Reviewerly/exHarmony huggingface dataset:
Haar wavelet operational matrix based numerical inversion of Laplace transform for irrational and transcendental transfer functions

Irrational and transcendental functions can often be seen in signal processing or physical phenomena analysis as consequences of fractional-order and distributed order models that result in fractional or partial differential equations. In cases when finding solution in analytical form tends to be difficult or impossible, numerical calculations such as Haar wavelet operational matrix method can be used. Haar wavelet establishes a direct procedure for transfer function inversion using the wavelet operational matrix for orthogonal function set integration. In this paper an inverse Laplace transform of irrational and transcendental transfer functions using Haar wavelet operational matrix is proposed. Results for a number inverse Laplace transforms are numerically solved and compared with the analytical solutions and solutions provided by commonly used Invlap and NILT algorithms. This approach is useful when the original cannot be represented by an analytical formula and validity of the obtained result needs to be crosschecked and error estimated.
Competition of educational and research projects of natural science orientation as a means of developing the cognitive interest of pedagogical university students

The development of the interest of students of humanitarian profiles in preparation for obtaining natural science knowledge is an urgent problem of the modern higher pedagogical school. There is a constant search and testing of techniques, means and methods to solve this problem. The article considers the competition of educational and research projects of natural science orientation as a means of developing students' cognitive interest. The authors shared their experience of organizing and conducting such a competition at a pedagogical university, as well as the results of its impact on the attitude of future teachers to obtaining scientific knowledge about the world around them. Examples of topics of educational and research projects of a natural science orientation, carried out by students studying in the humanities, as well as a list of questions that were asked to students in order to identify the nature of their attitude to the study of natural sciences, are presented. The conducted research confirmed the positive role of the competition of educational and research projects in solving the problem of developing the cognitive interest of students of a pedagogical university. The presented materials can serve as a basis for further research in the field of teaching methods of various university disciplines and the organization of independent work of students in higher education.
Model of organizational, methodological and personnel support for educational activities in Russian in the Tanzanian education system as a theoretical construct

The article updates the importance of promoting the Russian language in foreign countries in modern geopolitical conditions, one of the tools of which is assistance to foreign partners in organizational, methodological and personnel support for teaching Russian as a foreign language. The importance of developing a model of organizational, methodological and personnel support for educational activities in Russian in the education system of the United Republic of Tanzania is substantiated. It is proved that the model developed is a normative model that reflects ideas on improving pedagogical activities for teaching Tanzanians Russian. The main functions of the model are explanatory, illustrative, informational and projective ones. The leading idea of the model is proposed: «Language is the best mediator for establishing friendship and harmony» (Erasmus of Rotterdam). The block structure of the model is substantiated and the content of the target, analytical-diagnostic, motivational, methodological, content-design, methodological and performance-prospective blocks is revealed. The interpretation of organizational, methodological and personnel support for educational activities in Russian in the education system of the Republic of Tanzania is argued as a set of educational resources and pedagogical conditions that ensure the effectiveness of educational activities in Russian in the Republic of Tanzania, both in terms of increasing the level of teaching Russian as a foreign language and in part of promoting the interest in the Russian language among Tanzanians. Scientific approaches to the development of organizational, methodological and personnel support for educational activities in Russian in the Tanzanian education system and the principles of their implementation are outlined, including systemic, communicative-active, competence-based and cultural approaches, the principle of integrity, the principle of structure, the principle of dynamism, the principle of completeness of information provision, the principle of compliance with the speech needs of students, the principle of didactic consistency, the principle of personal orientation, the principle of activity, the principle of dialogicity (dialogue of cultures), the principle of cultural conformity.
Digital technologies in the process of studying regional studies in the framework of secondary general education: problems, practice, prospects (on the example of studying the Samara Region)

Due to the fact that the formation of an information society poses new challenges to pedagogy, the main trend of modern Russian education is a combination of traditional teaching methods and innovations. In this article, taking into account the accumulated experience in pedagogical science, the problems and features of the use of digital technologies in the educational process of studying regional studies of the Samara Region of secondary general education are considered. Based on the conducted pedagogical experiment, a sociological survey and their own experience, the authors reveal the advantages and possible disadvantages of using digital technologies such as information and communication map and 3D-modeling in regional studies lessons. The development and testing of scientific and methodological support of regional history in the educational process, taking into account the use of digital technologies, includes the following aspects: theoretical description of the digital technologies; the possibility of introducing regional studies into the educational process; description of a pedagogical experiment that showed an improvement in the quality of knowledge, the development of personal qualities and the formation of necessary competencies among students. The results of this study may be of practical interest for teachers of secondary schools and for students of pedagogical universities.
Development and evaluation of Reinforcement Learning models for the FOSSBot Open-Source educational robot

