• Journal of the Korean Geographical Society
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• v.31 no.3
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• pp.577-592
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• 1996

The forming process of poverty region in Taegu and the feature of its spatial distribution which are reviewed hitherto can be summarized like this. 1) In the froming porcess of poverty region in Taegu, during the soverignty of Japanese Empire petty farmers became tenantry by the colonial agricultural policy of Japanes Empire and some of those came into the city and g\became urban poor class. They generally lived in poor houses or dugouts in the city, and 6.6$\circ$ of poor house and dugouts of the whole country were in Taegu and 4.9$\circ of the popolatio in Taegu resided there. During the period of disorder, because of the historic accidents, such as the restoration of independence and Korean War, the returnees from aboad and refugees converged into the big city so that those who need the country's relief stood out as new poor class. They generally made their dwellings with tents and straw-bags on vacant grounds in suburbs living form hand to mouth and shaped the poor houses area, so-clalled "Liberated Village". During the developing period, the number of those who need aid gradually decreased, but the problem of poor people by the city-concentration of the poeple who shifted from agricultrual jobs by economic development came to the front. They mostly lived in squatter area forming large poor class area, and generally located near the center of Taegu consisiting of West. South. East Ward. 2) Reviewing the the feature of spatial distribution, the proportion of poor class are highest within 1~2km from the center of the city and also high within 2~3km form the center and suburbs. The poor class area in the center of the city are mostly cleared and removed area and in suburbs by the construction of permanently leased, and leased apartments large grouped poor class areas are forming. In Taegu, 16 low-income class group residence areas and residential environement improving areas are dispersed so that they came under the so-called poor class area. But by the improvement of dewelling environment and living the poor people who lived in groups dispersed or bettered their living for themselves, so the poverty area is greatly chaning into average-levelled residence area, and on the other hand, large poor people's apartment complexes are being constructed in suburbs. 3) Up to now, the distribution of poverty area could be limited its scale to generally the area within 1~3km because the poverty region which had been in suburbs relatively came near the center of the city by the rapid urbanization and poor people preferred that area because of the living convenience facilities as well as the transportation facilities and job-hunting being near the center of the city. But now poor people's apartment complex is being constructed regardless of their zone of job sites, so the low proportion of occupation is pointed as a new problem.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.247-264
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• 2016

With the rapid acceleration of low-birth rate and population aging, the employment of the neglected groups of people including the middle aged class is a crucial issue in South Korea. In particular, in the 2010s, the number of the middle aged who want to find a new job after retirement age is significantly increasing with the arrival of the retirement time of the baby boom generation (born 1955-1963). Despite the importance of matching jobs to this emerging middle aged class, private job portals as well as the Korean government do not provide any online job service tailored for them. A gigantic amount of job information is available online; however, the current recruiting systems do not meet the demand of the middle aged class as their primary targets are young workers. We are in dire need of a specially designed recruiting system for the middle aged. Meanwhile, when users are searching the desired occupations on the Worknet website, provided by the Korean Ministry of Employment and Labor, users are experiencing discomfort to search for similar jobs because Worknet is providing filtered search results on the basis of exact matches of a preferred job code. Besides, according to our Worknet data analysis, only about 24% of job seekers had landed on a job position consistent with their initial preferred job code while the rest had landed on a position different from their initial preference. To improve the situation, particularly for the middle aged class, we investigate a soft job matching technique by performing the following: 1) we review a user behavior logs of Worknet, which is a public job recruiting system set up by the Korean government and point out key system design implications for the middle aged. Specifically, we analyze the job postings that include preferential tags for the middle aged in order to disclose what types of jobs are in favor of the middle aged; 2) we develope a new occupation classification scheme for the middle aged, Korea Occupation Classification for the Middle-aged (KOCM), based on the similarity between jobs by reorganizing and modifying a general occupation classification scheme. When viewed from the perspective of job placement, an occupation classification scheme is a way to connect the enterprises and job seekers and a basic mechanism for job placement. The key features of KOCM include establishing the Simple Labor category, which is the most requested category by enterprises; and 3) we design MOMA (Middle-aged Occupation Matching Algorithm), which is a hybrid job matching algorithm comprising constraint-based reasoning and case-based reasoning. MOMA incorporates KOCM to expand query to search similar jobs in the database. MOMA utilizes cosine similarity between user requirement and job posting to rank a set of postings in terms of preferred job code, salary, distance, and job type. The developed system using MOMA demonstrates about 20 times of improvement over the hard matching performance. In implementing the algorithm for a web-based application of recruiting system for the middle aged, we also considered the usability issue of making the system easier to use, which is especially important for this particular class of users. That is, we wanted to improve the usability of the system during the job search process for the middle aged users by asking to enter only a few simple and core pieces of information such as preferred job (job code), salary, and (allowable) distance to the working place, enabling the middle aged to find a job suitable to their needs efficiently. The Web site implemented with MOMA should be able to contribute to improving job search of the middle aged class. We also expect the overall approach to be applicable to other groups of people for the improvement of job matching results.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.640-654
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• 2021

