Reviewerly/exHarmony huggingface dataset:
Contactless Gesture Recognition Using Air Canvas

The proposed work is a real-time implementation in Python for air-writing using algorithms like media pipe hands and easy OCR. Air writing has emerged as one of the most difficult study fields in image processing and pattern recognition in recent years. Air writing is the act of using one’s hands or fingers to scribble letters or words in a clear area. The proposed work aims at the implementation of a contactless gesture recognition software which uses an air canvas which sums up as providing an image input to the machine via a camera device, while the machine processes it and gives a delineated digital output of the work in real time, features such as optical character recognition, screenshot, and duplication will be an added attempt at closing the gap of limitations.
THE ROLE OF DIGITALIZATION IN THE SOCIAL RESPONSIBILITY OF UNIVERSITIES

INTRODUCTION. In the modern world, digital transformation has a significant impact on all aspects of life, including education. Universities, as key educational institutions, cannot avoid the influence of digital technologies. Therefore, there is a need to study the role of digitalization in the social responsibility of universities.&#x0D; RESEARCH HYPOTHESIS: Digital technologies can contribute to enhancing the social responsibility of universities by improving access to education, promoting environmental sustainability, and supporting social-economic development.&#x0D; THE AIM of this article is to explore the role of digitalization in the social responsibility of universities, identify the opportunities and challenges it brings, and develop recommendations for university institutions on effective use of digital technologies to achieve social goals.&#x0D; METHODS. This article utilizes literature analysis, statistical data, and empirical research to assess the impact of digitalization on the social responsibility of universities.&#x0D; FINDINGS. The analysis revealed that digital technologies enable universities to provide wide access to education, reduce environmental impact, and foster social-economic development. However, their effective utilization requires addressing technological, financial, and cultural barriers.&#x0D; CONCLUSION. Digital technologies have great potential to enhance the social responsibility of universities, but their implementation should be carried out considering the specific needs and context of each university institution.&#x0D; KEYWORDS: digitalization; social responsibility; universities; education; environmental sustainability; social-economic development.
On the Involvement of Bots in Promote-Hit-and-Run Scams – The Case of Rug Pulls

Many social media frauds related to finance can be summarized under what we consider promote-hit-and-run scams. Examples include rug pull scams also known as exit scams, pump-and-dump schemes or bogus crypto currency trading platforms. For scams of this kind to work they must be publicly advertised as lucrative investment opportunities disguising the fraudulent motivation behind them. Social media are key in this promotion. Here, fraudsters find platforms to persuade others investing into what later turns out to be a scam. Via social network analysis of Twitter screen names and their first-level contacts, our work investigates rug pulls. It is aimed at profiling social media communication around them with a special focus on the deployment of bots. Repeatedly bots have been identified in social media campaigns (Orabi et al., 2020). Bot deployment in the context of rug pulls, however, has not been studied yet. Our analysis of social data of 27 rug pulls reveals massive bot activity coordinated within and between rug pulls mainly targeting established finance news outlets, e.g., Bloomberg, Reuters. Among the conclusions of our work is that bot deployment may prove an early indicator for rug pulls and other promote-hit-and-run scams.
Use of machine learning techniques in non-probabilistic samples

Non-probabilistic surveys are increasingly used because they are easy and cheap to carry out. Even official statistical agencies are starting to use this type of surveys in their research, due to the difficulty and the amount of resources needed to carry out probabilistic surveys, which are currently the best option due to their reliability. When non-probabilistic surveys are used, the classical estimation methods cannot be used since the initial conditions for carrying them out are not met, so over the years new estimation techniques have been emerging in this type of sampling. Some of the most relevant estimation techniques currently being used are those related to machine learning techniques.In this work we focus on the estimation technique for non-probabilistic samples statistical matching, which can be enhanced and improved if we complement it with a machine learning technique known as XGBoost. We are going to study a variable of interest extracted from a real non-probabilistic survey carried out during the COVID-19 pandemic, and check if by applying such estimations we obtain better results than without applying this type of techniques.
Face Gender Classification by Using Improve Binary Particle Swarm Optimization and K-Nearest Neighbors

This paper studies the application of the Binary particle swarm optimization (BPSO) algorithm to the optimal search for facial features and gender classification by K-Nearest Neighbors (K-NN) model. The results show that the accuracy and processing time of the model is much better than that of VGG16, VGG19, Resnet50, Senet50, Face Net, Open Face and FbDeep Face models. a large-scale GenderFace80K dataset with 80,000 facial images with gender annotation used in the research model.
A Study on Pre-processing and Fault Analysis using PMU Big Data of Substation

