Reviewerly/exHarmony huggingface dataset:
MODELING THE LEVEL OF DISSATISFACTION OF THE NEEDS OF RESIDENTS OF SMALL TOWNS IN THE DECISION SUPPORT SYSTEM FOR WATER SUPPLY IN EXTREME CASES

The problem of ensuring water supply to small settlements in extreme cases is considered. It is stated that the authors previously formulated and solved the problem of creating a decision support system, which allows to calculate the optimal route of movement of a water tank with data on the number of residents in each district and the distances between districts. Using a number of parameters (average service time, tank volume, average percentage of the population going out for water, restrictions on the volume of water dispensed, etc.), the system allows you to make a schedule (schedule) of the movement of the tank, as well as give recommendations on increasing the number of these tanks and their optimal distribution by districts, to determine the places of their best location in each district for the maximum satisfaction of all its residents. Currently, a mathematical model has been developed for calculating a special coefficient that allows to assess the level of dissatisfaction of residents' needs in the provision of drinking water. The proposed coefficient contains three components, namely, the ratio of the volume of the recommended dispensing of the maximum possible number of liters per person to the calculated one; assignment of real "time on the road" to optimally calculated; the average relative distance from the calculated center of the district. The created model is added as an additional module to the existing decision-making support system, examples of the system's operation during route calculation and the position of tanks supplying the city of Toretsk, Donetsk region are given. The examples show that the reason for the biggest dissatisfaction is the insufficient number of cars, in second place is their not very good location; available time has almost no effect on the result. Increasing the number of machines to two reduces the first component of the coefficient to unity. The results indicate the possibility of reducing the indicator from 1.305 to 1.087.
Evaluation of Reading Difficulty Based on Factor Analysis and Linear Regression

This paper conducts research on how to measure the difficulty of English texts and how to extend it to other languages, such as Chinese. Firstly, text features that can represent reading difficulty are extracted and represented using parameters. Secondly, the extracted indicators are subjected to KMO and Bartlett's tests, and the results show that these indicators are suitable for factor analysis. Then, a comprehensive evaluation model of language difficulty based on factor analysis is constructed, and the difficulty coefficient composite score F of 16 English reading texts is calculated. Additionally, a linear regression equation is constructed to separately measure the correlation between Type-Token Ratio (TTR) and the text difficulty coefficient F. Finally, the conclusions obtained from the model are compared with the officially published difficulty coefficients of English reading in the postgraduate entrance examination, validating the rationality of the results. Furthermore, the above constructed multi-indicator comprehensive evaluation model is extended to other languages such as Chinese, confirming the rationality and universality of the model, effectively solving the problem of "measuring text reading difficulty.".
Use and impact of Kahoot in logistics education

Nowadays, the younger university students are increasingly using digital solutions, therefore teachers should turn to these solutions as well. One of the tools for this is to use an online platform instead of paper-based solutions for short end-of-class assessments. There are several such available software on the market, and this paper presents the use and results of Kahoot through two case studies. The details of the case studies also show the impact in logistics education.
Research on Traffic Sign Recognition Method Based on Generating Countermeasures Network

Nowadays, intelligent assisted driving technology is developing rapidly. In the field of intelligent assisted driving technology, traffic sign recognition is a crucial area which focuses on identifying input images and outputting corresponding traffic sign types in the images. However, due to the limitations of optical hardware in the image acquisition system or the poor environment in which the image acquisition system operates, the collected images may not be well recognized by classifiers used for traffic sign classification, despite containing traffic sign information. This could be due to low resolution of images, unclear images, and excessive noise. This article presents a modified generative adversarial network for reconstructing high-resolution traffic signs, specifically suited for the reconstruction of traffic sign images. The generated images and interpolated images are then sent into a convolutional neural network, which is a classifier, to complete the task of classifying traffic signs. The accuracy of the two sets of data is compared, and it is concluded that the classification accuracy of images reconstructed by the generative adversarial network is generally 15% higher than that of images processed by the interpolation method. This study not only proves that the low resolution of images containing traffic sign information can negatively affect subsequent traffic sign classification tasks, but also demonstrates that traffic sign recognition methods based on generating adversarial networks can indeed improve classification accuracy.
Research and Application Analysis of Correlative Optimization Algorithms for GAN