Purpose: Normal education operation was difficult in the national disaster situation of Coronavirus Infection-19. Non-face-to-face education can be an alternative to face to face education, but it is not easy to provide the same level of education. In this study, the professor of disaster management field will identify problems that can occur in the overall operation and progress of non-face-to-face education and seek ways to improve non-face-to-face education. Method: Non-face-to-face real-time education was largely categorized into pre-class, in-class, post-class, and evaluation, and case studies were conducted through the professor's case studies. Result&Conclusion: The results of the survey are as follows: First, pre-class, it was worth considering providing a non-face-to-face educational place for professors, and the need for prior education on non-face-to-face educational equipment and systems was required. In addition, it seems necessary to make sure that education is operated smoothly by giving enough notice on classes and to make efforts to develop non-face-to-face education programs for practical class. Second, communication between professor and learner, and among learners can be an important factor in non-face-to-face mid classes. To this end, it is necessary to actively utilize debate-type classes to lead learners to participate in education and enhance the educational effect through constant interaction. Third, non-face-to-face post classes, policies on the protection of privacy due to video records should be prepared to protect the privacy of professors in advance, and copyright infringement on educational materials should also be considered. In addition, it is necessary to devise various methods for fair and objective evaluation. According to the results of the interview, in the contents, which are components of non-face-to-face education, non-face-to-face education requires detailed plans on the number of students, contents, and curriculum suitable for non-face-to-face education from the design of the education. In the system, it is necessary to give the professor enough time to fully learn and familiarize with the function of the program through pre-education on the program before the professor gives non-face-to-face classes, and to operate the helpdesk, which can thoroughly check the pre-examination before non-face-to-face education and quickly resolve the problem in case of a problem.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.123-132
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• 2013