Recently, technology development for PMU (Phasor Measurement Unit) data analysis system and Korean-style WAMAC (Wide Area Monitoring And Control) construction is being pursued for stable system operation and cost reduction. With high-speed sampling up to 256 times per cycle and the ability to collect time synchronized big data based on GPS signals, PMUs offer advantages over today SCADA/EMS (Supervisory Control and Data Acquisition/Energy Management System) and PQMS (Power Quality Monitoring System), it is possible to build an accurate and real-time condition monitoring system. By processing a vast amount of PMU big data, useful and valuable information can be delivered.BRIn this paper, processing and fault analysis are performed for the utilization of big data collected by 35 PMUs installed in 154kV ○○ S/S (Substation). First, we introduce the PMU installation points of the S/S and collect PMU big data to understand the data structure. After performing the pre-processing in the form of a snapshot, we analyse the voltage, and power comparisons. In addition, the analysis is performed through the IVFR (instantaneous voltage fluctuation rate), which can efficiently monitor system changes acquired from multiple measurement points. The pre-processing and fault analysis are implemented using the language of Python 3.7 version. Finally, an aim of this study is to analyze the impact of power grid faults on ○○ S/S.
Mykola Rudenko’s poetry and prose as a platform for popularizing the author’s alternative hypothesis of the Universe

The results of literary research of members of Sumy Branch of Scientific Society named after Serhiy Podolyns’kyy are presented, which reveal the uniqueness of literary output of outstanding Ukrainian writer Mykola Rudenko. His literary legacy is unique because the writer used it as a popularization platform of his scientific hypotheses, and ideas; it saturates textual essence of his works with scientific terminology, formulas, mathematical, physical calculations; forms reader’s scientific worldview and allows wide readership to participate in writer’s scientific discoveries. The purpose of study is to show how scientific hypothesis of Mykola Rudenko’s Monadic world structure was formed and how it was reflected in his works. Research used general scientific (analysis, synthesis, comparison) and literary methods (systemic approach, hermeneutic, etc.). The works of I. Voloshyn, H. Vivat, P. Grigorenko, B. Pastukh, T. Trofymenko, O. Nezhivy, V. Shevchuk allow to comprehensively comprehend the legacy of Mykola Rudenko. A detailed analysis of works of M. Rudenko allows us to conclude: 1) writer used his literary works as a platform to popularize his scientific research; 2) M. Rudenko managed to create whole layer of "scientific" poetry and prose; 3) one should not consider his poetry and prose as an appendix to "Energy of Progress" – they are autonomous works, especially lyrics.
An Online Jamming Effect Evaluation Method for Asymmetric Radar Information in Complex Electromagnetic Environment

In the dynamic game between jammers and radars, it is easily affected by the complex electromagnetic environment, resulting in the generation of asymmetric radar information. In this paper, a jamming effect evaluation method for asymmetric radar information is proposed from the perspective of jammer. An online jamming effect evaluation model is constructed. An improved class-attribute contingency coefficient (ICACC) method is proposed. The feature attribute weight and inconsistency rate are used to constrain the division process of breakpoints, so that the discretization of radar data is more reasonable. At the same time, a basic belief assignment (BBA) correction method that combines probability similarity for asymmetric information is proposed. The output results of support vector machine (SVM) are corrected to reduce the influence of incomplete radar sample data on the discriminant and evaluation results. Experiments show that the proposed method can achieve efficient jamming effect evaluation under asymmetric radar information conditions.
TOPSIS-based Comprehensive Assessment Method of Short-term PV Power Prediction Errors

For the safe and economic operation of new power system comprising large-scale PV power generation, a highly accurate short-term photovoltaic (PV) power forecast is of significance. Currently, traditional statistical indicators are utilized to evaluate the prediction results, and it lacks a more reasonable comprehensive evaluation system to assess the merits and demerits of the selected prediction model and system. Consequently, this paper firstly proposes a series of comprehensive evaluation index system that includes common error evaluation indexes, forecast assessment indexes, and volatility indexes, considering the forecast assessment indexes of prediction results and the impact of cloud motions on PV power in PV sites. Then, a thorough evaluation approach based on technology for order preference by similarity to ideal solution (TOPSIS) for PV power prediction (PVPP) errors is developed. Finally, based on actual data from a PV power plant, various prediction methods and evaluation indexes are assessed comprehensively, which confirm the feasibility of the proposed evaluation approach of PVPP errors.
Multivariate functional principal component analysis for endogenously stratified data

