Reviewerly/exHarmony huggingface dataset:
K.D. Ushinsky – harbringer of personal-value approach in education

The analyzed basic pedagogical ideas of the great pedagogue are significant in the modern conditions of the development of Russian education. The considered main principles of K.D. Ushinsky's pedagogical theory were created in the era of Great reforms and are still relevant, such as the ideology of democratization of the education system and the commitment to the people in education. The central idea of K.D. Ushinsky's pedagogical theory is the recognition of the creative power of the working people in the historical process and their right to a full-fledged education. The struggle of the Russian progressive public for the creation of public education in the era of Great Reforms contributed to the formation of the social and pedagogical movement. Contrasting the policy of “unenlightened enlightenment” with the scientifically grounded concept of the democratization of the education system, K.D. Ushinsky began to develop an anthropological paradigm. Denying universal pedagogy, he declared the need for a national school and national pedagogy, which implied the rejection of blind copying of foreign educational systems, the approval of real education that prepares students for life. K.D. Ushinsky proclaimed the democratically interpreted concept of nationality as the basis of education; asserts the dominant role of the native language in education. The great scientist declared the connection of theory and practice in the pedagogical process; discovered the inseparable connection of pedagogy with other humanities; developed a unique concept of anthropological education and the formation of the creative personality of the teacher. The obtained results can be used in the educational process of students of pedagogical directions in the study of the discipline “History and Philosophy of Education”.
Point-rating system as a tool to increase learning motivation among foreign students

The purpose of this study is to select effective means of increasing the motivation for learning among foreign students and their widespread use in educational activities. The materials for the preparation of this study were the documents regulating the process of evaluating the educational activities of students, scientific works on this problem, as well as the author's own long-term observations in the course of pedagogical activity. As a result of the study, we conclude that one of the effective tools for increasing motivation for learning is a point-rating system. The practice of using the point-rating system in groups of foreign students proves its understanding and acceptance by foreign students, confirms its effectiveness as a means of enhancing learning activities, increasing motivation for constant self-control, and personal responsibility of students for learning outcomes in the discipline. For the first time we carry out an actual analysis and generalization of the results of the application of the point-rating system in groups of foreign students with the discipline “Russian Languag” as an example. The value of the work is due to the contribution to the popularization of the results of the introduction of educational technologies in the practice of teaching Russian as a foreign language. The obtained results are applicable as a component in further research on the selection of tools to increase the motivation to study among foreign students and the search for new methods and means to increase the effectiveness of educational activities in groups of foreign students.
Corporate culture of a university lecturer: development experience

We present the author's definition of the concept “corporate culture of a university lecturer”. Its structural components are: value, cognitive, operational, regulatory and corresponding indicators for assessing their development: adherence to corporate values, striving for self-realization, knowledge of corporate norms and rules of behavior, ethics of relations in a team, ability to work in a team, style of interpersonal interaction, readiness for self-regulation, social and professional responsibility. The normative, normative-personal and personal-productive levels of development of the corporate culture of the university teacher are established. The results of the statistically confirmed formative stage of the experiment based on the results of the developed pedagogical workshop “Improving the corporate culture of a lecturer”, presented by information and practical blocks and including productive learning technologies (cooperation, training, adult education) are presented; forms (lectures, seminars, discussions, practical work, organizational activity game, brainstorming, panel discussion); methods (problem learning, game, case, essay, portfolio, psychological exercises, presentation) and stages (motivational, transformative, reflexive).
Improved artificial potential field route planning method based on stochastic perturbation

As a typical route planning algorithm, the APF method has been widely used in robotics, autopilot and other fields. However, it also has weakness in local optimal point. In computer science and mathematics, stochastic perturbation is an accepted approach that can assist optimization algorithms leave the local optimal solution and achieve the global optimal solution as well as improve robustness and adaptability in specific application situations. therefore, the problem can be successfully solved by using stochastic perturbation. In this article, the traditional APF improved through stochastic perturbation. When the robot is running in an APF, the distance between the robot and the target point is calculated at all times. If the distance between the robot and the target point remains unchanged for a period of time or if the stop point oscillates, it is determined whether the robot has reached the target point at this time. If not, it is determined that the robot has fallen into a local optimal point at this time. After completing the judgment, stochastic perturbation is applied to the robot, causing it to jump out of the local optimal point and continue to navigate to the target point. The results of the comparison between the paths generated by the traditional method and the improved method validate the effectiveness of this method.
Controller design for transition flight of rotor-driven VTOL fixed-wing UAV based on PID

