Reviewerly/exHarmony huggingface dataset:
Socio-demographic Factors Related to Older Adults’ Lifelong Education Participation Patterns

본 연구는 노인의 인구사회학적 특성에 따라 노인의 평생교육 참여양상에 차이가 있는지를 밝히는 것이 목적이다. 이를 위해 한국보건사회연구원이 실시한「 2017년 노인실태조사」의 원자료를 바탕으로 65세 이상 노인 10,073명의 성별, 연령, 학력, 연가구소득, 최장기 일자리 지위, 건강상태에 따라 평생교육 참여여부, 참여영역, 참여기관, 참여빈도에 차이가 있는지를 분석하였다. 연구결과 여성(성별), 70-74세, 75-79세(연령), 중학교 재학/졸업, 고등학교 재학/졸업, 전문대학 휴학 이상(학력), 상용근로자, 무급가족종사자(최장 기 일자리 지위), 그리고 건강상태의 노인집단이 평생교육에 보다 많이 참여하였다. 중학교 졸업 이하의 학력집단과 고등학교 졸업이상 집단, 그리고 제1, 2분위 소득집단과 제3, 4, 5분위 소득집단 간에 유의미한 평생교육 참여영역에 차이가 발견되었다. 평생교육 참여기관과 관련해서는 노인복지관, 경로당, 노인교실은 70세 이상, 중학교 학력 이하, 3분위 소득 이하, 건강이 나쁜 집단이 참여하는 경향이 나타났다. 그리고 평생교육 참여빈도와 관련해서는 무학에 비해 고등학교 재학/졸업 및 전문대학 휴학 이상 집단이, 무급가족종사자에 비해 상용근로자 집단이 자주 평생교육에 참여하는 것으로 나타났다. 본 연구를 통해 우려되고 있는 인구사회학적 특성에 따른 평생교육 참여 불평등 현상이 노인 집단 안에서도 있다는 것을 밝힐 수 있다.The purpose of this study was to understand socio-demographic factors related to older adults participation patterns in lifelong education. For the purpose, this study used the raw data of 2017 Survey of the Living Conditions of the Elderly (SLCE) conducted by The Korea Institute for Health and Social Affairs. From the data of 10,073 older adults, their lifelong education participation, participating program types, participating organizations, and participating frequency were analyzed by their sex, age, educational level, household income, the longest job status, and health status. This study found that female, age of 70-74 and 75-79, educational levels of high school and higher, the longest job status of regular employees and unpaid family workers, and decent health status of older adults more participated in lifelong education. According to lifelong education program types, significant differences were found between education groups of middle school/lower and groups of high school/higher and between 1, 2 quintile income groups and 3, 4, 5 quintile income groups. In relation to the participating organizations, groups of 70 years and older, middle school and higher education level, under 3 quintile income, and poor health tended to participate in lifelong education at the elderly welfare center, senior citizens, and elderly classrooms. In terms of participation frequency, high school and college/higher than 0 year of school education, and regular workers than unpaid family workers were more frequently participated in lifelong education. This study showed the inequality in lifelong education participation according to older adults’ demographic characteristics; finally, this study suggested necessary policies and academic discussions for future older adults’ lifelong education.
Switching Control of A Mecanum Wheeled Mobile Robot for Vision-Based Tracking with Intermittent Image Losses

Vision-based detection and estimation technologies play crucial roles in target tracking for autonomous vehicles. However, for vision system it is difficult to continuously collect the image information due to occlusion, feature matching failure, etc. This paper is concerned with the target tracking problem for a Mecanum wheeled mobile robot (MWMR) with camera in the presence of intermittent image losses. A switching scheme with two control strategies has been developed to deal with the problem. During the period when the target image is acquired, a stabilizing controller is designed to ensure the convergence of tracking error. When the target image is lost, a predictor is used to provide the controller with estimated tracking error, by which a regular graphic region containing the real position of the target is determined. The stability condition for the tracking system with minimum dwell-time during which the image is acquired has been obtained utilizing the region. Simulation results verify the effectiveness of the proposed methods.
Degenerate zero-truncated Poisson random variables

In this paper, we introduce the degenerate zero-truncated Poisson random variables whose probability mass functions are a natural extension of the zero-truncated Poisson distributions, and investigate various properties of those random variables.
A Code Summarization Approach for Object Oriented Programs

