Reviewerly/exHarmony huggingface dataset:
Staff Management for Android

The employees or the staffs are believed to be as the backbone of any organization and so their management becomes the deciding aspect for any firm to be successful. The proposed thesis seeks to design a personnel management model that may be accessed by the admin as well as the user. However, the primary purpose of the author is to target the healthcare sector and offer the thesis in managing the nursing staff. The model is intended to be put on two platforms that may be accessed by the user as well as the admin. In this example, the user is the one who is seeking for a job in the healthcare field. For this goal, an Android platform is constructed using the principles and programming language of JAVA; whilst on the other hand an application interface of web app is being offered to the admin as the back end. C# and Asp.net are the programming languages that are used on the back end of the system. The implemented software allows the user the ability to look for a job in nearby place and apply for a vacant position. The user is also provided the ability to schedule a space as per his feasibility. On the other side, the admin is granted a complete access to staff’s mobility and he may monitor it through the web app. The admin is given the opportunity to manage vacancies and allocate respective jobs to the workforce. Hence, it can be claimed that this proposed system model delivers an efficient manner to the admin to monitor and control the workforce. Further, the system is also planned to be implemented in the cloud using MS Azure.
A Case Study on Clustering of Datasets Using K-Means Algorithm in Spark

A crucial step in data analysis is clustering, which seeks to put similar data items together based on their inherent qualities. For the purpose of gaining meaningful insights from the growing number and complexity of contemporary datasets, effective and scalable clustering algorithms are crucial. In this article, we give a case study on clustering datasets in Spark, a well-liked distributed computing framework, using the K-means technique. The paper starts with an extensive overview of the literature on clustering methods and the difficulties posed by large-scale datasets. We draw attention to Spark's benefits for handling huge data processing and its applicability for K-means algorithm implementation. Then, we give a brief overview of Spark, highlighting its main features and functionalities, such as fault tolerance and in-memory processing. We investigate its applicability to distributed computing and how it resolves issues brought on by huge datasets. The case study design is provided, explaining the procedures needed to carry out the investigation. This covers the choice of the experimental design and its execution. To assess the scalability and performance of the K-means algorithm in Spark, it is essential to choose a suitable dataset that accurately represents real-world features. The experimental setup entails setting up the Spark cluster, utilising the Spark programming interface to perform the K-means algorithm, and choosing evaluation metrics to gauge the effectiveness of the clustering. The purpose of the study is to illustrate the advantages of utilising Spark for clustering analysis, including its performance, scalability, and usability. We test the K-means algorithm's capability to handle enormous datasets by running the clustering tests on the Spark cluster. In conclusion, this case study highlights Spark's potential for scalable and effective clustering analysis and advances our understanding of clustering algorithms in that environment. The results of this study can help academics and professionals use Spark's distributed computing capabilities for clustering huge datasets, allowing them to get insightful knowledge and make wise choices.
Design and Development of Mathematical Models for Computational Neuroscience

Through the use of mathematical and computer models, computational neuroscience is an interdisciplinary study that seeks to comprehend the principles and mechanisms underlying the functioning of the brain. These models are essential for understanding intricate neurological processes, giving information about the dynamics of the brain, and directing experimental research. The design and creation of mathematical models for computational neuroscience are presented in-depth in this study.The sections that follow explore several mathematical modelling strategies used in computational neuroscience. These include statistical models that analyse and infer associations from experimental data, biophysical models that explain the electrical properties of individual neurons and their connections, and network models that capture the connectivity and dynamics of brain circuits. Each modelling strategy is examined in terms of its mathematical foundation, underlying presuppositions, and potential limits, as well as instances of how it has been applied to certain areas of neuroscience study.The research also examines the model parameterization, validation, and refinement stages of the model creation process. It emphasises how the integration of experimental evidence, theoretical understanding, and computational simulations leads to iterative model refining. The difficulties and unanswered concerns associated with modelling complex neurological systems are also covered, emphasising the necessity of multi-scale and multi-modal methods to fully capture the complex dynamics of the brain.The paper ends with a prognosis for mathematical modeling's future in computational neuroscience. The development of virtual brain simulations for comprehending brain illnesses and planning therapeutic approaches are some of the rising trends that are highlighted in this article, along with the incorporation of machine learning techniques, anatomical and physiological restrictions, and the assimilation of these trends into models.
Prospects of Culture, Science and Human in the Context of a Technocratic Tide of the Post-Industrial Era

