemes with little regard for security in the editing process, and there is a lack of effective monitoring and auditing measures. These are also susceptible to the formation of a centralized trend of editing rights and bring privacy protection problems. Aiming at editable application requirements, to solve the problems of "who will edit" and "the security of editable", selection of node with editing rights and privacy protection mechanisms based on dual-blockchain are proposed, including data chain and audit chain. In the data chain, to avoid some nodes being in the chain for a long time, being used by attackers and editing rights tending to be centralized, heterogeneity among nodes is used as one of the decisions. An Execution Node Authority Confirmation Mechanism (ENACM) based on confidence, reliability, and heterogeneity is proposed. The performance of nodes at different stages can be effectively used. Node with editing rights is ensured to be unpredictable and protected from targeted attacks. In the audit chain, the Parameter Audit Mechanism (PAM) is proposed. To anonymously update and verify the parameters used in the ENACM, Concise Linkable Spontaneous Anonymous Group signature, Pedersen commitment, and Schnorr signature are employed. Privacy protection is ensured for associated information of the node. A prototype system of the proposed scheme based on Hyperledger Fabric has been implemented. System performance, security, parameter settings, and comparison with related schemes are analyzed. Experimental and test results show that the proposed scheme is feasible, effective, and reliable.
Intelligent energy systems ontology to support markets and power systems co-simulation interoperability

The significant changes the electricity sector has been suffering in the latest decades increased the complexity and unpredictability of power and energy systems (PES). To deal with such a volatile environment, different software tools are available to simulate, study, test, and support the decisions of the various entities involved in the sector. However, being developed for specific subdomains of PES, these tools lack interoperability with each other, hindering the possibility to achieve more complex and complete simulations, management, operation and decision support scenarios. This paper presents the Intelligent Energy Systems Ontology (IESO), which provides semantic interoperability within a society of multi-agent systems (MAS) in the frame of PES. It leverages the knowledge from existing and publicly available semantic models developed for specific domains to accomplish a shared vocabulary among the agents of the MAS society, overcoming the existing heterogeneity among the reused ontologies. Moreover, IESO provides agents with semantic reasoning, constraints validation, and data uniformization. The use of IESO is demonstrated through a case study that simulates the management of a distribution grid, considering the validation of the network’s technical constraints. The results demonstrate the applicability of IESO for semantic interoperability, reasoning through constraints validation, and automatic units’ conversion. IESO is publicly available and accomplishes the pre-established requirements for ontology sharing.
Uma Nova Abordagem para Localização de Robô Móvel em Ambiente Virtual utilizando Mapas Topológicos a partir de Matriz de Co-ocorrência Estrutural em Imagens Omnidirecionais

In Robotics, location is an essential task, allowing the robot to navigate and thus carry out its activities. In this article, we propose a new approach to localization and navigation for mobile robots in a virtual environment using topological maps and classification with an option to reject from the Structural Co-occurrence Matrix (SCM) in omnidirectional images. To prove the high performance of the considered technique, an analysis is performed between several feature extractors and classifiers, established in the literature. Parameters such as processing time and accuracy are calculated to demonstrate the reliability and effectiveness of the approach, since these properties are paramount in embedded systems. Regarding the proposed approach, the SCM obtained an average accuracy of 99.27% and the shortest extraction time, 0.0786s, among all the feature extractors, proving to be an expressive method in the activities of location and navigation. SCM also achieved 100% accuracy in navigation tests.
Geoinformation mapping of the ecological and magnetic state of soils in Krasnokamsk

The article is devoted to geoinformation mapping of the ecological and magnetic state of soils in the city of Krasnokamsk. The study included point determinations of the volumetric magnetic susceptibility of soils and the concentration of heavy metals, followed by mathematical-cartographic modeling of the results obtained. Mapping the magnetic susceptibility of soils makes it possible to form areas of the ecological and magnetic state of soils, along which samples are taken for the determination of heavy metals. Thus, the sample size of soil samples for heavy metal studies is reduced without a significant decrease in the representativeness of the final results, which determines the relevance of the studies performed. The purpose of the research is geoinformation mapping of the ecological and magnetic state of soils in the central part of the city of Krasnokamsk. The boundaries of research from the south are limited by the industrial zone of the city, which is mainly represented by pulp and paper industry enterprises. In the eastern part of the survey area, Zakamskaya CHPP-5 and a machine-building plant are located. The soil cover of Krasnokamsk is represented by urbanozems and technogenic surface formations. The total number of magnetic susceptibility measurement points was 77, and heavy metals were determined in 10 samples. During processing spatial data, the methods of mathematical-cartographic modeling, spatial overlay and binary classification were used. The interpolation was performed by the geostatistical method ordinary kriging using the Spatial Analyst tool of the ArcGIS 10.8 program. The creation of the Web GIS “Geoinformation system of magnetic susceptibility and heavy metals in the soils of Krasnokamsk” was performed on the basis of QGIS open geotechnologies using the qgis2web tool, the VS Code code editor. The Web GIS is hosted on the free GitHub platform. Based on the results of mapping, soil contamination with nickel and copper was established. The total area of pollution was 0.55 km2.
Data Augmentation Based on Wavelet Filtration During Neural Network Training