As the smartphones are equipped with various sensors such as the accelerometer, GPS, gravity sensor, gyros, ambient light sensor, proximity sensor, and so on, there have been many research works on making use of these sensors to create valuable applications. Human activity recognition is one such application that is motivated by various welfare applications such as the support for the elderly, measurement of calorie consumption, analysis of lifestyles, analysis of exercise patterns, and so on. One of the challenges faced when using the smartphone sensors for activity recognition is that the number of sensors used should be minimized to save the battery power. When the number of sensors used are restricted, it is difficult to realize a highly accurate activity recognizer or a classifier because it is hard to distinguish between subtly different activities relying on only limited information. The difficulty gets especially severe when the number of different activity classes to be distinguished is very large. In this paper, we show that a fairly accurate classifier can be built that can distinguish ten different activities by using only a single sensor data, i.e., the smartphone accelerometer data. The approach that we take to dealing with this ten-class problem is to use the ensemble of nested dichotomy (END) method that transforms a multi-class problem into multiple two-class problems. END builds a committee of binary classifiers in a nested fashion using a binary tree. At the root of the binary tree, the set of all the classes are split into two subsets of classes by using a binary classifier. At a child node of the tree, a subset of classes is again split into two smaller subsets by using another binary classifier. Continuing in this way, we can obtain a binary tree where each leaf node contains a single class. This binary tree can be viewed as a nested dichotomy that can make multi-class predictions. Depending on how a set of classes are split into two subsets at each node, the final tree that we obtain can be different. Since there can be some classes that are correlated, a particular tree may perform better than the others. However, we can hardly identify the best tree without deep domain knowledge. The END method copes with this problem by building multiple dichotomy trees randomly during learning, and then combining the predictions made by each tree during classification. The END method is generally known to perform well even when the base learner is unable to model complex decision boundaries As the base classifier at each node of the dichotomy, we have used another ensemble classifier called the random forest. A random forest is built by repeatedly generating a decision tree each time with a different random subset of features using a bootstrap sample. By combining bagging with random feature subset selection, a random forest enjoys the advantage of having more diverse ensemble members than a simple bagging. As an overall result, our ensemble of nested dichotomy can actually be seen as a committee of committees of decision trees that can deal with a multi-class problem with high accuracy. The ten classes of activities that we distinguish in this paper are 'Sitting', 'Standing', 'Walking', 'Running', 'Walking Uphill', 'Walking Downhill', 'Running Uphill', 'Running Downhill', 'Falling', and 'Hobbling'. The features used for classifying these activities include not only the magnitude of acceleration vector at each time point but also the maximum, the minimum, and the standard deviation of vector magnitude within a time window of the last 2 seconds, etc. For experiments to compare the performance of END with those of other methods, the accelerometer data has been collected at every 0.1 second for 2 minutes for each activity from 5 volunteers. Among these 5,900 ($=5{\times}(60{\times}2-2)/0.1$) data collected for each activity (the data for the first 2 seconds are trashed because they do not have time window data), 4,700 have been used for training and the rest for testing. Although 'Walking Uphill' is often confused with some other similar activities, END has been found to classify all of the ten activities with a fairly high accuracy of 98.4%. On the other hand, the accuracies achieved by a decision tree, a k-nearest neighbor, and a one-versus-rest support vector machine have been observed as 97.6%, 96.5%, and 97.6%, respectively.

• Journal of the Korean Home Economics Association
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• v.11 no.4
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• pp.414-423
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• 1973

Before the modern education was introduced in Korea men had the opportunity to be educated. Women's education was limited to a small number of girls belonging to ruling class. It was the men who got a job to earn the money for the family. The customary law prohibited women from being employed. They were to stay at home engaged in household affairs. This phenomenon has undergone a change when modern education was adopted which gave women the equal opportunity in education. The modernization of the country required a lot of educated and skilled labour. Since 1945 when Korea was liberated from the Japanese colonial administration the modernization programme has been worked out in every field such as industry, education, culture and politics, etc. The traditional grand family was transformed to nuclear family. The migration took place from country to town. With the adoption of compulsory education in the primary school the schoolgirls are increased in great number. The number of girls has been increased every year in Middle Schools, High schools and Universities. Even if boys still outnumber girls in all education institutions, the rate of increase of girl students are higher than that of boy students. Accordingly women are given more opportunity than ever for the employment vis-a-vis men. The number of employed women has been increasing greatly in recent years inproportion to the acceleration of industrialization. The type of their job is also various and colorful ranging from factory worker to doctor and lawyer. There are some problems to be solved with respect to the improvement of women's education. The improved women's education should be reviewed light of the fact that inequality still exists between men and women in occupation and wages, and that women is required of good education contributable to the better Korean society.

• Journal of Educational Research in Mathematics
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• v.27 no.2
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• pp.249-267
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• 2017

The unit of multiplication in the mathematics textbook for third graders deals with two-digit number multiplied by one-digit number. Students tend to perform multiplication without necessarily understanding the principle behind the calculation. Against this background, we designed the unit in a way for students to explore the principle of multiplication with cuisenaire rods and array models. The results of this study showed that most students were able to represent the process of multiplication with both cuisenaire rods and array models and to connect such a process with multiplicative expressions. More importantly, the associative property of multiplication and the distributive property of multiplication over addition were meaningfully used in the process of writing expressions. To be sure, some students at first had difficulties in representing the process of multiplication but overcame such difficulties through the whole-class discussion. This study is expected to suggest implications for how to teach multiplication on the basis of the properties of the operation with appropriate instructional tools.