The software maintenance process is not an easy job, especially when the source code becomes more complex and less documented. Software developers and engineers may spend plenty of time to understand the structures and features of the source code in order to maintain their software projects. Using an automated code summarization technique is a key factor to save time and cost of the maintenance process. Generating descriptive documents from the source code helps the software developers to understand their software. In this paper, a code summarization framework is proposed to document the source code. A set of software features are extracted and displayed to the developers based on mapping the target source code segments to an XML representation. The proposed framework has been conducted on an open source project to evaluate its effectiveness. The generated results showed that the proposed approach is useful in understanding the different structural aspects of the source code.
Computer-aided Metals' Microstructure Analysis. A non-standard approach to image analysis

Analysis of metals' microstructure images is an actual quantitative analysis problem, solved by quality control and research labs in the field of metallurgy. SIAMS Ltd pursues the goal of improving microstructure analysis quality, speed, and convenience. This article discusses the issues of recognition of the microstructure elements of metals and alloys, the most common problems and recognition errors, methods for solving them. Microstructure examples are given before and after digital image processing. The question of the advisability of using server technology in digital microscopy that removes such restrictions from users as the size of the shooting area and the area of microstructure analysis, binding to one working computer and one software license, as well as restrictions on the exchange of results between industry experts, will be raised.
Synthesizing Visual Illusions Using Generative Adversarial Networks

Visual illusions are a very useful tool for vision scientists, because they allow them to better probe the limits, thresholds and errors of the visual system. In this work we introduce the first ever framework to generate novel visual illusions with an artificial neural network (ANN). It takes the form of a generative adversarial network, with a generator of visual illusion candidates and two discriminator modules, one for the inducer background and another that decides whether or not the candidate is indeed an illusion. The generality of the model is exemplified by synthesizing illusions of different types, and validated with psychophysical experiments that corroborate that the outputs of our ANN are indeed visual illusions to human observers. Apart from synthesizing new visual illusions, which may help vision researchers, the proposed model has the potential to open new ways to study the similarities and differences between ANN and human visual perception.
Suitable small farm reservoir development planning for drought disaster risk management of agricultural land using remote sensing and GIS

Water is the resource and determinant factor that determines the performance of the agricultural sector, although the role is very strategic, the water management is still far from expected so that the water that should be a farmer's turn turned into a cause of disaster for farmers. Small farm reservoir is built to accommodate excess rainwater during the rainy season. The water collected is then used as a source of supplementary irrigation for the cultivation of high-value economic commodities in the dry season. This research aims to survey, inventory, and study the potential of small farm reservoir development, and to plan potential site locations to be developed into small farm reservoir based on the results of analysis of physical and socio-economic data and drought potential using advanced remote sensing technology in Jombang Regency. Research planning of small farm reservoir location in Jombang Regency is designed in four stages of activity that is data gathering, mapping, and compilation of database, analysis, planning of location, and recommendation of the small farm reservoir location. The results of this research state that the appropriate area built small farm reservoir based on the parameters used are Ngoro, Mojoagung, Kesamben, and Kabuh village.
Teachers’ Management In Planning and Implementing for the Success of EFL Learning

Group work as one of teaching techniques on the framework of communicative approach is believed to be an effective model for building students’ communicative competence. The process of applying this technique needs high skills of management of an English teacher. The English teacher has autonomy to plan and implement the group work in order to achieve an objective of EFL learning. Since the autonomy handled by the teacher, any decisions and actions taken should be considered for assuring the success of EFL learning. The teachers’ management in planning and implementing group work influenced the successful of this teaching technique. This article, which is derived and further developed based on research conducted in 2013, analyzes and discusses the way the teachers plan and implement group work for the success of EFL learning at MAN Kota Solok. The research was conducted in terms of qualitative research by involving 5 English teachers at MAN kota Solok. The research found that the teachers still needs some guidance and improvements in planning and implementing group work for students at MAN Kota Solok
Maximum gap in cyclotomic polynomials

Cyclotomic polynomials play fundamental roles in number theory, combinatorics, algebra and their applications. Hence their properties have been extensively investigated. In this paper, we study the maximum gap $g$ (maximum of the differences between any two consecutive exponents). In 2012, it was shown that $g\left( \Phi_{p_{1}p_{2}}\right) =p_{1} -1$ for primes $p_{2}>p_{1}$. In 2017, based on numerous calculations, the following generalization was conjectured: $g\left( \Phi_{mp}\right) =\varphi(m)$ for square free odd $m$ and prime $p>m$. The main contribution of this paper is a proof of this conjecture.
Unsupervised Domain Adaptation of Language Models for Reading Comprehension