Machine learning algorithms play a crucial role in addressing real-world tasks by harnessing data-driven insights to optimize and enhance decision-making processes. Their application in robotics usually relies on simulations as an essential testing ground, allowing us to refine solutions, minimize risks, and fine-tune algorithms before deploying them in practical, physical environments. In this work, we evaluate the performance of two popular reinforcement learning algorithms, namely Proximal Policy Optimization (PPO) and Deep Q Network (DQN): i) on an obstacle detection and avoidance task, and ii) on a navigation task. For this purpose, we rely on the FOSSBot open-source educational robot, which contains ultrasonic sensors, infrared obstacle sensors, an Inertial Measurement Unit, and odometers. More specifically, we work on a simulation of FOSSBot in the CoppeliaSim environment. The evaluation results on the grid environment are very promising for our algorithms since the success rates (i.e. reaching the destination without collision) for both algorithms is above 93%. As for the simulated environment, the results show the superiority of PPO over DQN, with PPO scoring a success rate of 74%, making it a promising navigation algorithm candidate for the real-world FOSSBot.
Evaluating Cybersecurity Certifications: A Framework for Extracting Educational Scenarios in Cybersecurity Training

In the rapidly evolving domain of cybersecurity, traditional educational paradigms often fall short in equipping students with the practical skills necessary to defend against real-world threats. This paper delves into a review of existing literature, focusing on the development of hands-on cybersecurity training that synergistically integrates both offensive and defensive scenarios. Recognizing the wealth of knowledge encapsulated in established community and private-sector technical certifications, we propose a novel framework. This framework seeks to distill and adapt scenarios from accredited cybersecurity offensive and defensive certifications, creating a holistic training experience. Furthermore, the adaptability of these scenarios to lab infrastructures, as suggested in prior research, ensures a seamless integration into existing educational environments. By bridging the gap between offense and defense, our approach aims to foster a new generation of cybersecurity professionals who are not only theoretically proficient but also practically adept in navigating the multifaceted challenges of the cyber realm.
Automatic Fuzzy Cognitive Maps for Explainable Image-based Pneumonia Detection

Pneumonia has a significant impact on morbidity and mortality worldwide and is associated with serious diseases, such as coronavirus disease 2019 (COVID-19). Pneumonia diagnosis is typically performed by medical experts, trained to evaluate chest x-rays, which is usually a difficult and time-consuming task. To address this problem, in this paper, a novel classification scheme based on a Fuzzy Cognitive Map (FCM) is introduced. The proposed FCM model is applied for the detection of foci of consolidation, which is a common radiographic manifestation of pneumonia, while enabling the explanation of the outcome using linguistic terms. Also, unlike most FCM models, it is automatic, in the sense that it does not require any manual intervention for the construction of the fuzzy graph. Experimental results using publicly available datasets demonstrate the effectiveness of the introduced model.
Evaluation and Prediction of Resource Usage for multi-parametric Deep Learning training and inference

Deep learning is increasingly used in diverse application fields with results typically surpassing those of traditional machine learning techniques. The portfolio of available neural networks is wide, consisting of the full range in terms of complexity, from compact networks to large ones with multiple layers and parameters. This heterogeneity in the model topology is reflected, not necessarily linearly, on the required computational resources for training and inference. Similarly, the environments where the neural networks are trained and executed are transformed from fully-fledged centralized nodes to distributed architectures with constrained resources. In this view, computational resource requirements can be one of the criteria for resource usage management and neural network selection. In this work we measure the training times for a set of five convolutional neural networks of varying complexity and age (GoogleNet, ShuffleNet, VGGish, YAMNet) under different training configurations, considering the batch size, the number of epochs and the learning rate. These measurements are used to create a CPU-time-training dataset of more than 500 values. This dataset is used to train and evaluate models, based on neural networks, for estimating and predicting training times depending on the models employed and the training parameters. Five regression models have been trained and evaluated in terms of correlation coefficient and root mean square error. In addition, we measure the CPU times needed for inference for a subset of the trained models, which prove to be uncorrelated with the corresponding training times.
Development and Research of a Control Model Based on Noise-Immune Quantum Computing, Suppression and Correction of Errors in Quantum Computing