Generative Adversarial Networks (GANs) have been one of the most successful deep learning architectures in recent years, providing a powerful way to model high-dimensional data such as images, audio, and text data. GANs use two neural networks, generator and discriminator, to generate samples that resemble real data. The generator tries to create realistic looking samples while the discriminator tries to differentiate the generated samples from real ones. Through this adversarial training process, the generator learns to produce high-quality samples indistinguishable from the real ones.Different optimization algorithms have been utilized in GAN research, including different types of loss functions and regularization techniques, to improve the performance of GANs. Some of the most significant recent developments in GANs include M-DCGAN, which stands for multi-scale deep convolutional generative adversarial network, designed for image dataset augmentation; StackGAN, which is a text-to-image generation technique designed to produce high-resolution images with fine details and BigGAN, a scaled-up version of GAN that has shown improved performance in generating high-fidelity images.Moreover, the potential applications of GANs are vast and cross-disciplinary. They have been applied in various fields such as image and video synthesis, data augmentation, image translation, and style transfer. GANs also show promise in extending their use to healthcare, finance, and creative art fields. Despite their significant advancements and promising applications, GANs face several challenges such as mode collapse, vanishing gradients, and instability, which need to be addressed to achieve better performance and broader applicability.In conclusion, this review gives insights into the current state-of-the-art in GAN research, discussing its core ideas, structure, optimization techniques, applications, and challenges faced. This knowledge aims to help researchers and practitioners alike to understand the current GAN models' strengths and weaknesses and guide future GAN developments. As GANs continue to evolve, they have the potential to transform the way we understand and generate complex datasets across various fields.
Efficient Application of the Factorized form of the Unitary Coupled-Cluster Ansatz for the Variational Quantum Eigensolver Algorithm by Using Linear Combination of Unitaries

The variational quantum eigensolver is one of the most promising algorithms for near-term quantum computers. It has the potential to solve quantum chemistry problems involving strongly correlated electrons with relatively low-depth circuits, which are otherwise difficult to solve on classical computers. The variational eigenstate is constructed from a number of factorized unitary coupled-cluster terms applied onto an initial (single-reference) state. Current algorithms for applying one of these operators to a quantum state require a number of operations that scale exponentially with the rank of the operator. We exploit a hidden SU(2) symmetry to allow us to employ the linear combination of unitaries approach, Our Prepare subroutine uses n+2 ancilla qubits for a rank-n operator. Our Select(U^) scheme uses O(n)Cnot gates. This results in a full algorithm that scales like the cube of the rank of the operator n3, a significant reduction in complexity for rank five or higher operators. This approach, when combined with other algorithms for lower-rank operators (when compared to the standard implementation), will make the factorized form of the unitary coupled-cluster approach much more efficient to implement on all types of quantum computers.
Fair Max–Min Diversity Maximization in Streaming and Sliding-Window Models

Diversity maximization is a fundamental problem with broad applications in data summarization, web search, and recommender systems. Given a set X of n elements, the problem asks for a subset S of k≪n elements with maximum diversity, as quantified by the dissimilarities among the elements in S. In this paper, we study diversity maximization with fairness constraints in streaming and sliding-window models. Specifically, we focus on the max–min diversity maximization problem, which selects a subset S that maximizes the minimum distance (dissimilarity) between any pair of distinct elements within it. Assuming that the set X is partitioned into m disjoint groups by a specific sensitive attribute, e.g., sex or race, ensuring fairness requires that the selected subset S contains ki elements from each group i∈[m]. Although diversity maximization has been extensively studied, existing algorithms for fair max–min diversity maximization are inefficient for data streams. To address the problem, we first design efficient approximation algorithms for this problem in the (insert-only) streaming model, where data arrive one element at a time, and a solution should be computed based on the elements observed in one pass. Furthermore, we propose approximation algorithms for this problem in the sliding-window model, where only the latest w elements in the stream are considered for computation to capture the recency of the data. Experimental results on real-world and synthetic datasets show that our algorithms provide solutions of comparable quality to the state-of-the-art offline algorithms while running several orders of magnitude faster in the streaming and sliding-window settings.
Toward Establishing Criteria for Evaluating Paragraph Writing of Pre-Intermediate Learners of English

Japanese students’ lack of English writing skills has been a long-standing problem. It not only originates from students’ lack of grammatical knowledge and vocabulary, but also from the availability of fewer opportunities to write essays in English. Further, the current standards for evaluating the essays written by Japanese beginner-level learners in English are not established sufficiently, resulting in a heavy grading burden on the teachers. This study establishes an evaluation standard to assess the English essays written by Japanese beginner-level English learners. An “analytic scoring” is conducted for each essay, involving criteria ranging from level 1 to 4, while another group of evaluators perform a “holistic scoring”, judging the quality based on their overall impression, on a scale from 1 to 4. The reliability and validity of the “analytic scoring” are established through the correlation analysis of both evaluations. Text mining is used to analyze the explanation of reasons provided by the holistic scoring evaluators to the highest-rated essays and identify the factors determining the quality of English writing.
Logical qubit behavior model and fast simulation for surface code