This study tackles unsupervised domain adaptation of reading comprehension (UDARC). Reading comprehension (RC) is a task to learn the capability for question answering with textual sources. State-of-the-art models on RC still do not have general linguistic intelligence; i.e., their accuracy worsens for out-domain datasets that are not used in the training. We hypothesize that this discrepancy is caused by a lack of the language modeling (LM) capability for the out-domain. The UDARC task allows models to use supervised RC training data in the source domain and only unlabeled passages in the target domain. To solve the UDARC problem, we provide two domain adaptation models. The first one learns the out-domain LM and in-domain RC task sequentially. The second one is the proposed model that uses a multi-task learning approach of LM and RC. The models can retain both the RC capability acquired from the supervised data in the source domain and the LM capability from the unlabeled data in the target domain. We evaluated the models on UDARC with five datasets in different domains. The models outperformed the model without domain adaptation. In particular, the proposed model yielded an improvement of 4.3/4.2 points in EM/F1 in an unseen biomedical domain.
Fraud Detection and Fraudulent Risks Management in the Insurance Sector Using Selected Data Mining Tools

Knowledge discovery, shortly known as Data mining plays a crucial role within the insurance sector.Serious troublesome cases such as fraudulent cases can be well managed in the insurance sector through data mining application.In this paper, we aim to put on surface the two forms of fraud that is softy and hard fraud, to give out the causes of such fraudulent acts and to state out different suggested data mining techniques that can be applied to the insurance data to detect fraud.Also, we aim to highlight other benefits that can be enjoyed from using data mining in the insurance sector.We conjectured and found that, application of data mining helps to quickly detect fraud, reduce operation cost and to improve profit margins and increased competitive advantages.We put forward that techniques such as association, clustering, classification and regression are good when detecting fraud from the insurance claims data and should be acquired and applied.We then recommended that, underwriters and insurance officials should contribute much in preventing fraudulent cases in the insurance sector.This is so because, prevention is better than cure.Above all, we concluded that, application of data mining techniques through sequential pattern mining can help much to predict any future and potential fraudulent cases.This is helpful on planning and to keep the insurers alert before the fraudulent risk occurs.
Decomposition of a Media Event Through the Definition of Information Waves

An unexpected event affects the media space of social networks and through the impacts on social processes in society. These phenomena need to be recorded, investigated, and interpreted. However, existing approaches to the collection and analysis of textual data place social outcomes beyond the research framework. This article proposes an integrated approach to the consideration of media space as a set of media events. To understand the nature of a media event, it is necessary to decompose it to the level of information waves. These concepts (media event and information waves) allow us to build a meaningful methodology and draw conclusions about the nature of the publication flows following the event. The explosion in Magnitogorsk, which occurred on December 31, 2018, and had a comprehensive public response, was analyzed as an example of a media event. The article shows the possibility of combining sociological methodology as a meaningful analytical framework and mathematical and linguistic procedures to identify text clusters.
EBSCO Discovery Service (EDS) Usage in Israeli Academic Libraries

Awareness to the problem that different search interfaces discourage patrons’ use of library information sources has led academic libraries to implement web-scale discovery services. These services offer the user a “Google-like” search experience of library resources. This study aims to explore library professionals’ satisfaction, patrons’ information behavior, and use of EDS discovery tool service in academic libraries in Israel. Mixed research methods were used in this study: qualitative and quantitative. Qualitative research methods are through content analysis of library directors’ interviews, and quantitative research method is through collected library metrics (from Google analytics) data analysis, regarding usage patterns and search session analysis. The study aims to gain insight regarding library implementation and patrons’ information behavior of the EDS discovery tool, in Israeli higher education institutions.
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVM

OneClass SVM is a popular method for unsupervised anomaly detection. As many other methods, it suffers from the \textit{black box} problem: it is difficult to justify, in an intuitive and simple manner, why the decision frontier is identifying data points as anomalous or non anomalous. Such type of problem is being widely addressed for supervised models. However, it is still an uncharted area for unsupervised learning. In this paper, we evaluate some of the most important rule extraction techniques over OneClass SVM models, as well as presenting alternative designs for some of those XAI algorithms. Together with that, we propose algorithms to compute metrics related with XAI regarding the comprehensivility, representativeness, stability and diversity of the rules extracted. We evaluate our proposals with different datasets, including real-world data coming from industry. With this, our proposal contributes to extend Explainable AI techniques to unsupervised machine learning models.
SCIENTIFIC REFERENCES IMPORT FROM UNSTRUCTURED DATA

Along teaching scientific research is second most important task for every university and faculty member. Research results are commonly published as journal papers, book chapters or as conference proceedings with the purpose to communicate the findings in academic circles, serve as promotion, or be used for tenure or reapplication. Maintaining the list of scientific references, can be a time consuming and tedious activity. To help along the process we have created a Web application named References that makes the process of editing and formatting the references partially automated and less time consuming. In this paper we present the architecture of the application, its functionalities and newly added feature that enables importing references both from structured and unstructured data.
Braided distributivity

