Reviewerly/exHarmony huggingface dataset:
Infinite discrete chains and the maximal number of countable models

The paper is aimed at studying the countable spectrum of small linearly ordered theories.The objectives of the research are to study the structural properties of countable linearly ordered theories, as well as to promote the solution to the well-known open problem of model theory, Vaught's conjecture, which assumes that the number of countable models of a countable complete first-order theory cannot be equal to ℵ 1 .An important step in solving Vaught's conjecture is the search for conditions under which the theory has the maximal number of countable pairwise non-isomorphic models.By limiting ourselves to linearly ordered theories we do not get special advantages from the viewpoint of studying their countable spectrum.Therefore, in the article, a restriction on 1-types and 1-formulas of the theory is introduced.It is proved that a small countable linearly ordered theory that satisfies the restriction and has an infinite discrete chain has the maximal number of countable non-isomorphic models.To build models, the authors use the method of constructing countable models over countable sets, based on the Tarski-Vaught criterion.It is shown that it is possible to carry out the construction in such a way that the types of unnecessary elements in the resulting model are omitted, what guarantees non-isomorphism of the models and their maximal number.
Balanced, Unbalances, and One-Sided Distributed Teams - An Empirical View on Global Software Engineering Education

Global software engineering education faces unique challenges to reflect as close as possible real-world distributed team development in various forms. The complex nature of planning, collaborating, and upholding partnerships present administrative difficulties on top of budgetary constrains. These lead to limited opportunities for students to gain international experiences and for researchers to propagate educational and practical insights. This paper presents an empirical view on three different course structures conducted by the same research and educational team over a four-year time span. The courses were managed in Japan and Germany, facing cultural challenges, time-zone differences, language barriers, heterogeneous and homogeneous team structures, amongst others. Three semesters were carried out before and one during the Covid-19 pandemic. Implications for a recent focus on online education for software engineering education and future directions are discussed. As administrational and institutional differences typically do not guarantee the same number of students on all sides, distributed teams can be 1. balanced, where the number of students on one side is less than double the other, 2. unbalanced, where the number of students on one side is significantly larger than double the other, or 3. one-sided, where one side lacks students altogether. An approach for each of these three course structures is presented and discussed. Empirical analyses and reoccurring patterns in global software engineering education are reported. In the most recent three global software engineering classes, students were surveyed at the beginning and the end of the semester. The questionnaires ask students to rank how impactful they perceive factors related to global software development such as cultural aspects, team structure, language, and interaction. Results of the shift in mean perception are compared and discussed for each of the three team structures.
On-Line Signature Verification

In this paper, we are going to propose a new technique for On-Line signature based on global n local features. In general, shape of an on-line signature is used as a single discriminating feature. Sometimes shape of signature is used alone for verification purposes and sometimes it is used in combination with some other dynamic features such as velocity, pressure and entropy. In proposed system shape of signature is examined using Edge-Detection Algorithm (EDA), pressure points are calculated using Pressure Points Allocation using Clustering (PPAC). So the overall process can be thought as the process is signature examination based on shape and pressure points in combination with entropy and velocity and it performs verification on each partition separately. Finally, the signature is classified as genuine or a forgery.
A Fast and Efficient Algorithm for Outlier Detection Over Data Streams

Outlier detection over data streams is an important task in data mining.It has various applications such as fraud detection, public health, and computer network security.Many approaches have been proposed for outlier detection over data streams such as distance-,clustering-, density-, and learning-based approaches.In this paper, we are interested in the densitybased outlier detection over data streams.Specifically, we propose an improvement of DILOF, a recent density-based algorithm.We observed that the main disadvantage of DILOF is that its summarization method has many drawbacks such as it takes a lot of time and the algorithm accuracy is significant degradation.Our new algorithm is called DILOF C that utilizing an efficient summarization method.Our performance study shows that DILOF C outperforms DILOF in terms of total response time and outlier detection accuracy.
Improved Affinity Propagation Clustering Based on K-Nearest Neighbors and Canopy Algorithm