Abstract This study proposes a logical qubit behavior model (LQBM) that calculates the state of the logical qubit based on a surface code after a logical qubit operation. LQBM can simulate the surface code whose distance exceeds 5, which is impossible with conventional quantum simulators. A complex error syndrome measurement must not be executed in this model. LQBM works at the mixed level of a logical and physical qubit. Probability amplitudes and the number of state vectors are designed at the physical qubit level. The state vectors of physical qubits constituting a logical qubit are abstracted to the value of the logical qubit level. LQBM converts all logical qubit operations, which include an initialization, a state injection, universal gates, and lattice surgery operations (e.g., a merge and a split) to simple calculations. LQBM provides a better computational complexity of O( log ( distance )) than O(distance $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msup> </mml:math> ). Through simulation experiments, it was found that LQBM performs logical qubit operations up to 24 million times faster than existing quantum simulators in the case of distance = 5.
Analysis of Indoor Path Planning Techniques for Wheeled Mobile Robots

Path planning is crucial for autonomous mobile robots to navigate in unknown settings. Achieving autonomous navigation in indoor environments without collisions with static or dynamic obstacles, reaching the destination quickly, and meeting the requirements of specific work scenarios are essential challenges. Over the years, various path planning algorithms have been proposed in the literature, each with advantages and limitations. This paper provides a brief overview of advanced indoor path planning algorithms for wheeled mobile robots, listing classical algorithms as well as recent advancements. These algorithms' fundamental principles, features, and computational complexities are discussed and analyzed. Furthermore, this paper highlights some challenges and open research questions in path planning. This survey aims to provide readers with a concise review of existing indoor wheeled mobile robot path planning techniques and their applications, to inspire future research in this vital field of robotics.
TECHNOLOGY USE DURING COVID-19 PANDEMIC AMONG STUDENTS OF OF SPECIAL EDUCATION

Due to COVID-19 pandemic faculties were closed and online educational platforms were used, which caused the process of transferring from conventional learning to online learning at majority of higher educational institutions across the world. The aim of this research was to examine types of technology students of Faculty for special education and rehabilitation own, types of technology their faculty owns, self-perceived level of proficiency in using certain programs, students’ satisfaction with certain services or spaces provided to them by their faculty, students’ belief in usefulness of certain aspects of technology use at their faculty, perception of usefulness of particular technologies at their faculty, as well as their opinions about technology use during lectures. The sample included 156 students and the survey distributed to respondents was a survey designed by Honeychurch &amp; McCluckie (2007).Almost all respondents own smartphone (97.4%) and a computer, while only 26.3% of the sample owns tablet device. The programs that students believe their level of proficiency in using is excellent are internet (53.2%) and email (41%). The students are not satisfied with faculty spaces and services. Majority of the sample believes that usingtechnology can help them in all aspects of their studies and that technology is the most useful in making them feel more connected to their peers.We recommend replicating this research again now, since the study was conducted when the COVID-19 pandemic started. We believe it is important for students, as well as teaching staff to be properly trained on how to use technology. Key words: technology, faculty, satisfaction, students.
Selection Criteria and Operation Direction of Engineering Project Delivery Method Using AHP

Purpose The purpose of this study is to suggest an improvement plan for the engineering PDM (Project Delivery Method) by exploring elements that should be considered important in the selection criteria for the public engineering projects and analyzing the expert perception of the PDM operation direction. Methods After reviewing 10 precedent studies on PDM and public engineering project characteristics, selection factors of PDM were extracted and stratified. A survey was conducted targeting 130 engineering experts, and valid responses verified by CR (Consistency Ratio) were analyzed using the AHP method in order to calculate the weight of each PDM selection element and the expansion need for each PDM type. Results ① Importance of PDM selection criteria in main areas was in the order of project characteristics > project·policy goals > industrial·institutional environment > owner characteristics. ② In the importance of detailed elements, project difficulty, government policy goals, budget reduction, and life cycle cost minimization were high. ③ As for the expansion need of PDM type, design-bid-build was much higher than that of technical bid, and qualification assessment method was the highest in detailed PDM types. Conclusion In engineering PDM selection criteria, project characteristics and industry environment should be considered more important than project size. It is also necessary to reflect the project difficulty and social responsibility in the selection criteria of comprehensive evaluation method currently being debated.
A Study on the Directions of Policing for Autonomous Police in the Post-COVID Era : Focusing on the Chungcheongnam-do