The field of drones has received a lot of attention in recent years and is developing well. In the process of resource circulation in cities, UAVs play an increasingly important role, such as logistics, monitoring, emergency rescue, etc. However, traditional rotary-wing UAVs and fixed-wing UAVs can be limited in some conditions, such as load, cruising speed and take-off area. Based on the above reasons, this paper designs a rotor-driven VTOL fixed-wing UAV, which combines the vertical take-off and landing of rotor and the fast cruise of fixed-wing UAV and other characteristics. And based on this UAV, for the transition process from hovering state to cruising state, a PID controller is used to control the cruising speed, and then the flight altitude is controlled by a series-level PID controller. The final results show that the cruise speed can be stabilized at about 50km/h within 3 seconds by adjusting the PID parameters without considering the motor speed saturation. In addition, while ensuring the cruise speed reaches convergence quickly, the PID parameters of the acceleration loop, velocity loop and altitude loop can be adjusted separately to control the altitude error of the UAV within 5cm. This study provides a reference for researchers to study the control of rotor-driven UAVs in the transition process.
Quantum Computing: Fundamentals, Progress, and Implications

Abstract: Quantum computing, an interdisciplinary field blending quantum mechanics and computer science, holds promise to revolutionize computational capabilities. Unlike classical computers, which utilize bits to process information in a binary fashion, quantum computers deploy qubits, capable of existing in multiple states simultaneously due to superposition. This unique property, paired with entanglement, offers unparalleled processing power, positioning quantum computing to tackle problems deemed unsolvable by classical means. While heralded for potential applications in cryptography, drug discovery, and complex modeling, quantum computing faces challenges including decoherence, scalability, and error correction. This paper delves into the foundational principles of quantum computing, its current advancements, the vast applications it could revolutionize, and the significant challenges and societal implications it brings. Through comprehensive exploration, we aim to shed light on both the transformative potential of quantum computing and the hurdles that lay in its path.
APPLICATION OF DATA MINING USING THE NAÏVE BAYES CLASSIFIER METHOD TO ANALYZE THE LEVEL OF CUSTOMER SATISFACTION IN ICE CREAM MIXUE

Ice Cream Mixue is a company engaged in the production of beverages and food. They provide a wide range of ice cream flavors with premium quality and natural ingredients, attracting many customers' attention. This study uses a quantitative method because the data sources are apparent. The research method is a scientific way to obtain data with specific purposes and uses. The technique used is the Naïve Bayes Classifier Algorithm with rapidminer results. 356 customers answered the questionnaire results satisfied, 64 customers who answered the questionnaire results were neutral, and 80 customers responded to the questionnaire results and were not happy. In this case, the Naïve Bayes Classifier Method can predict the level of customer satisfaction for ice cream mixes using the Rapidminer Application with Satisfied Words. This analysis obtained Class Precision satisfaction results of 94.72%, pred. neutral 2.0%, and pred. They are dissatisfied by 4%.
A Novel Method of Fraud Detection of Credit Cards by Fuzzy, LSTM, and PSO Optimization

Since online shopping has become so popular, credit card theft has skyrocketed. This makes it hard to spot fake charges on accounts. In this research, credit card fraud detection is performed using a fuzzy database. It has been considered a data mining challenge to reliably identify whether or not a transaction is legitimate. This paper discusses the LSTM method and fuzzy logic. The learning process was sped up and made more precise by using a technique called particle swarm optimization (PSO). Performance values have been compared with those of the LSTM and fuzzy logic techniques, and a PSO-based neural network has been intensively trained by increasing the number of iterations and the population, or number of swarms. It has been shown that the PSO-based algorithm yields the best result for detecting fraudulent transactions. The goal of this method is to lower the mean square error (MSE) rate of the system. PSO is a popular optimization technique that can be used to locate the optimal set of features for the credit card fraud detection system. The proposed method PSO shows less mean squared error compared with Fuzzy and LSTM techniques.
Enhanced Approach for Bug Severity Prediction: Experimentation and Scope for Improvements

