Reviewerly/exHarmony huggingface dataset:
Method of projects as a resource for professional and personal development of a future teacher

The article is devoted to the problem of professional and personal development of future teachers. Modern society needs highly educated specialists who are ready for the challenges of the time. The teacher must possess not only subject competencies, but also high adaptability to updates, susceptibility to pedagogical innovations, dynamism, flexibility, mobility, digital literacy. Actual requirements for a teacher against the backdrop of rapid changes in society determine the content of his professional training, which is undergoing transformations at different levels. Attention is focused on the method of projects as a resource for the development of professional and personal characteristics of a specialist, ensuring his demand in the labor market. The project method is understood as a joint search-cognitive, creative, productive activity of a teacher and students, aimed at finding a solution to a problem that has arisen and culminating in a practical result. The experience of the successfully implemented information and creative project "Is it good to be able to read?", which became the winner of the International Festival "Children of the Rainbow: Socialization and Development of Communication Skills", 2021. More than 200 people took part in the project. Among them are students of the PSTGU pedagogical direction, children with disabilities and their parents. The work on the project included the stages of forecasting, modeling, design, actual design, testing and reflection. The final product of the project was an interactive digital content developed by students on the Microsoft Sway platform. The article analyzes the didactic, developing, diagnostic, educational possibilities of the project method. The importance of project activities for the development of professional competence of a teacher is shown. The results of a survey of students about the advantages and difficulties of project activities at the university are presented. The results of the analysis of the successful experience of project activities show its potential in the development of a future teacher with a conscious pedagogical position and readiness for active and creative activity. A conclusion is formulated about the resource of the project method, which contributes to building a constructive path for the development of a person in the profession.
[Characteristics and Source Analysis of Heavy Metals in Farmland Soil on the South of Dongting Lake].

Based on the grid distribution method, 1589 topsoil samples were collected from farmland on the south side of Dongting Lake from April to August 2020. The content, potential ecological risk, spatial distribution characteristics, and source analysis of heavy metals in the soil were studied. The concentrations and pH of As, Cd, Pb, Cu, Zn, Ni, Cr, and Hg in the soil were determined using ICP-MS, ICP-OES, HG-AFS, and ISE methods. The results showed that the average concentrations of Zn, Cr, Pb, Cu, Ni, As, Cd, and Hg were 118.18, 82.21, 52.1, 33.76, 32.81, 18.25, 0.42, and 0.13 mg·kg-1, respectively. All heavy metals had medium or high variability. The soil was mainly weakly acidic with a pH between 3.96 and 7.90. Hg and Cd had high ecological risks. The spatial distribution of heavy metals showed a tendency of being high in the southwest and low in the northeast. The PMF and PCA methods were used to analyze the sources and calculate the contribution rates of the eight types of heavy metals. The PMF results showed that the contribution rates of soil heavy metals in the study area were as follows:agricultural activity sources (36.98%), natural sources (32.94%), water and soil exchange sources (17.05%), and atmospheric dry and wet deposition sources (13.03%). The PCA results showed that the soil heavy metals in the study area mainly came from agricultural activity sources (68.04%), natural sources (31.02%), and atmospheric deposition sources (0.94%). The conclusions of the two source analysis methods tended to be consistent, that is, agricultural activities were the main source of soil heavy metals in the study area. The vertical profile analysis showed that the contents of Cd, Hg, and Pb elements were high in the surface layer and low in the deep layer, and the phenomenon of anthropogenic input was obvious. These results can provide a scientific basis for the formulation of prevention and control countermeasures of farmland soil heavy metal pollution around Dongting Lake.
Preliminary Study on Shadow Detection in Drone-Acquired Images with U-NET

This study shows a preliminary investigation of shadow detection in drone-acquired images using a deep learning method with minimal labelled shadow images.The aim is to discuss how the selected U-Net architecture performs in a small-sized dataset consisting of various types of shadow brightness and objects.Two types of data augmentation methods, which are shadow variant and geometric transformation are implemented, aiming to improve the segmentation accuracy.Several experimental procedures are performed to observe the model performance.The study shows that adding images for training increases the accuracy of shadow detection in drone images from 0.95 to 0.96, and geometric transformation data augmentation method increases the accuracy from 0.961 to 0.963, while the shadow variant method increases the flexibility of detection.
Research on the development of equipment operation skills based on a statistical analysis of data