In 2020, the COVID-19 pandemic spread, and strong quarantine policies were implemented, which restricted social activities. As the lifestyle changed to a non-face-to-face-centered lifestyle, the crime pattern also changed. Crimes that could have occurred in daily life decreased, but non-face-to-face crimes and crimes committed at home showed an increasing trend. In the pandemic situation, not only the crime but also the police had a big change. In 2021, the autonomous police system was introduced and implemented, which led to various changes in the police's office area and activities. Since then, COVID-19 has changed to an Endemic of infectious diseases, and society is recovering from daily activities and the security environment is changing. In line with this situation, the police should develop policies and activities that reflect the changes in security activities.&#x0D; In this study, we analyze the changes in the public safety environment, focusing on Chungcheongnam-do, in the context of the COVID-19 pandemic and its transition to an endemic phase. Based on previous studies, we present the current issues within the police force and provide directions for public safety activities. Firstly, we propose strengthened responses and preventive measures against changes in crime and safety incidents resulting from the recovery of daily activities. Secondly, considering the continued economic downturn, we suggest preventive and responsive actions against financial and property crimes. Thirdly, we propose the formation of a security governance system, which includes welfare-oriented public safety activities tailored to specific crime types and targets.
Predictive Model of the ENSO Phenomenon Based on Regression Trees

In this work, the supervised machine learning technique was applied to develop a predictive model of the phase of the El Niño-Southern Oscillation (ENSO) phenomenon. Regression trees were specifically used by means of the Scikit-Learn library of the Python programming language. Data from the period 1950-2022 were used as training and test. The performance of the predictive model was validated using three continuous type error measurement metrics: Mean Absolute Error, Maximum Error and Root Mean Square Root. The results indicate that with a greater number of training data the model improves its performance, with a tendency to decrease the error in forecasts. Which starts for the year 1953 with errors of 0.77, 1.41 and 0.75 for MAE, ME and RMSE respectively, ending for the year 2022 with errors of 0.28, 0.72 and 0.13 for the same metrics. It is concluded that, based on the results, the developed model is consistent and reliable for ENSO phase forecasts in a 12-month window.
Analysis on the Listening Comprehension Parts of National Standardized University Entrance Exams of Three Asian Countries

Importance of Listening comprehensionIn order to communicate appropriately, people should develop all four language skills, reading, writing, speaking and listening harmoniously.However, listening is known to be the most frequently used language skill (Field, 2009).For example, in people's life, listening takes place four times more than reading, five times more than writing, two times more than speaking (Morley, 2001).However, listening has received less attention in L2 teaching (Graham, 2017) with inappropriate thoughts about how L2 listening can develop the skill itself (Brown, 1987).However, these days, L2 listening has become an important topic of teaching L2 and L2 instructors argue that explicit and direct L2 listening instruction is required in order to help L2 learners to be proficient L2 listeners (Emerick, 2019).
THE RESEARCH OF PSYCHOLOGICAL FACTORS OF THE DEVELOPMENT OF LEADERSHIP QUALITIES OF FUTURE TEACHERS

The article is dedicated to the study of psychological factors in the development of leadership qualities in future teachers during their professional study. It highlights that a significant number of works by contemporary domestic and foreign psychologists are devoted to the issue of teacher's leadership qualities. Researchers define the phenomenon of leadership qualities as a relevant topic that generates considerable scientific interest. The article describes an author's approach to understanding the holistic structure of leadership qualities and the importance of its formation and development during the professional training of teachers, particularly in the period of educational reforms (such as the concept of the New Ukrainian School). A complex of psychodiagnostic tools was proposed, allowing for a comprehensive and thorough examination of the personality of future teachers from the perspective of leadership. The study involved students from the II to IV year, from three higher education institutions, aged 18 to 21. Based on the analysis of the components of pedagogical activity, taking into account the structural and content components of leadership qualities in future teachers, the following psychodiagnostic methods were identified: "Self-Actualization Test" by Yu. Alyoshina, L. Gozman, "Method for determining the level of professional orientation of students" by T. Dubovitska, "Assessment of Communicative and Organizational Inclinations - KOS" by V. Sinyavsky and B. Fedorishin, "Diagnostic of Leadership Abilities" by Ye. Zharikov, Ye. Krushelnytsky, "Hall's Test for Determining the Level of Emotional Intelligence," "Behavioral Self-Regulation Style" by V. Morosanova, "Method for studying the level of subjective control (RSC)" by J. Rotter, and the "Questionnaire on reflexivity" by A. Karpova. The empirical study allowed for determining the level of development of leadership qualities in the selected sample. The analysis of the empirical research results revealed that certain indicators are underdeveloped and require improvement. This necessitates further research into the psychological factors that contribute to the development of the aforementioned qualities.