The research is devoted to studying the influence of data augmentation by means of wavelet filtration on recognition accuracy of noised images by the neural network detector. It is proposed adding noise to samples of the training dataset and then processing them by Wiener filter in the domain of discrete wavelet transform (DWT). Generating of input training images uses the model applying white additive Gaussian noise to distort them. In addition to the Wiener filtering, we considered some thresholding methods in the domain of DWT for images denoising. Based on the obtained experimental data, we compare the results of the detector recognition for the different values of noise standard deviation. Also, we considered the case, when the neural network was trained on noised-augmented images without subsequent filtering. It is shown that the proposed approach allows higher accuracy of recognition to be achieved, than the augmentation, which doesn’t use noise suppression.
Influence of the Accuracy of Estimation of Coordinates of Reference Areas on the Accuracy of Image Registration

The problem of estimating the accuracy of geometric registration of images when using registration methods based on the choice of reference areas is considered. As a mathematical model of geometric transformations of images, a scale-shift transformation is used, which is typical for stationary video surveillance systems equipped with a rotary device and a camera with a variable focal length. The choice of reference areas is carried out by a multi-reference algorithm with a random search for reference points. Expressions are given for the correlation matrix of errors in determining the parameters of geometric transformations and the correlation matrices of alignment errors at different points of the image. The dependence of the variance of the alignment error on the coordinates of the reference areas is obtained. Experimental studies were carried out on full-scale images formed in the vehicle traffic control system. The results obtained can be used in the development of algorithms for image stabilization, selection of moving objects, detection of scene changes.
Study of the Possibility of Parallelization of the Lempel-Ziv- Welch (LZW) Algorithm in Import-Substituting Telecommunication Multimedia Technologies

The article discusses the results of the author's research in the field of serial/parallel compression of one-dimensional, adaptive audio signals. An overview of many well-known compression algorithms is provided, including the basic LZW algorithm and its components, and a detailed analysis of two parallelization schemes is presented: a serial time scheme divided into several parts for compression, and parallelization using a prefix tree using expansion inner loops. The possibility of parallelization of the LZW algorithm for compression and evaluation of its performance is investigated. An estimation of acceleration according to Amdahl's law and an estimation of scaling due to an increase in the number of threads and processes are described. The results of experiments comparing serial and parallel LZW algorithms in terms of compression ratio, quality, and computational complexity are presented.
Modeling Forms of Cognitive Activity of an Individual on the Example of the System «Learning Motivation» student

The purpose of the work is to study the types of cognitive activity of an individual that are used in decision making. In our work, the student's learning motivation is studied. To achieve the goal, a set of tasks has been formulated. The first task is to identify the factors that influence the student's learning motivation. The second task is to create cognitive models in the form of weighted directed graphs. Further, the model is used for simplicial analysis and computational experiment. The study involved teachers and students. Teachers recommend being guided by three factors "pragmatic aspects", "personality of the teacher" and the factor "personal qualities" of the student. These factors were revealed during the simplicial analysis. There are two factors in the student recommendation. The first factor coincides with the opinion of teachers. This is the factor of "personal qualities" of the student. The second factor is the “psychological climate in the teaching staff” factor. Conclusions can be used by specialists to support management decision-making.
Using Interactive Visualization in the Problem of Feature Extraction from Semi-structured Text Data