Through an in-depth understanding of the training situation of the basic units, the new tasks, new demands and new changes in the training of the basic units were analysed.In the light of the above new situations, this paper identifies the main contradictions and problems in the teaching of the course on the use of equipment, and then puts forward suggestions to further strengthen the teaching and training of equipment operation skills, in order to achieve the purpose of promoting the quality of the teaching of the course as well as the personnel training.
Empathy in Virtual Organization: Lesson Learned from Indonesia Covid-19 Pandemic

The crisis of oxygen availability that occurred during the COVID-19 pandemic created a sense of empathy for a group of communities and the general public who are interconnected with technological sophistication.The development of technology that is growing [1] and without limits, brings every individual to be able to carry out various activities and interactions without having to carry out face-to-face or what is called virtual.The purpose of this study is to find out more about the ways and purposes of virtual organizations via Telegram after the COVID-19 pandemic.This research was conducted using a qualitative method using a netnographic approach, observation and documentation data collection techniques in the Telegram group with 2,216 active members and will be analyzed in depth through data reduction, display data and drawing conclusions.The results of the study stated that empathy can appear through a virtual telegram organ called the "Jogja Oxygen Community" group.This is evidenced by several forms of empathy, including: firstly, providing information about oxygen cylinders in the Yogyakarta area, secondly, offering assistance and mutual support among members.The group, even though it is virtual, the three provide solutions to each other if there are difficulties related to oxygen cylinder information, finally reminding and taking care of each other if something is not in accordance with the group's provisions.
Implementation of Podcasts as Japanese Language Learning Media

The condition of Indonesia, which has begun to be hit by the COVID-19 pandemic, requires that all face-to-face teaching and learning activities be temporarily suspended and transferred to Distance Learning (PJJ).Teachers must have innovations in the learning media that will be used so that students can easily understand the learning material.In implementing distance learning activities using electronic media, such as mobile phones or laptops.There are various learning media in the form of applications that can be utilized in the two electronic media, one of which is podcasts.In this study, the researcher wanted to find out how the process and responses of the respondents to the use of podcasts as learning media for the Japanese language were.The research method used in this research is descriptive with a qualitative approach.The research sample used was 25 students of class X Clinical Pharmacy & Community Health Vocational School of Sadewa for the Academic Year of 2021/2022.Data collection techniques are used in the form of observation, filling out questionnaires, and interviews.Based on the results of the research that has been done, it can be seen that most students think that using podcasts as a learning medium makes it easier for them to understand the learning material.In addition, the teacher's response to the use of podcasts in teaching and learning activities is that they can be used as an alternative learning media, especially in distance learning.This explains that podcasts are excellent and appropriate for learning media.
Particle Swarm–Grey Wolf Cooperation Algorithm Based on Microservice Container Scheduling Problem

In recent years, microservices have been very widely used as a new application development technology in edge computing, IoT, and cloud computing.Application containerization technology is one of its core technologies, which allows multiple containers to be deployed within the same physical node.Then a single physical node could provide different services to user.How to rationally deploy containers on a cluster of physical nodes is one of the main research directions nowadays.Although a number of researchers have modeled the microservice container scheduling problem and proposed effective solutions, there are still shortcomings, for example, the slow speed of finding the optimal solution and the tendency of the algorithm to fall into local optimality.This paper propose a Particle Swarm -Grey Wolf Cooperation Algorithm based on Microservice Container Scheduling Problem (PS-GWCA) by using particle swarm optimization algorithm (PSO) and grey wolf algorithm (GWO) in a multi-core parallel way, which enables the two algorithms to complement each other in the whole search process through the information exchange between populations.In the early of the search stage, the GWO can use its global search capability to guide the PSO to jump out of the local optimum to avoid premature convergence, and in the late of the search stage, the PSO can enhance the search capability of the GWO on the pareto optimal frontier.The experimental results show that compared with the other three algorithms, the algorithm optimizes 18.07% in network transmission cost, 14.67% in local load balancing, 20.66% in global load balancing, and 7.5% in search speed, and 5.69% in service reliability.
A comparative study to determine the photovoltaic annual energy generation in the Brazilian Northeast