We address the problem of performing dimension reduction on multivariate functional data observed on different domains in an endogenously stratified sampling context. The aim is to propose a new multivariate functional principal component analysis \(MFPCA) approach for data sampled by a stratification of a population according to a binary variable of interest. This estimation strategy is derived from a direct relationship between univariate and multivariate FPCA for finite Karhunen-Loève decompositions. The proposed methodology yields encouraging results and can be applied to data with measurement errors. Computational results on simulated data highlight the good performance of the proposed methodology compared to the classical MFPCA, which ignores the type of data sampling. A real-life application considering breast cancer cells data is also presented.
School Financial Management in Meeting Infrastructure Standards (Case Study at SMP Negeri 17 in Serang City)

The purpose of this study was to analyze and understand the fulfillment of school financial management facilities and infrastructure standards at SMP Negeri 17 Kota Serang. This research uses a qualitative approach with case studies. The informants in this study consisted of school principals, deputy director of facilities and infrastructure (wakasek), treasurers of Education Unit Operational Assistance (BOSP), and school committees. Data collection techniques were carried out through interviews, observation and documentation. Data analysis techniques use data reduction, data display and verification inference. Information accuracy is verified; credibility, transferability, reliability, and verifiability. The results of the study show that school financial management in meeting educational facilities and infrastructure standards for SMP Negeri 17 Serang City through planning and budgeting, administration and reporting and accountability according to Permendikbudristek 63 BOSP Juknis Year 2022. This study influences the importance of financial management to meet institutional and infrastructure standards education, because Stakeholders both internal and external can assess, analyze, monitor and fulfill various school needs.
Prevention and Detection of Poisoning Attacks in Medical-Based Machine Learning Web Applications

Machine Learning concepts and processes are widely used in all the fields for automated, accurate, and immediate decisions. But, hackers have vigorous motives to change the outcomes of the models and applications generated by algorithms and techniques of machine learning. The project revolves around the detection and prevention of poisoning attacks on the medical models of machine learning by either an Anomaly detection method or cryptographically based authentication and provenance. Anomaly detection is an application of cybersecurity where an anomaly detector is built. It identifies the rare occurrence of any unfamiliar data from the model and prevents the models from poisoning attacks. The web application holds cryptographic authentication and provenance through a secure socket layer and by some framework methods, through which the poisoning attacks can be detected and prevented. On this basis, our projects prevent hacks from occurring in the field of medicine.
Vision-Based Flying Obstacle Detection for Avoiding Midair Collisions: A Systematic Review

This paper presents a systematic review of articles on computer-vision-based flying obstacle detection with a focus on midair collision avoidance. Publications from the beginning until 2022 were searched in Scopus, IEEE, ACM, MDPI, and Web of Science databases. From the initial 647 publications obtained, 85 were finally selected and examined. The results show an increasing interest in this topic, especially in relation to object detection and tracking. Our study hypothesizes that the widespread access to commercial drones, the improvements in single-board computers, and their compatibility with computer vision libraries have contributed to the increase in the number of publications. The review also shows that the proposed algorithms are mainly tested using simulation software and flight simulators, and only 26 papers report testing with physical flying vehicles. This systematic review highlights other gaps to be addressed in future work. Several identified challenges are related to increasing the success rate of threat detection and testing solutions in complex scenarios.
Problem Faced by the Farmers in Using Information Communication Technology Tools for receiving Rice Production Information

The facilitation of technology transfer to farmers plays a crucial role in enhancing agricultural productivity within the context of Bangladesh. In relation to this matter, a study was undertaken to evaluate the extent of problems faced by the farmers in utilizing Information Communication Technology (ICT) technologies for accessing rice production information. Data were collected from randomly selected 340 ICT tool using rice farmers of four selected districts of Bangladesh. The results indicated that majority (66.2%) of the rice farmers faced severe problem, compared to 27.9% and 5.9% of them faced moderate and low problem respectively in using ICT tools for receiving rice production technologies. Based on Standardized Problem Faced Index, top five severe problems were lack of ICT knowledge of farmers, high cost of internet packages, lack of training in using of ICT tools, high cost of ICT tools and required more time to learn using ICT tools. These problems may be minimized by providing necessary training to the farmers on using ICT tools for receiving rice production information, cost reduction of internet package and ICT tools, providing better internet connectivity, uninterrupted power supply as perceived by the farmers.
Searchable Encryption

This chapter presents the beginnings of searchable encryption, definitions, and types. Also, one section is focused on the components of searchable encryption schemes, presenting the architectures, security requirements, and search operation types. Further, the chapter presents recent advancements in searchable encryption discussing the advantages, disadvantages, performance, security requirements, and presents case studies and practical applications in different domains (for example, Internet-of-Things, Machine Learning, Artificial Intelligence, etc.). Lastly, the chapter presents the challenges and research directions for searchable encryption.
Analysing the Influence of Components of Entrepreneurial Ecosystem on the Entrepreneurial Process