The main aim of the study is to investigate prerequisites, essence and possible consequences of deaxiologisation trends in culture and deanthropologisation tendencies in science which arose out in the post-industrial age. Globalization processes are of a great importance in deprivation the science in its technocratic mechanistic interpretation of its historically determined constructive “human-centric” potential that had been implementing through creation and development of spiritual foundations of a human and society. The new shape of science – techno-science – exists without general metaphysical basis, summarizing and generalizable epistemological principles and person-oriented beginnings. It becomes to signify the return to mechanism in the field of implementation of logical procedures and processes but in the new – technocratic – dimension. The “new” mechanism, unlike the “classic”, is not about reducing the nature of man, society and culture to their nature-centric grounds that base and depend on physical laws, but is about orientating to extrabiological and virtual technical analogies, so it makes one to point out comprehensive competition between the human and artificial intelligence in all fields of vital activity. The main points and conclusions have their grounds on analysis of the most significant stages of science development from classics to post-non-classics.
A Calibration Method for IMU Attatched to a Mobile Robot Using LSTM Network

In this paper, we proposed a calibration method for an IMU already attached to a mobile robot by using an long short-term memory (LSTM) neural network. The LSTM network takes IMU and lidar measurements as inputs and outputs compensated IMU measurements. The proposed method offers the advantage of compensating for deterministic errors of the IMU, including settling misalignment errors, while maintaining the IMU attached to the mobile robot. In the LSTM training process, true values of the IMU measurements are required. To obtain these values, we used an external camera to capture a marker on the mobile robot. By numerically differentiating the position and attitude calculated from the captured image, we acquired the true acceleration and angular velocity. We verified the performance of the proposed IMU error compensation method through experiments by using a mobile robot in an indoor environment.
Preprocessing Method for Color-Adaptive Pix2Pix Generative Neural Network Data

Adversarial generative neural networks (GANs) have received increased research interest in recent years, and they have been applied in various fields, such as image generation, transformation, and restoration. Pix2Pix, a popular GAN model, trains data in which input and output data are paired. However, because this method is based on sketches, it cannot consider colors once the training is completed. Not only to apply colors requires coloring on edges of all training data, which requires high effort to build data, but also it is difficult to automatically generate edges. In this paper, we proposed a method that considers colors and performs the automatic generation of edges required for Pix2Pix. The proposed method extracts edge data by using the canny edge detection algorithm and constructs color edge and color background data by using the extracted edge data. The applicability of the proposed method was verified by conducting experiments in the fashion field.
Blockchain in Hotel Business

Abstract: The hotel industry is facing numerous challenges such as inefficiencies, lack of transparency, and security issues. Blockchain technology has the potential to address these challenges and improve the overall operations of the hotel business. This research paper aims to explore the potential benefits and challenges of implementing blockchain technology in the hotel industry and identify successful case studies and examples. The study uses a mixed-methods approach to gather data from various sources and presents the findings through a comprehensive analysis of the potential use cases of blockchain technology in the hotel industry. The paper concludes with recommendations for hotel businesses to adopt blockchain technology successfully and a discussion on the future of blockchain technology in the hotel industry. The study highlights the potential of blockchain technology to revolutionize the hotel industry and bring innovation and disruption to traditional processes.
An Efficient Approach for Mitigating Insecure Direct Object Reference (IDOR) Bug Bounty Method

Abstract: This real-time paper explores Insecure Direct Object Reference (IDOR) vulnerabilities in the context of bug bounty programs. It provides an overview of IDOR attacks and their impact on web application security. The paper discusses common IDOR vulnerabilities, including how they can be discovered and exploited by attackers. It also highlights the importance of implementing proper access controls and other security measures to prevent IDOR attacks. The paper concludes with a discussion of best practices for identifying and mitigating IDOR vulnerabilities in bug bounty programs, including the use of manual testing
Reversible Medical Image Watermarking for ROI-Based Tamper Detection and Recovery