Renewable energy sources have been growing worldwide and solar energy is a significant part of such sources. An essential step in planning studies, including the problem of optimal location and sizing of photovoltaic (PV) generators, is estimating how much energy the panels will generate over time. For that, two aspects must be considered: the stochastic nature of the input variables and the way to calculate the generator's output power. A brief literature review identified 12 approaches to determine the PV output power. Therefore, this article compares such methods through actual meteorological data and generated energy over 1 year by a solar power plant located in a specific site in the Brazilian Northeast. An analysis is carried out on the influence of correlations between meteorological variables in estimating the generated energy. There is no comparative work in the literature on the analytical models for calculating the PV output power in conjunction with the correlation analysis. As expected, models that consider the influence of ambient temperature on output power performed better than those that do not. How the energy generated by the PV panel is estimated can influence the economic viability of a project since oversizing the PV system entails unnecessary additional costs. Monte Carlo simulations generate data samples used by the studied models for power output calculation. According to our findings, considering the correlation between meteorological variables reduces the error in estimating the generated energy by PV panels.
Classification of Acute Leukaemia based on Blast Morphology, Cytochemistry and Flow Cytometry

Introduction: Acute leukaemias are a heterogeneous group of malignancies due to the abnormal proliferation of immature cells. The classification of acute leukaemia has been transformed over a period. French American British’s (FAB) classification of acute leukaemia is based on morphology and cytochemistry. Later, the World Health Organization (WHO) classification stressed immunophenotyping to be done in the classification of acute leukaemia. Immunophenotyping is a powerful tool for classification and also for treatment and prognosis. Aim: To study the morphology of blasts in the peripheral smear and bone marrow cytochemistry and to compare with immunophenotyping in the classification of acute leukaemia. Materials and Methods: A cross-sectional study was undertaken in the Department of Transfusion Medicine and Immunohematology, at St. Johns National Academy of Health Sciences a Tertiary Care Hospital in Bengaluru over a period of one year from November 2018 to October 2019. The morphology of the blast was studied in peripheral blood and bone marrow aspirate stained by Leishman stain. Cytochemistry in peripheral smear and bone marrow with Sudan Black B and Periodic Acid-Schiff (PAS) stain was done in all acute leukaemia. The flow cytometry samples were processed within 24 hours of collection of the samples with a panel of markers including myeloid and lymphoid lineage and precursor markers. The results of morphology were confirmed with flow cytometry and final reports were released. Results: A total of 112 cases of acute leukaemia were studied and classified based on morphology, cytochemistry, and immunophenotyping of blasts. A total of 46 cases of Acute Myeloid Leukaemia (AML) and 63 cases of Acute Lymphoblastic Leukaemia (ALL). Two cases were found with Chronic Myeloid Leukaemia (CML) in blast crisis, one with myeloid and another with lymphoid blast crisis. One biphenotypic leukaemia was noted. Sudan Black was positive in 35 (76%) of AML and PAS was positive in 28 (44%) of ALL. The most common AML subtype was AML-M2 and the most common ALL subtype was B cell ALL. The most common symptom was fever and the sign was pallor. Conclusion: The present study showed that morphological diagnosis is important, based on which the cytochemistry and flow cytometry is performed for diagnosis. In cases of the diagnostic dilemma of acute leukaemia with morphology and cytochemistry, immunophenotyping supports diagnosing and classifying acute leukaemia.
Students’ Perception of Higher Institutional Support Towards Online Learning Satisfaction: The Mediating Effect of Self-regulated Learning

The emergence of the COVID-19 pandemic has altered the global educational process.Many higher education institutions (HEIs) have quickly shifted the learning mode from physical classrooms to online platforms.Undoubtedly, self-regulated learning (SRL) is the key element that influences student satisfaction in online learning, yet students frequently struggle with the notion of online learning.Thus, it is crucial for learning institutions to facilitate and support their SRL skills.This study aims to examine the influences of perceived institutional support on students' online learning satisfaction.SRL is served a mediating role.The stimulus-organism-response (S-O-R) model was adopted to support this study.A total of 420 students from private HEIs have voluntarily taken part in the online survey.By using structural equation modelling (SEM) together with bootstrapping, the results proved that students who perceive greater institutional support reported higher online learning satisfaction.Further, institutional support does affect students' SRL, which in turn affects the level of online learning satisfaction.This implies that it is important for students to integrate SRL into online learning environments to improve their satisfaction.The study is significant for educational institutions to improve the platform design, pedagogical practice, and online course design in the future.
Selection System of Madura Tourism Object as The Best Recommendation Based on Comparative Analysis of K-NN and Naive Bayes Methods