Software development is an iterative process, where developers create, test, and refine their code until it is ready for release. Along the way, bugs and issues are inevitable. A bug can be any error identified in requirement specification, design or implementation of any project. These bugs need to be categorized and assigned to developers to be resolved. the number of bugs generated in any large scale project are vast in number. These bugs can have significant or no impact on the project depending on the type of bug. The aim of this study is to develop a deep learning-based bug severity prediction model that can accurately predict the severity levels of software bugs. This study aims to address the limitations of the current manual bug severity assessment process and provide an automated solution using various classifiers e.g. Naïve Bayes, Logistic regression, KNN and Support vector machine along with Mutual information as feature selection method, that can assist software development teams in giving severity code to bugs effectively. It seeks to improve the overall software development process by reducing the time and effort required for bug resolution and enhancing the quality and reliability of software.
Review of robotics competition. Air robotics

The development of robotics technologies requires an increase in the level of scientific and technical develop-ments and the creation of a profile reserve, as well as the formation of a system for training highly qualified spe-cialists. One of the ways to assess the achieved level of development and qualifications of engineering teams is to hold competitions at various levels. The article provides an overview of various events in competitive robotics. The second part deals with competitions in water and air. The competitions are structured according to the format of the competition, the environment of functioning and the age of the participants. Conclusions are made about the pro-spects of various events of competitive robotics, as well as the relevance of some of them. The interrelationships of the competencies achieved when participating in competitions in various areas are presented, and a conclusion is made about the conduct of vocational guidance work.
An Efficient and Improved Algorithm for a Recommender System to Detect &amp; Recognize Communities in Social Networks

Social Network is a communicative platform which is a part of social media, useful for interaction of information among people i.e. users. There will be millions of users over online Social Networks, they might or might not have similar interests. People with similar interests / mindset would like to have friendly relationship among themselves. Connections with many similar mindset people forms groups or communities. These Communities will be helpful for gaining knowledge/information transmission. In this paper, we will observe efficient methods for recommending groups or communities to users based on their similarities with their friend's or user’s similar to them and groups followed by their friend's, using Hybrid Recommendation Filtering System combined with Singular Value Decomposition.
Image Classification for Breast Cancer Using a Modified Convolution Neural Network Architecture

The most common type of cancer that results in death is breast cancer. In the world, millions of people struggle with this disease. Breast cancer can affect men and women but women are more affected. For awareness, it is necessary to understand the sign and symptoms of breast cancer. The most common sign is an abnormal lump in the breast. But there may be many reasons of develop abnormal lumps. Computer-Aided Diagnosis (CAD) is extensively used in pathological image analysis to help pathologists enhance diagnosis efficiency, accuracy, and consistency. Recent studies have looked into deep learning methodologies to improve the effectiveness of pathological CAD.
A Hybrid Grey Wolf Optimization and Constriction Factor based PSO Algorithm for Workflow Scheduling in Cloud

Due to its flexibility, scalability, and cost-effectiveness of cloud computing, it has emerged as a popular platform for hosting various applications. However, optimizing workflow scheduling in the cloud is still a challenging problem because of the dynamic nature of cloud resources and the diversity of user requirements. In this context, Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) algorithms have been proposed as effective techniques for improving workflow scheduling in cloud environments. The primary objective of this work is to propose a workflow scheduling algorithm that optimizes the makespan, service cost, and load balance in the cloud. The proposed HGWOCPSO hybrid algorithm employs GWO and Constriction factor based PSO (CPSO) for the workflow optimization. The algorithm is simulated on Workflowsim, where a set of scientific workflows with varying task sizes and inter-task communication requirements are executed on a cloud platform. The simulation results show that the proposed algorithm outperforms existing algorithms in terms of makespan, service cost, and load balance. The employed GWO algorithm mitigates the problem of local optima that is inherent in PSO algorithm.
A Deep Reinforcement Learning-Based Model for Optimal Resource Allocation and Task Scheduling in Cloud Computing

The advent of cloud computing has dramatically altered how information is stored and retrieved. However, the effectiveness and speed of cloud-based applications can be significantly impacted by inefficiencies in the distribution of resources and task scheduling. Such issues have been challenging, but machine and deep learning methods have shown great potential in recent years. This paper suggests a new technique called Deep Q-Networks and Actor-Critic (DQNAC) models that enhance cloud computing efficiency by optimizing resource allocation and task scheduling. We evaluate our approach using a dataset of real-world cloud workload traces and demonstrate that it can significantly improve resource utilization and overall performance compared to traditional approaches. Furthermore, our findings indicate that deep reinforcement learning (DRL)-based methods can be potent and effective for optimizing cloud computing, leading to improved cloud-based application efficiency and flexibility.
Performance Analysis of Selection and Migration for Virtual Machines in Cloud Computing