For large-scale data sets, the traditional AP clustering algorithm processes all data samples through the idea of iteration and three metrics calculation. This kind of processing will lead to poor clustering effect and high time consumption. Based on this problem, an algorithm which is called KCAP based on Affinity Propagation clustering and Canopy clustering algorithm was proposed. Firstly, KNN distance was used to calculate the local density for each data. Secondly, we used the Canopy algorithm to roughly cluster the data sets, and the center points were formed into a new data set $$Y$$ . Then, we applied affinity propagation clustering algorithm to cluster the data set $$Y$$ . Finally, we assigned the data to the nearest center. Compared with the traditional AP algorithm, K-means algorithm and FCM algorithm, the experimental results on synthetic data sets and UCI data sets show that algorithm KCAP not only improves the clustering accuracy, but also have higher efficiency.
Sirens

This chapter focuses on basics – what sirens are and how they work – as well as history. It presents practical information on the making of sirens as musical instruments. As an acoustical device, a siren takes what would otherwise be a continuous flow of air and converts it into a series of pulses. These pulses, occurring at some frequency within the hearing range, propagate out into the surrounding atmosphere as sound. In its simplest form, the heart of the siren is a rotating disk. The musical intervals available from a siren disk will be determined by the relationships between the numbers of holes in each ring. The numbers of holes will not determine the actual pitches, because pitch depends on the disk’s rotational speed. On a siren disk with multiple concentric rings of holes, the ratios of the pitches available will correspond to the ratios of the numbers of holes in each ring.
View of Consumerism of High School Students and Factors That Shaped Their Concept

Money management has always been an issue for teenagers these days.With the advancement of internet technology and pop culture, new ideas and activities such as popular brands, video games, and idol groups gained attraction from teenagers.While these events are entertaining and sociable, they are also costly.Famous brands could cost much more expensive compared to common brands but students view them as a way to become popular in school.Video games could cause addiction for kids and trick them into spending a huge amount of money on in-game purchases.Crazy fans are willing to buy multiple albums for their idols just to get a fan meeting ticket.Thus, for teenagers in the 21st Century, money management is a vital concept that should be taught in school and by parents to help them when they become economically independent in society.This paper is conducted based on current news, reports and the author's personal experience as a teenager.Most of the data and real events are from internet articles.The technology and internet basis of the main topics are new inventions in the 21st century.Therefore, the corresponding solutions to these problems have not yet been applied to the public.Optimizing current education programs focusing on money management should be prioritized.Guidance of moral views of consumerism throughout childhood is very crucial for their mental development. Keywords： High School Students, View of Consumerism, Overconsumption, Money Management, EducationWith the wide spreading of fashion and pop culture in the 21 century, clothing and commodities start to develop their own brand.Inviting popular celebrities to endorse their product, some of the brands attracted a great number of young people with unique styles.
Machine Learning Based Scholarship and Credit Pre-Assessment System

The researcher explained the implementation process of finding the scholarship for the students by using machine learning supervised learning algorithm i.e. Naïve Bayes algorithm. Addition to this it includes a small description of naïve bayes classifier which used to be used through the authors. It explains the significance of training facts set and trying out information set in Machine mastering techniques. Machine learning nowadays becomes plenty used technique in the field of IT industry. It is a very effective instrument and technique for many quite a number fields such as education, IT and even in enterprise industry. In this paper, the researcher attempt to find computerized end result reputation of scholarships of college students by way of using naïve bayes classifier algorithm primarily based on the scholar educational performance, conversation skills, greedy power, IHS, income, time management, regularity etc. A scholarship offers a strength and self assurance to a student. It also boosts the performance of students indirectly. Usually scholarships are furnished by governments or authorities organizations. It is very essential for students to recognize their personal potentiality early in their educational profession so that they faster its growth, receiving attention from an employer or corporation helps college students take this step. Students can apply for scholarships primarily based on the eligibility criteria (such as caste category, annual income, etc). The scholarship will be issued based on merit, student performance and career specific. Different schemes of scholarships are provided for the students based on distinct eligibility criteria. By the use of a naïve bayes classifier, the researcher acquired a end result with accuracy of 96.7% and error of 3.3%. The repute of scholarship students was once displayed in the form of yes or no.
Implementation and Evaluation of Accounting Information Systems in Manufacturing Company Using System Usability Scale