In category-theoretic models for the anyon systems proposed for topological quantum computing, the essential ingredients are two monoidal structures, $\oplus$ and $\otimes$. The former is symmetric but the latter is only braided, and $\otimes$ is required to distribute over $\oplus$. What are the appropriate coherence conditions for the distributivity isomorphisms? We came to this question working on a simplification of the category-theoretical foundation of topological quantum computing, which is the intended application of the research reported here. This question was answered by Laplaza when both monoidal structures are symmetric, but topological quantum computation depends crucially on $\otimes$ being only braided, not symmetric. We propose coherence conditions for distributivity in this situation, and we prove that our conditions are (a) strong enough to imply Laplaza's when the latter are suitably formulated, and (b) weak enough to hold when --- as in the categories used to model anyons --- the additive structure is that of an abelian category and the braided $\otimes$ is additive. Working on these results, we found a new redundancy in Laplaza's conditions.
The Group Loss for Deep Metric Learning

Deep metric learning has yielded impressive results in tasks such as clustering and image retrieval by leveraging neural networks to obtain highly discriminative feature embeddings, which can be used to group samples into different classes. Much research has been devoted to the design of smart loss functions or data mining strategies for training such networks. Most methods consider only pairs or triplets of samples within a mini-batch to compute the loss function, which is commonly based on the distance between embeddings. We propose Group Loss, a loss function based on a differentiable label-propagation method that enforces embedding similarity across all samples of a group while promoting, at the same time, low-density regions amongst data points belonging to different groups. Guided by the smoothness assumption that similar objects should belong to the same group, the proposed loss trains the neural network for a classification task, enforcing a consistent labelling amongst samples within a class. We show state-of-the-art results on clustering and image retrieval on several datasets, and show the potential of our method when combined with other techniques such as ensembles
MODERN TRENDS OF MUSICAL ART SPECIALISTS’ PROFESSIONAL TRAINING IN THE CONDITIONS OF EUROPEAN EDUCATION

The article defines modern approaches to European education in the context of artistic educational reforms. Separate legislative acts on this issue are analyzed, the attention is focused on scientific intelligence of recent years. The main strategic goal of the development of higher education of Ukraine at the present stage is the integration of higher education of Ukraine into the European educational system in the conditions of joining the Bologna process, preventing possible difficulties and taking into account the prospects of this progressive process, harmonizing and combining the national components of higher education with the requirements of the Bologna Declaration for the creation of a unified European Higher Education Area. The research was conducted on the basis of identified new degrees and levels of future specialists’ training. The tasks are: to find out modern approaches to the training of specialists in art education, to analyze the state of the problem under study by the example of individual educational institutions of Ukraine. Methods of scientific research: analysis, comparison, observation of the educational process, questionnaire, synthesis. The author proposes separate learning technologies in the process of art disciplines teaching.
2D Wasserstein Loss for Robust Facial Landmark Detection

The recent performance of facial landmark detection has been significantly improved by using deep Convolutional Neural Networks (CNNs), especially the Heatmap Regression Models (HRMs). Although their performance on common benchmark datasets has reached a high level, the robustness of these models still remains a challenging problem in the practical use under noisy conditions of realistic environments. Contrary to most existing work focusing on the design of new models, we argue that improving the robustness requires rethinking many other aspects, including the use of datasets, the format of landmark annotation, the evaluation metric as well as the training and detection algorithm itself. In this paper, we propose a novel method for robust facial landmark detection, using a loss function based on the 2D Wasserstein distance combined with a new landmark coordinate sampling relying on the barycenter of the individual probability distributions. Our method can be plugged-and-play on most state-of-the-art HRMs with neither additional complexity nor structural modifications of the models. Further, with the large performance increase, we found that current evaluation metrics can no longer fully reflect the robustness of these models. Therefore, we propose several improvements to the standard evaluation protocol. Extensive experimental results on both traditional evaluation metrics and our evaluation metrics demonstrate that our approach significantly improves the robustness of state-of-the-art facial landmark detection models.
Expanding the boundaries of the university: the Regional complex for gifted children and youth

Formation of a modern university as a driver of regional development is impossible without its participation in forming a territory’s intellectual potential, in particular, by its involvement into system of children’s additional education. This work is devoted to studying а university’s role in children’s additional education mechanisms development. Advantages of higher educational institutions in its implementation are shown. To illustrate the results of the study, the case of Don State Technical University, a Pillar University of the region, is presented. Role, structure and specificities of functioning of the Regional complex for gifted children and youth, which is created at the Pillar University, are shown.