The article deals with the visualization of semi-structured text data (SSTD) in order to solve the problems of exploratory analysis and build a model for processing text data for their further use in data analysis models. The problems faced by researchers when adding SSTS to the data analysis model are considered. Existing approaches to visualization of text data for solving various problems of natural language processing are considered. A model of intelligent processing of SSTD and approaches to data transformation within the data processing. A visual model used to visualize the process of transformation of SSTD is based on the Sankey charts. The proposed visual model allows to reduce the expert's time for data processing by increasing the visibility of the process of extracting features from SSTD using interactive visual tools. The developed approach was tested on data from the information system of the employment service.
Method for automatic initialization of trainable active contours for instance segmentation in histological images

The method of trainable active contour is one of the semi-automatic segmentation methods that can be applied to segment glands in histological images. In this paper, we propose a method for automatic initialization of trainable active contour model, which makes the segmentation method fully automatic. Using a U-Net like architecture, a preprocessed image segmentation masks is predicted for the input image, from which initial approximations of contours are calculated for each gland. The proposed method correctly marks 96.2 % part for the glands on the test set of the PATH-DT-MSU S1-v2 dataset. As a result, we get initial approximations located inside each gland in the image.
Self-supervised Algorithms for Anomaly Detection on X-Rays

In this paper, we consider the problem of prohibited objects detection on X-Ray images obtained by personal inspection scanners. Such scanners are often used on objects that require increased security control. The available data has a number of problems, which are described and addressed in the text. In this paper we consider only self-supervised anomaly detection algorithms. We are using several architectures of autoencoders and comparing them with the state-of-the-art algorithm Patch SVDD, which could be designed and trained on our data from scratch. Unlike supervised learning algorithms, which are often used for such problems, these models do not require a large amount of labeled data for training.
Influence of Non-Equilateral Apertures on the Characteristics of Digital Filters "Truncated Pyramid" Laplacian and "Double Pyramid" Laplacian

In the process of processing digital images, an important role is played by increasing the efficiency of the applied methods and processing algorithms. One way to improve efficiency is to convert spatial filters into recursively separable digital filters. The use of such filters makes it possible to reduce the number of computational operations. This article presents digital recursively-separable Laplacian filters "truncated pyramid" and "double pyramid" converted into non-equilateral form. This transformation allows to further reduce the number of computational operations and speed up the processing. Also, the article discusses the principle of operation of these filters, their structure and operation algorithm. Based on the results of image processing by a digital recursively separable filter Laplacian "double pyramid", the quality of processing by a filter with a base mask of 7x7 elements and non-equilateral apertures is evaluated and compared.
Super-resolution for Whole Slide Histological Images

Histological images serve as crucial tools in the diagnosis and treatment of diverse afflictions. However, the acquisition of images exhibiting exceptional resolution whole slide images, WSIs, capturing intricate textures and vital nuances, can present a formidable challenge, primarily due to the requirement of expensive and intricate apparatus, proficient personnel, and considerable time commitments. To tackle this predicament, it is important that we conceive an effective and precise framework to increase the resolution of whole slide histological images. There are several algorithms used for super-resolution, including interpolation-based, deep learning based and bayes based algorithms. After scrutinizing and dissecting the available super-resolution models and algorithms, we arrived at the conclusion that the most suitable approach for histological WSIs would be to fine-tune the already trained Real-ESRGAN model to reconstruct histological images and apply it in a patch-based way. For histological WSIs, it is typical to have a lagre number of empty areas that do not contain tissue. To circumvent the impact of these empty areas on the model’s efficiency, we filter the dataset using Shannon’s information entropy. Furthermore, we have modified the structure of the loss function to optimize the reconstruction of low-level details of histological images. In this study, we fine-tune the pre-trained Real-ESRGAN model using the histological image dataset PATH-DT-MSU. It enabled us to outperform all preexisting models in terms of reconstructing low-level details in WSI histological images. Moreover, without retraining the model, we have tested it on additional histological image datasets, thereby proving its high generalization ability.
Digital Earth and Big Data: Development VR

The article considers important aspects for the Digital Earth project: Virtual reality application technology as an example of technology using big data. The author pays attention to the history of the emergence of virtual reality, as well as the areas of its application. The article emphasizes the importance and prospects of the technology. Several examples of the use of virtual reality, which are of great importance, are considered. The data and forecasts on investment investments in the development of virtual reality are presented. The statistics not only demonstrate the main areas of application, but also the difficulties faced by both companies and users. Particular attention is paid to one of the important issues that are highlighted by the Digital Earth community within the framework of promising areas of development – the security of society. The use of virtual reality opens up new opportunities for both the armed forces and the judiciary.