Information systems can facilitate business actors to control and evaluate business reporting effectively and efficiently. Anugrah Sri Jaya is a business engaged in manufacturing. This company still uses a manual recording system which causes several problems, namely frequent errors in calculations and mismatch of inventory stock records with actual conditions. Therefore, the researcher proposes to implement an information system in the accounting process at Anugrah Sri Jaya. From the results of the study, researchers have succeeded in building a web-based accounting information system at Anugrah Srijaya. Information system testing uses the System Usability Scale (SUS) method where the test uses a questionnaire as an assessor and the number of responses is 5 respondents where the respondents from this study are business owners and several employees who will later use this system. From the results of the tests carried out, it can be seen that the average score obtained from the calculation of the System Usability Scale (SUS) is 74.5. Based on the results of the average score, this system can be said to be in the category of acceptance (Acceptable) and on the adjective rating scale, the information system built is rated as excellent.&#x0D;
SISTEM REKOMENDASI TEMPAT WISATA MENGGUNAKAN ALGORITMA CHEAPEST INSERTION HEURISTIC DAN NAÏVE BAYES

Bali is one of the best tourism areas in Indonesia, as evidenced in 2016 Bali received a number of awards on the TripAdvisor Travelers Choice Award in global and Asian scope. However, the Corona virus outbreak from 2019, caused the tourism sector in Bali to decline, thus a solution is needed to restore the tourism sector in Bali, where one solution is to increase cultural tourism to the maximum, as the main attraction of tourist destinations in Bali. Bali. So the author proposes a tourism recommendation system, which aims to recommend tourist attractions that are suitable for tourists, which in this recommendation system is also recommended cultural tourism destinations that are directly recommended by the community, and there is also a mapping of tourist attractions as part of a tourist recommendation system, mapping of tourist attractions public and cultural attractions. In this tourism recommendation system, using the Naïve Bayes Algorithm to recommend general tourist destinations based on the personal motivation of tourists, which is based on the attributes of age, gender, natural interest, artificial interest, cultural interest of tourists, using 200 training data consisting of 14 classes of tourist attractions. . In addition, this tourist recommendation system is equipped with recommendations for routing tourist attractions using the Cheapest Insertion Heuristic Algorithm, to arrange a list of tourist attractions.&#x0D; Keywords: Recommendation System, Naïve Bayes Algorithm, Cheapest Insertion Heuristic Algorithm, Personal Motivation, Place Mapping.
The Potential of Hyperbaric Oxygen Therapy Against Codeine Addiction Reduction

Excessive opioid usage such as codeine can lead to anxiety, dose dependency, and addiction.Furthermore, drug addiction induce intoxication and in some case death.Several approach can be done to treat codein abuse.Hyberbaric oxygen is one of the therapy choices, considered that it can be used as main and supporting management.Related studies said increasing level of Nitric Oxide (NO) and Dopaminergic, main substances in drug addiction, can be inhibited by hyperbaric oxygen therapy thus it has the potential value to reduce narcotic addiction such as codeine.However, the review papers of previous research is rare.Therefore, this article discussed codeine, hyperbaric oxygen therapy (HBO), and its relation to decrease level of codein addiction, based on scientific literatures.In conclusion, HBO treatment has a potential correlation to minimize the effects of opioid withdrawal, since it can induce nitric oxide and involves the control of monoaminergic neurotransmitters that reduce withdrawal effects.
Test Paper Automatic Generating Method Based on Hybrid Genetic Algorithm

As an important part of computer-assisted instruction, test paper automatic generation system provides data and intelligent information support for the teaching process. In order to solve the problem that the result of test paper generating system is not ideal, a hybrid genetic algorithm is proposed to improve the multi-objective optimization process of test paper automatic generating. The selection operator adopts the μ + λ selection strategy of combinatorial optimization evolutionary algorithm, which improves the global search ability of the algorithm. The crossover operator adopts the method of subsection crossover according to the question type to ensure that the number of questions of each question type remains unchanged after crossover. Mutation operator adopts mutation mechanism based on tabu search, which solves the defect of premature convergence of genetic algorithm. The experimental results show that the improved algorithm improves the efficiency and success rate of test paper automatic generation.
Impact of Career Path-Related Education on Learning Self and Career Efficacy: A Performance Analysis of Content-Related Vocational Education Considering Student PROTECTION