In recent years, quantum information systems have attracted increasing attention of researchers in the field of computer science and physics. However, the introduction and practical application of quantum computing is limited by the influence of noise and errors that occur in quantum systems. To implement effective control and improve the reliability of quantum information systems, it is necessary to develop methods that can suppress and correct errors in the process of quantum computing. The purpose of this work is to develop and study a control model based on noise-immune quantum computing, as well as methods for suppressing and correcting errors in quantum computing. The paper proposes a combination of different approaches, including the use of error correction codes, noise suppression algorithms, and methods for optimal control of quantum information systems. In the course of the study, a control model was developed that allows efficient processing of information in quantum systems, taking into account the presence of noise and errors. Experiments were carried out using real quantum devices to evaluate the effectiveness of the proposed model. The experimental results show that the developed method can significantly improve the reliability and accuracy of quantum computing. The proposed control model based on noise-immune quantum computing and methods for suppressing and correcting errors represent a significant contribution to the development of quantum information systems. Further development and optimization of the proposed approach can lead to the creation of more reliable and efficient quantum systems capable of solving complex computational problems.
CPU and GPU Parallelism of the A* Algorithm on solving N-Puzzle problems

This paper discusses the implementation of parallelism on the A* algorithm, using both the central processing unit and the graphics processing unit, in order to increase its efficiency in terms of the necessary time to solve Sliding Puzzle problems. The purpose of this paper, is to investigate the capabilities of the graphics card programming and to examine potential improvements resulting from its integration in the A* algorithm. As part of the work, data is collected from experimental tests, which are used to support hypotheses and ultimately draw conclusions and recommendations that may lead to increased efficiency by reducing the time required to solve N-Puzzle problems.
Correlation as an ARM Interestingness Measure for Numeric Datasets

A key issue in Associating Rule Mining (ARM) is the handling of the huge number of association rules that emerge in the output of the data mining process. A number of interestingness measures have been proposed and are used in order to quantify the usefulness (relevance) of each one rule. This study approaches measure effectiveness in terms of mitigating information loss introduced by the noise inherent to the application considered, and/or by the data preparation stage. The approach is put to test by applying ARM on two numeric datasets: (a) the 1M MovieLens dataset, and (b) an academic dataset of student assessment scores accumulated over a twelve years period. The Pearson and Spearman correlation coefficients are used to rank the rules in the output of the ARM process. The results obtained indicate that the proposed approach leads to improved performance and provides more insight into the rules ranking problem when ARM is applied on collections involving numeric data of the ratio measurement scale.
AN APPROACH TO THE COLLECTION OF PERFORMANCE INDICATORS IN THE TASKS OF PLANNING THE ACTIVITIES OF A UNIVERSITY IN THE CONDITIONS OF THE SPREAD OF A NEW CORONAVIRUS INFECTION

The article describes an approach to the reconciliation of performance indicators when solving critical tasks inherent to higher education institutions in the context of planning activities in the spread of particularly dangerous infections. The approach can be generalized to the processes of planning the activities of large organizations in cases when a large number of independent tasks with different indicators and criteria of solution quality are solved in parallel. The described approach allows to determine the variant of university management and indicators of task solution quality based on the fulfillment of the necessary list of critical tasks and&#x0D; performance evaluation criteria. The choice of the best option for solving critical tasks is made on the basis of maximizing the convolution of these indicators. An example illustrating the peculiarities of practical application of the proposed approach is given.
Adaptive AI-Driven Assessment for Competency-Based Learning Scenarios

In the realm of competency-based education, the integration of adaptive AI-driven assessment strategies brings forth a paradigm shift in evaluating learner mastery. This chapter delves into the intricacies of designing learning scenarios that seamlessly blend pedagogy with AI algorithms to offer personalized, data-informed assessments. By meticulously selecting objectives, designing pedagogical approaches, and orchestrating learner activities, educators create a foundation for adaptive assessment. The integration of AI algorithms enhances evaluation precision, enabling real-time identification of learning gaps and strengths. This chapter delves into the application of machine learning algorithms for tailored feedback, remediation, and ongoing supervision, fostering a learner-centric environment. Through real-world cases and innovative practices, educators gain insights into crafting assessment systems that empower learners to excel in a competency-driven landscape.
The Impact of Data Processing by Neural Networks on Academic Failure