A cloud computing environment is composed of numerous heterogeneous resources and for efficient utilization of resources endorses virtualization. In virtualization, a cloud provider creates multiple instances of virtual machine which can be configured on a single host, hence increases the resource utilization. Though this can be further enhanced through the consolidation process, it requires selecting appropriate VM allocation and selection policy that work together to complete the process. To achieve the consolidation process with the physical data center is quite challenging therefore a virtual data center can be created with the help of simulators where by tasks can be simulated with varied configurations in different simulation environments. The simulation gives output corresponding to the simulation environment and is based on the performance metrics. In this work, a study is carried out on one such simulator named as CloudSim and analysis of its VM allocation along with selection policies. These policies will be implemented using PlanetLab workload to conclude about their performance based on few metrics.
فعّالية استخدام طريقة "المفتاح للعلوم" لترقيّة كفاءة الطلاب في استيعاب علم النحو بالمدرسة التربيّة الإسلاميّة جندونج أغام

Teaching grammar to students is one of the important subjects that need to choose and use the appropriate educational method for the conditions of students and their ability to understand the study. The educational method is the system used by teachers to achieve and communicate information to the minds of students in a way that improves the goals of education. In education, teachers use different methods to communicate the material to students in their classes so that the study goals are achieved. The researcher used experimental quantitative research to find out the effectiveness of using the "Key to Science" method to upgrade students' competence in understanding grammar. The community in this research is students in the first semester of the Islamic Educational School of Gundong and the researcher takes the sample in a proposive sampling way. The research tools used are observation and testing. The researcher used the research analysis as the "T" test base. The average result of the post-test in the experimental semester is 82.41. The average result of the post-test in the control class is 77.27. The researcher found that the result of the calculation (2.448) and the table (2.003) The result of the calculation is greater than the table and therefore the null hypothesis is returned and the alternative hypothesis is acceptable. The effectiveness of using the key to science method to upgrade students' competence in understanding grammar is 76-99% They absorb students in most teaching subjects The average score is 82.41. The researcher takes the conclusion that there is an effective use of the “key to science” method to upgrade the efficiency of students in absorbing grammar in the Islamic educational school of Canduang Agam.
A Novel Cyber Resilience Framework – Strategies and Best Practices for Today's Organizations

Cyber resilience refers to an organization's ability to maintain its essential functions, services despite cyber-attacks and swiftly recover from any disruptions. It involves proactive measures like gathering threat intelligence and managing risks, as well as reactive measures such as incident response planning, data backup and recovery. To achieve cyber resilience, organizations must implement robust cyber security measures, regularly update their incident response plans, and educate employees on safe online practices. Furthermore, having a comprehensive backup and recovery strategy in place is crucial to swiftly restore critical systems and data in the event of an attack. Overall, the proposed framework emphasizes cyber resilience as a continuous and proactive approach for managing cyber security risks and safeguarding against the growing threat of cyber-attacks.
Translation Service Implementation in Cloud: Automation Trends in English Translation Industry

INTRODUCTION: The development of information technology has led to the renewal of teaching methods, and cloud translation combining offline and online learning has become a trend in higher education.&#x0D; OBJECTIVES: It is becoming increasingly apparent that the "surface issues" of blended learning are being addressed, especially the lack of consistency in online task development, which leads to inefficiencies in deep understanding.&#x0D; METHODS: Through literature research, the factors affecting task planning in cloud translation are analyzed, and a cloud computing task planning model is established based on task learning theory.&#x0D; RESULTS: The results show that task-based cloud translation can increase students' learning engagement and that targeted group task design is critical in improving students' interest and translation skills.&#x0D; CONCLUSION: Using complex task modeling can improve the academic level of translation students and increase their involvement in translation projects.
Study on Software Defect Prediction Based on SVM and Decision Tree Algorithm

Software Defect Prediction is a process of identifying the potential defects in software systems before they occur. In this approach, the dataset containing information about software attributes is used as input, and the output is the prediction of whether the software is defective or not. The input dataset is generally in the form of a CSV file, which contains various software attributes such as cyclomatic complexity, essential complexity, Design Complexity, etc. The output of the defect prediction is binary classification. It is done by using SVM (Support Vector Machine) and a decision tree algorithm. This approach can help software developers identify their systems' defects before they cause any harm or affect the system’s performance.