Purpose: This study seeks to examine whether the students who experience difficult vocational curricula can form a healthy vocational mindset and career efficacy based on teachers’ support and career path-related counseling. Moreover, it aims to determine whether students undergoing the vocational education consignment course do, in fact, take up a related career. Method: A total of 624 senior students participated at Ahyeon Industrial Information School. The questionnaire survey was conducted twice. A total of four questions regarding career path counseling were asked, to be scored on a 6-point scale; a total of six questions regarding teachers’ support and another four regard-ing the learning self were asked, both scored on a 5-point scale; and four questions were asked about career efficacy, to be scored on a 4-point scale. Results: According to the results of the statistical model based on the social cognitive theory, career path counseling and teachers’ support, which correspond to environmen-tal factors, seem to influence the formation of the learning self. The teachers’ support and career path-related counseling influenced career efficacy; however, when the learning self question was added, the relevant effect was not directly demonstrated. Conclusion: The career path-related counseling and teachers’ support also had an effect on the formation of the learning self, and the learning self which was formed had a large effect on the development of career efficacy. Furthermore, career efficacy had a significant effect on career path maturity, mastery approach goal, and performance approach goal, suggest-ing that it is a powerful factor in helping students make their career path decisions. This study is expected to make a meaningful contribution to reminding them of the need for teachers’ support and career path counseling to help improve students’ learning self and career efficacy when designing game-related content vocational education programs.
Examining the Influence of the Demographic Variables on Organizational Citizenship Behavior Levels among Bank Employees in Madurai District

The study concentrates on the influence of the demographic variables on Organizational Citizenship Behavior levels among bank employees. The term Organizational Citizenship Behavior (OCB) is defined as the voluntary commitment exhibited by the employees within an organization. OCB allows employees to maintain continuous interaction with the existing and prospective customers, especially in branch banking. To increase productivity, efficiency, and customer satisfaction and reduce turnover rates and absenteeism, the organization should motivate and encourage the employees to exhibit OCB. Also, voluntary behavior will result in the success of the organization. The study is focused on the effects of five dimensions of OCB as Altruism, Courtesy, Conscientiousness, Civic Virtue and Sportsmanship. The researcher uses a descriptive research design and a simple random sampling method with a sample size of 100 respondents. The main aim of the study is to examine the influence of the demographic variables on OCB levels among bank employees in the Madurai district.
Mining High Utility-probability Sequential Patterns in Bigdata Environments

High utility-probability sequential pattern mining (HUPSPM) has become a novel research errand realm in data mining. In practical application, most of the data are collected by a variety of sensors, forming an uncertain database. Therefore, the data collected in real life can be expressed in terms of probability. In previous research work, for the sake of heightening the performance of mining in small data sets and single machine, a great deal of algorithm have been posed. Unfortunately, these algorithms have loopholes in mining large databases with mickle records. Therefore, it is very urgent to construct an efficient model to deal with large uncertain dateabas data sets. In this article, a MapReduce framework is moved to mine effective information in multitudinous uncertain data sets and reduce the number of database scans. Experiments on a large number of uncertain data sets show that the posed Hadoop algorithm has a strong ability to mine the high utility-probability sequential pattern.
Peer review declaration

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Single-blind • Conference submission management system: The system developed in the information and computing centre of NRU MPEI (webgroup@mpei.ru). All the files and information necessary for registration are uploaded to the conference website, after which the report is assigned a registration number that is used to identify the article • Number of submissions received: 71 • Number of submissions sent for review: 71 • Number of submissions accepted: 56 • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 69,6 • Average number of reviews per paper: 2.126 • Total number of reviewers involved: 152 • Any additional info on review process: The reviewers were members of the scientific committee and scientists (foreign and domestic) who are experts in each of the presented topics • Contact person for queries: Name : Pyatnitskaya Natalia Affiliation: National Research Univercity Moscow Power Engineering Institute Email : PiatnitskyaNY@mpei.ru
Gray-Scale Image Color Migration Algorithm Based on Dual-Stream Convolutional Neural Network

In order to solve the problems of insufficient image feature information extraction in image processing by traditional color migration algorithms, resulting images have obvious color mis-transmission and unnatural colors, a gray-scale image color migration algorithm based on dual-stream convolutional neural network is proposed. Firstly, the improved SE-ResNet-20 subnetwork with feature recalibration function and the improved VGG19 subnetwork are used to extract feature maps containing local information and global information respectively, and then the two types of feature maps are fused, and the fusion results are used as the input of the coloring network. Finally, the color transfer of gray image is realized by feature analysis combined with classification information. In the training process, two loss functions, pixel loss and classification loss, are used to train the model at the same time. The experimental results show that the algorithm in this paper effectively solves the problem of color mis-transmission. As a result, the color distribution of the image is more reasonable and natural, and the texture is clearer.