• Molecules and Cells
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• v.43 no.7
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• pp.607-618
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• 2020

microRNAs (miRNAs) are non-coding RNA molecules involved in the regulation of gene expression. miRNAs inhibit gene expression by binding to the 3' untranslated region (UTR) of their target gene. miRNAs can originate from transposable elements (TEs), which comprise approximately half of the eukaryotic genome and one type of TE, called the long terminal repeat (LTR) is found in class of retrotransposons. Amongst the miRNAs derived from LTR, hsa-miR-3681 was chosen and analyzed using bioinformatics tools and experimental analysis. Studies on hsa-miR-3681 have been scarce and this study provides the relative expression analysis of hsa-miR-3681-5p from humans, chimpanzees, crab-eating monkeys, and mice. Luciferase assay for hsa-miR-3681-5p and its target gene SHISA7 supports our hypothesis that the number of miRNA binding sites affects target gene expression. Especially, the variable number tandem repeat (VNTR) and hsa-miR-3681-5p share the binding sites in the 3' UTR of SHISA7, which leads the enhancer function of hsamiR-3681-5p to inhibit the activity of VNTR. In conclusion, hsa-miR-3681-5p acts as a super-enhancer and the enhancer function of hsa-miR-3681-5p acts as a repressor of VNTR activity in the 3' UTR of SHISA7.

• Molecules and Cells
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• v.43 no.9
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• pp.821-830
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• 2020

Altered dendritic morphology is frequently observed in various neurological disorders including amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD), but the cellular and molecular basis underlying these pathogenic dendritic abnormalities remains largely unclear. In this study, we investigated dendritic morphological defects caused by dipeptide repeat protein (DPR) toxicity associated with G4C2 expansion mutation of C9orf72 (the leading genetic cause of ALS and FTD) in Drosophila neurons and characterized the underlying pathogenic mechanisms. Among the five DPRs produced by repeat-associated non-ATG translation of G4C2 repeats, we found that arginine-rich DPRs (PR and GR) led to the most significant reduction in dendritic branches and plasma membrane (PM) supply in Class IV dendritic arborization (C4 da) neurons. Furthermore, expression of PR and GR reduced the number of Golgi outposts (GOPs) in dendrites. In Drosophila brains, expression of PR, but not GR, led to a significant reduction in the mRNA level of CrebA, a transcription factor regulating the formation of GOPs. Overexpressing CrebA in PR-expressing C4 da neurons mitigated PM supply defects and restored the number of GOPs, but the number of dendritic branches remained unchanged, suggesting that other molecules besides CrebA may be involved in dendritic branching. Taken together, our results provide valuable insight into the understanding of dendritic pathology associated with C9-ALS/FTD.

• Convergence Security Journal
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• v.23 no.1
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• pp.69-77
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• 2023

Recently, ransomware damage has been increasing rapidly around the world, including Irish health authorities and U.S. oil pipelines, and is causing damage to all sectors of society. In particular, research using machine learning as well as existing detection methods is increasing for ransomware detection and response. However, traditional machine learning has a problem in that it is difficult to extract accurate predictions because the model tends to predict in the direction where there is a lot of data. Accordingly, in an imbalance class consisting of a large number of non-Ransomware (normal code or malware) and a small number of Ransomware, a technique for resolving the imbalance and improving ransomware detection performance is proposed. In this experiment, we use two scenarios (Binary, Multi Classification) to confirm that the sampling technique improves the detection performance of a small number of classes while maintaining the detection performance of a large number of classes. In particular, the proposed mixed sampling technique (SMOTE+ENN) resulted in a performance(G-mean, F1-score) improvement of more than 10%.