Academic failure has become a shocking phenomenon, affecting large numbers of students at all stages of education, particularly in the early years, and students and their families experience academic failure in a dramatic way. For a long time, academic failure among university students has been the subject of heated debate. Many educational psychologists have tried to understand and explain it. Statisticians have tried to predict it. This research (chapter) aims to classify students into several categories, as well as using artificial neural networks to classify first-year students and identify variables likely to explain the problem (students failing at the end of their first year).
E-Learning From Virtual Reality to Augmented Reality

E-learning has evolved to incorporate innovative technologies, from virtual reality (VR) to augmented reality (AR). VR immerses learners in immersive virtual worlds, ideal for simulations and hands-on training. In contrast, AR superimposes digital elements onto the real world via devices such as smartphones and AR glasses, offering contextual information and interactive simulations. These technologies improve engagement by capturing attention, enabling hands-on experiences and deeper understanding, while personalizing learning. VR's 3D environments and AR's overlays enhance memorization. The integration of these approaches depends on pedagogical objectives and available resources, while their combination can create even richer and more varied learning experiences.
Biometric Identification Using Face Mask DL and Open CV

COVID-19 is a disease which spreads with human-to-human interaction. In this situation, humans need to be distanced from each other. To prevent the pandemic, people need to wear masks. Wearing a mask is mandatory for all people. That's why it is important to detect whether a person is wearing it or not. This chapter aims to provide a biometric approach for COVID 19 prevention. In this research the machine is able to check whether a person is wearing a mask or not from an image or a live stream. This research is also a smart approach for smart cities. In this research, the authors generate the artificial dataset by adding the mask on the faces of the persons in the images. And that can be done by the automation of the unmasked people's images dataset. The dataset is trained using tensor flow/keras for providing the classifier which classifies the image. This research is valid on image and on live streams.
Enhancing Access to Electronic Information Through Digital Transformation in KwaZulu-Natal Department of Health Libraries in South Africa

This chapter analyses the role of digital transformation in KwaZulu-Natal Department of Health Libraries in South Africa, exploring if and how digital technologies can be adopted and used in enhancing access to electronic information. The chapter utilised literature review to critically analyse the role of digital transformation in healthcare libraries, digital technologies used in enhancing access to electronic information, challenges of digital transformation in healthcare libraries, and the strategies to overcoming these challenges. Technology acceptance model (TAM) was used as the underpinning theory to guide this study. Recommendations suggest a need to prioritize digital transformation and provide enough resources to healthcare libraries and formulate policies that would facilitate this process.
Key Challenges Faced When Preserving Records in Traditional Councils During the 4th Industrial Revolution

The 4th industrial revolution (4IR) has brought significant technological advancements that have impacted various sectors, including governance and record-keeping. This chapter examines the challenges faced by traditional councils when preserving records during the 4IR. The chapter adopted a desktop research methodology. The data were subjected to substantive and extensive analysis through the instrumentality of content validity, content analysis, and textual criticism to establish facts that defend or refute the hypothesis. The study revealed that traditional councils lack the necessary infrastructure and resources to digitise records, leading to concerns about data loss, system failures, and cybersecurity threats. In essence, the councils are still struggling to protect and secure the records preserved in their custody; as such, council members need digital literacy training. The study recommends strategies that traditional councils should use to preserve records during the 4IR.
Enhancing Identification of IoT Anomalies in Smart Homes Using Secure Blockchain Technology

Numerous technologies that automate processes and simplify our lives are included in smart homes. These gadgets may be helpful for various things, including temperature, lighting, and security access. Smart homes fundamentally enable remote control of equipment and appliances for homeowners via the internet of things (IoT) platform. Smart houses are able to understand their owners' routines and modify in accordance with their capacity for self-learning. The requirement to identify abnormalities in data created by smart homes arises from the necessity of convenience and cost savings in such a setting, as well as from the involvement of numerous devices. The topic of anomaly detection using deep learning is covered in this chapter. Additionally, the suggested solution is more secure because to the usage of block chain technology. Results show that the suggested strategy has exceptional accuracy and recall.