Abstract Medical image watermarking has become a widely studied topic in recent years. The method used to embed data into medical images must be reversible becouse tampered in medical images caused by irreversible embedding can lead to inaccurate diagnosis. Medical image watermark- ing should be able to identify the tampered area and recover it. This research work introduced a method for reversible watermarking of med- ical images. This method can embed data, detect any tampering that occurs, locate the area that has been tampered with, and then recov- ery that area of tampered in the medical image. The medical image is segmented into three sections. The first is the region of interest (ROI) for reversible embedding of patient records. The second is the region of non-interest (RONI) for reversible embedding of localization data for tampered and recovery areas. The third is the border area used for embedding coordinates of the selected ROI using Least Significant Bits (LSB). The proposed method has a capacity of up to 6 bpp of data and 12 % of the ROI area with very good visual quality of up to 50.86 dB. The watermarked image, when extracted, is identical from the original image except for the border area, provided that there is no tampering. If the ROI is tampered, the area of tampered can be identified and restored with results reaching up to +27.61 dB, with only a small difference in image quality -2.86 dB compared to the original image after extraction.
On the Convergence of Stochastic Aggregated Gradient Method

The minimization problem of the sum of a large set of convex functions arises in various applications. Methods such as incremental gradient, stochastic gradient, and aggregated gradient are popular choices for solving those problems as they do not require a full gradient evaluation at every iteration. In this paper, we analyze a generalization of the stochastic aggregated gradient method via an alternative technique based on the convergence of iterative linear systems. The technique provides a short proof for the $O(\kappa^{-1})$ linear convergence rate in the quadratic case. We observe that the technique is rather restrictive for the general case, and can provide weaker results.
Autonomous Face Classification Online Self-Training System Using Pretrained ResNet50 and Multinomial Naïve Bayes

This paper presents a novel, autonomous learning system working in real-time for face recognition. Multiple convolutional neural networks for face recognition tasks are available; however, these networks need training data and a relatively long training process as the training speed depends on hardware characteristics. Pretrained convolutional neural networks could be useful for encoding face images (after classifier layers are removed). This system uses a pretrained ResNet50 model to encode face images from a camera and the Multinomial Naïve Bayes for autonomous training in the real-time classification of persons. Faces of several persons visible in a camera are tracked using special cognitive tracking agents who deal with machine learning models. After a face in a new position of the frame appears (in a place where there was no face in the previous frames), the system checks if it is novel or not using a novelty detection algorithm based on an SVM classifier; if it is unknown, the system automatically starts training. As a result of the conducted experiments, one can conclude that good conditions provide assurance that the system can learn the faces of a new person who appears in the frame correctly. Based on our research, we can conclude that the critical element of this system working is the novelty detection algorithm. If false novelty detection works, the system can assign two or more different identities or classify a new person into one of the existing groups.
Impact of Education on Economic Development

Education is widely acknowledged as the most important tool for socioeconomic development in a country. It catalyzes boosting productivity and promoting technological advancements. There is a clear positive relationship between education levels and stages of economic growth. As the population's education level improves, it opens up and expands various sectors of the economy, including secondary, tertiary, and quaternary sectors, leading to increased job opportunities and contributing to the overall size of the country's gross domestic product. Qualitative research methods were employed in this study to uncover the impact of education on economic development, with a specific focus on the agriculture, manufacturing, and earnings sectors. The study explored the intricate connections between education and these sectors through document analysis, and observations. The findings highlighted the transformative power of education in driving sustainable and inclusive economic progress. Education also plays a pivotal role in uplifting agricultural and industrial productivity by adopting modern inputs and technology, resulting in higher wages for the workforce. Educated individuals tend to earn more, invest in their family's well-being and healthcare, and enjoy longer lives, thereby fostering societal development. This paper contributes to the existing body of knowledge by comprehensively understanding how education influences economic development, particularly in the agriculture, manufacturing, and earnings sectors. It underscores the importance of education as a driver of sustainable and inclusive growth, emphasizing the need for continued investment in education to ensure long-term socioeconomic progress.
Complementary Architecture of E-Learning Path Personalization and Optimization