The current decision-making system is developing very rapidly. Various methods can be used to assist recommendations in the decision-making process. The technique chosen in decision-making is sometimes irrelevant and less precise. In addition, differences in classification methods often result in different results in the recommendation process. Therefore, to determine the decision-making, the decision support system is one of the tools in solving this problem. This study proposes an approach to help make decisions as the best recommendations in choosing tourist attractions in Madura by comparing the classification methods, namely the K-Nearest Neighbor (K-NN) and Naive Bayes (NB) methods. For data validation, k-fold cross-validation is used. The NB method was chosen because it can produce maximum accuracy with little training data. In comparison, the K-NN method was selected because it is robust against random data, and this method is the simplest. The performance of the two ways will be compared to determine which method is better in the selection process. This research aims to help tourists at home and abroad choose suitable tourist attractions according to the tourists’ needs. This study uses 21 tourist destinations. The parameters used for the tourist attraction selection process consist of 11 indicators, including gender, age, occupation, education, marital status, tourism type, management services, facilities, education, ticket prices, and sales trends. From 320 datasets with several trials, it produces the highest accuracy value in the K-NN method with the 1st fold (K1), which is 77.81%, while the NB method has the highest accuracy value in the 2nd fold (K2) is 53.75%.
Parallel Implementation of KNN Algorithm for Dry Beans Dataset

In recent years, the interest in using machine learning to solve complex problems in different sectors using parallel algorithms has increased. The KNN algorithm is the most popular method to classify the dataset. The main problem in implementing this algorithm is how to quickly find K nearest neighbors. Accordingly, this research aims to study the effect of the parallel execution of the KNN algorithm by using different metrics such as speedup and efficiency. The performance of serial and parallel KNN algorithms was compared using different values of K and a different number of processers. The results show that The Message Passing Interface (MPI4py) improves performance by increasing speedup and decreasing execution time. Also, the impact of serial execution on the size of the dataset is discussed. It was found that as the size of the dataset increases, the serial time increases. Meanwhile, the use of seven processers in parallel execution with the KNN algorithm needs less time. In addition, as the number of processers increases, the speedup increases, and the efficiency decreases
A SLIC based camouflage region selection method for mobile patrol robots

Patrol robots are often in dangerous situations and may be damaged by criminals. To deal with this problem, a method to protect the robot by camouflage is proposed. It divides scene regions based on color, texture, and spatial features to search for suitable hiding regions. SLIC is employed to pre-divide scene images, in which three different image features are used to describe superpixels. Furthermore, superpixels with similar characteristics are classified as a same category. It can get a region by merging a kind of superpixels. Several scenes are applied to test the proposed method. The experimental result shows that the proposed method can realize camouflage region selection for patrol robots to hide.
Research on Personalized Learning Mode Based on Network Learning Space

In order to improve the efficiency of teaching and learning, in view of the disadvantages of low learning efficiency of learners under the traditional teaching mode, it has become an inevitable trend to explore a new personalized teaching mode based on network learning space. In this paper, we take respecting students' personality development as the starting point, build personalized teaching mode, design personalized learning environment, provide students with personalized learning resources, personalized learning support, personalized learning services and personalized learning evaluation, help students learn more effectively, and promote students' personalized development. The results show that under the personalized learning mode, students' learning effect, learning attitude and learning enthusiasm have been significantly improved.
Fast secret key-sharing method based on channel information and chaos randomization

Recently, physical layer security has attracted increased attention. In this context, researchers have proposed secret key-sharing methods using time-varying channel state information (CSI). However, CSI is time-correlated; thus, the randomness of the key deteriorates when the key generation time is shortened. Accordingly, we propose a 256-bit secret key-sharing method that does not degrade the key randomness even when the generation time is shortened. The method is based on exploiting chaos signals exhibiting complex and unpredictable behaviors. Through numerical simulations, we demonstrate that the proposed method passes the randomness evaluation test provided by the National Institute of Standards and Technology in a short time.
Information system for extraction of information from open web resources

The purpose of the work is to develop a project of an information and reference system for finding answers to questions based on the highest degree of comparison using text content from open English- language web resources. Examples of such questions can be: “What is the best book ever?”, “What is the most popular IDE for Python”. The result of the functioning of the information and reference system is a ranked list of answers based on the frequency of appearance of each of the answer options. Also, a numerical characteristic of the probability of the preference of a particular answer over others is added to each element of the list. Based on this metric, the obtained results are ranked. This information and reference system works with questions to which there is no unequivocal answer, what differs it from classic information systems for finding answers to questions of the QA-system type. The latter have a hypothesis that there is only one true answer to the question, often such systems work with well-known facts. Examples of questions they answer can be, for example, the date of birth of a famous person, or the population of a certain country. Instead, the proposed information and reference system answers subjective questions, for example, “What is the best book in the fantasy genre?” or “What is the best programming language?”. The system is based on the popularity of one or another answer. Proper names based on the analysis of N-grams are also keywords for forming the answer to the question.