The entrepreneurial ecosystem is a popular concept because of its systematic approach to analysing entrepreneurship and promoting entrepreneurial activity in the region. However, studies on EE have lacked a framework for indicating the influence that EE components have on entrepreneurship. The authors fill this gap in our research by investigating the influence of the components of the entrepreneurial environment on the entrepreneurial process. Entrepreneurial Intention Development, Entrepreneurial Exploration, and Entrepreneurial Exploitation are the three stages of the entrepreneurial process that were identified. To investigate complex systems and grasp the system's causal linkages, fuzzy cognitive modelling approach was employed. This study provides insights into the relationships between the components of the entrepreneurial ecosystem and the entrepreneurial process, which helps to support entrepreneurship during its different phases.
Early Diagnosis of Typhoid Fever: Uses of Clinical Profile, Blood Culture and IGM Antibody: A Prospective Study from a Tertiary Care Centre in Western Uttar Pradesh

Objective:• To study clinical profile of typhoid in children.• To study Comparison of clinical profile and its correlation with blood culture and IgM antibody detection testfor early diagnosis of fever.• To study blood culture and widal at tertiary care centre in western UP.Methods: The study was carried out in Department of Pediatrics, Saraswathi Institute of Medical Sciences, Hapur,UP. All the children presenting with fever, aged from 6 months to 18 years, visited in the pediatric OPD(outpatientdepartment) and IPD(in patient department) of Saraswathi Institute of Medical Sciences from November 2020 toOctober 2022 were included in the study.Results: IgM antibody test was found positive in total 167 cases and negative in 63 cases. Also, blood culture wasreported positive in 157 cases and negative in 73 cases. Out of total positive IgM antibody, 138 showed positivefor blood culture and 29 were reported negative in blood culture. Out of total negative IgM antibody, 19 showedpositive for blood culture and 44 were reported negative in blood culture.Conclusion: Present study shows that typhoid IgM procedure is more sensitive than the established Widalagglutination assay and results can be obtained within 15 minutes while widal agglutination test can be doneafter 1 week. Typhi IgM test can be done after 72 hours of illness as IgM antibody becomes positive after 3 days ofinfection with Salmonella typhi. It is cost effective and can be done bed side.
LOGO recognition system based on deep learning

We used the deep learning architecture designed by ourselves to identify the logo, with good effect and accuracy. Our architecture uses four convolutional neural network architectures, two pooling structures and two fully connected neural network architecture.The characteristic of our architecture is that it is relatively simple. We can use the limited things we learn to create a program that meets our requirements.The results of the test were relatively successful. The logo recognition accuracy for our own data set can reach 95.83%.
Ship course change control based on humanoid intelligent control

A ship heading intelligent control algorithm is designed in this paper. First, ship heading control objective is proposed by input of control rudder angle and output of ship heading; Then, according to the deviation trend of ship heading output and its ideal trajectory, information about course deviation and its rate is extracted; Finally, to achieve ship heading control, rudder angle control strategy is enactmented under specific state by adopting human thinking, reasoning and control strategy. The simulation is executed with MATLAB software, and the results show that the controllerwhich is stability has strong robustness.
ANALYSIS AND SELECTION OF TOOLS FOR THE DEVELOPMENT OF AN APPLICATION WITH ELEMENTS OF AUGMENTED REALITY IN THE FIELD OF EDUCATION

The article includes review, analysis and selection of application development instruments with augmented reality elements. The object and subject of the study were identified as well. The assessment of the need in an application with elements of augmented reality in educational field was carried out by conducting a survey among students of higher educational institutions. Such tools as Vuforia, Tango, ARCore, ARKit were observed. Instruments for development of the application in order to choose a suitable one to integrate augmented reality technology, for. ex. Unity 3D, Unreal Engine. Instruments of 3D model design and illustrations were also analyzed. for ex. Blender 3D, 3Ds Max. Advantages and disadvantages of every kind of application are considered, that allows to justify the choice of instruments for development of the application with augmented reality elements in the educational field.
COMPARATIVE ANALYSIS OF METHODS OF UNSUPERVISED MACHINE LEARNING FOR ANOMALY DETECTION IN THE IOT SYSTEMS

Classical unsupervised machine learning methods such as k-nearest neighbor method, histogram-based outlier estimation, isolating forest, cluster local outlier factor are considered in order to identify the most efficient one to be used as the basis for anomaly detection system in IoT traffic. The IoT–23 Dataset, an open-source dataset, is used as the data for the study. The dataset is dimensioned and consists of 23 factors. The study considers an unsupervised learning method «with no teacher» based on copulas, the use of which helps to fully reveal interaction between evaluated factors, which can be successfully used in network traffic analysis in order to detect anomalies. As a result, the accuracy levels of these IoT network anomaly detection methods are compared.