Using computing algorithms to generate personalized learning resources to provide the needs and improve the capabilities, preferences, and academic performance of diverse learners is creating preferred learning environments. As more learning resources, strategies and techniques are frequently added to these e-learning systems, input data to personalize the learning path has been growing exponentially making swift responses to learner’s requests difficult. This study proposed a complementary learning path personalization architecture using ant colony (ACO) with nearest neighbour technique and genetic algorithm (GA) to extend the functions of the Spark framework purposely to develop a robust evolutionary computing algorithm. Experimental results indicate that complementing ACO, GA and Spark frameworks improved the generation of personalized learning resources and best-fitted-optimized learning paths. Spark-ACO took less computational time than standalone ACO. Combining ACO and GA improved the likelihood of an ant colony being trapped at a local optimum, and Spark-ACO-GA significantly enhanced the accuracy of the solutions.
Accurate Movement Detection of Artificially Intelligent Security Objects

A number of intelligent applications demand multiple item detection and tracking as vital elements. Although object search links the identified object through a series of frames, object detection pinpoints the thing's location in a scene. Over the last few decades, a wide range of approaches has been created, which may be divided into 2D and stereo-based 3D techniques. When used in limited circumstances, most of these strategies yield trustworthy findings. These limiting presumptions are used to determine the number of complex elements that object detection and tracking naturally entail. Environmental factors, object appearance, flow density, backdrop colour intensity information, the amount of time an object is present in the scene, object occlusion, a scene's maximum number of things, etc., are among the most often held presumptions. In real-time applications, these approaches' dependability is not assured. A modern surveillance system needs reliable object identification and tracking in an open area.
FORMATION OF PROFESSIONAL COMPETENCE OF ARCHITECT STUDENTS IN THE FRAMEWORK OF STUDYING CREATIVE DISCIPLINES

Annotation.At the present stage of reforming the system of higher architectural and artistic education, the development of the creative potential of future specialists is of considerable interest.As a result, the problem of development and improvement of personal qualities necessary for creative professional activity comes to the fore.The article analyzes the use of an in-depth scientific and methodological approach in the theory and practice of vocational education for representatives of creative specialties.It is noted that the profession of an architect has a unique creative potential for cognition and transformation of the surrounding reality.The analysis of the specifics of the formation of artistic and visual competencies of studentsarchitects, which are closely related to future professional creativity, is carried out.The article analyzes the results of research activities on the study of the creative heritage in the field of architecture and methods for developing the creative potential of students of architectural and artistic specialties.The object of the study is the process of professional university training of students of architectural and artistic specialties.The research was carried out on the basis of the generalized results of observations of the current and final assessments of classroom graphic tasks among first and second year students of the Architectural and Art Institute of the Odesa State Academy of Civil Engineering and Architecture.To achieve this goal, the following research methods were used: theoretical generalizations and comparisons of data, practical methods of educational observations, a multivariate method of statistical data processing, expert assessments and a comparative method of content analysis.The article presents the results of a methodological study of the process of architectural and artistic education.The conducted research determines the need to supplement and develop this methodology in order to increase the level of professional training of specialists in the field of creative professions.
Malaria detection using custom Semantic Segmentation Neural Network Architecture

Malaria is a significant disease that affects both animals and humans. The four main Plasmodium species that cause human malaria are Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae, and Plasmodium ovale. Plasmodium knowlesi, a parasite typically infecting forest macaque monkeys, was recently revealed to be able to be transmitted by anophelines and provoke malaria in humans. This provides an increasing risk of spreading the disease to areas previously unaffected with it and infecting people during the increasingly popular travels abroad. Microscopic examination remains one of the most often used methods for its laboratory confirmation. These tests, however, should be performed immediately after receiving samples from a firstcontact doctor to allow immediate therapy. This research presents a novel, semantic segmentation neural network architecture designed to quickly create a classification mask, giving the doctor information about the position, shape, and possible affiliation of detected elements. The evaluation method is based on a light microscope imagery and was created to overcome problems resulting from the human diagnosis specifics. There are 3 abstract classes containing healthy cells, cells with malaria and background. The outputted mask can be later mapped to a more readable form with the inclusion of contrasting colors, next to an original image for quick validation. Such an approach allows for semi-automatic recognition of possible disease, nevertheless still giving the final verdict to the specialist. The developed solution has achieved a high recognition accuracy of 96.65%, while the computer power requirements are kept at a minimum. The proposed solution can help reduce misclassification rates by providing additional data for the doctor and speed up the entire process with the early diagnosis made by a deep learning model.