• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.681-688
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• 2015

The k nearest neighbor (k-NN) graph construction is an important operation with many web-related applications, including collaborative filtering, similarity search, and many others in data mining and machine learning. Despite its many elegant properties, the brute force k-NN graph construction method has a computational complexity of $O(n^2)$, which is prohibitive for large scale data sets. Thus, (Key, Value)-based distributed framework, MapReduce, is gaining increasingly widespread use in Locality Sensitive Hashing which is efficient for high-dimension and sparse data. Based on the two-stage strategy, we engage the locality sensitive hashing technique to divide users into small subsets, and then calculate similarity between pairs in the small subsets using a brute force method on MapReduce. Specifically, generating a candidate group stage is important since brute-force calculation is performed in the following step. However, existing methods do not prevent large candidate groups. In this paper, we proposed an efficient algorithm for approximate k-NN graph construction by regrouping candidate groups. Experimental results show that our approach is more effective than existing methods in terms of graph accuracy and scan rate.

• 한국노년학
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• v.30 no.1
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• pp.221-240
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• 2010

Despite a growing trend in elder mistreatment, research about the problem and its effects on the victims has been sparse. Notably missing are the perspectives of older adults themselves, whose perceptions and responses to elder mistreatment are greatly affected by social and cultural context. The purpose of this study is to examine factors associated with older persons' perceptions of elder mistreatment and subsequent help-seeking behaviors. Six hypothetical scenarios featuring elders were the basis of interviews with 124 older persons, drawn by a quota sampling strategy. Findings indicated that older persons perceived situations of physical abuse (87.9%), financial abuse (86.3%), and psychological abuse (66.1%) to be elder mistreatment, yet respondents showed less sensitivity to elder mistreatment that took the form of physical mistreatment within a couple (47.6%), neglect (40.3%), and self-neglect (16.9%). Certain scenarios yielded less intention to seek help: namely, physical mistreatment within a couple, neglect, and self-neglect. Older persons' personal characteristics, social factors, and cultural factors exerted significant influence on both perception and help-seeking behavior. Perception was identified as a key factor that significantly influenced help-seeking behavior. Findings point to awareness of cultural and social context for success in elder mistreatment prevention, intervention, and policy design for this population.

• Smart Media Journal
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• v.10 no.3
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• pp.39-47
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• 2021

Enhanced index tracking is a problem of optimizing the objective function to generate returns above the index based on the index tracking that follows the market return. In order to avoid problems such as large transaction costs and illiquidity, we used a method of constructing a portfolio by selecting only some of the stocks included in the index. Commonly used enhanced index tracking methods tried to find the optimal portfolio with only one objective function in all tested periods, but it is almost impossible to find the ultimate strategy that always works well in the volatile financial market. In addition, it is important to improve generalization performance beyond optimizing the objective function for training data due to the nature of the financial market, where statistical characteristics change significantly over time, but existing methods have a limitation in that there is no direct discussion for this. In order to solve these problems, this paper proposes ensemble learning that composes a portfolio by combining several objective functions and a 3-stage portfolio selection algorithm that can select a portfolio by applying criteria other than the objective function to the training data. The proposed method in an experiment using the S&P500 index shows Sharpe ratio that is 27% higher than the index and the existing methods, showing that the 3-stage portfolio selection algorithm and ensemble learning are effective in selecting an enhanced index portfolio.

• Microbiology and Biotechnology Letters
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• v.47 no.1
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• pp.164-171
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• 2019

Radiation therapy has many side effects, such as digestive mucosal ulcers, without regard to its efficacy. The purpose of this study is to address an alternative method to replace the limitation of radiation therapy using radiomimetic microbial ribotoxins. In the evaluation of cancer therapy, we analyzed the formation of colorectal cancer (CRC) cell spheroids, which can take into account the heterogeneous cellular constitution, tumor stem cells, and the surrounding microenvironment. Ribotoxic stress interfered with the spheroid structure composed of relatively small clusters. Spheroids under ribotoxic stress were structurally sparse and their shrinkage was very slow. In the control group, the clusters of strongly aggregated cells were resistant to physical stress, but the ribotoxic stress-exposed spheroids were easily broken up by the physical stress. Moreover, the ribosome-insulted CRC cells slowly migrated to form clusters and the cell-cell junctional points in the ribosome-insulted spheroids were rarer than those in the control CRC spheroid. Moreover, levels of the cell-to-cell junctional protein E-cadherin were suppressed by ribotoxic stress in both allograft and xenograft spheroids. In conclusion, the radiomimetic microbial ribotoxins induced structural defects in CRC cell spheroids via retardation of migration and cell-cell junction in the formation of three-dimensional structures, and provides a basis for the mechanism of pharmacological radiomimetic anticancer actions as an alternate to radiotherapy against cancer.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• The Korean Journal of Applied Statistics
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• v.34 no.3
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• pp.309-327
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• 2021

Dynamic treatment regimes (DTRs) are decision-making rules designed to provide personalized treatment to individuals in multi-stage randomized trials. Unlike classical methods, in which all individuals are prescribed the same type of treatment, DTRs prescribe patient-tailored treatments which take into account individual characteristics that may change over time. The Q-learning method, one of regression-based algorithms to figure out optimal treatment rules, becomes more popular as it can be easily implemented. However, the performance of the Q-learning algorithm heavily relies on the correct specification of the Q-function for response, especially in observational studies. In this article, we examine a number of double-robust weighted least-squares estimating methods for Q-learning in high-dimensional settings, where treatment models for propensity score and penalization for sparse estimation are also investigated. We further consider flexible ensemble machine learning methods for the treatment model to achieve double-robustness, so that optimal decision rule can be correctly estimated as long as at least one of the outcome model or treatment model is correct. Extensive simulation studies show that the proposed methods work well with practical sample sizes. The practical utility of the proposed methods is proven with real data example.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.1
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• pp.13-24
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• 2021

Tunnel boring machine (TBM) is widely used for tunnel excavation in hard rock and soft ground. In the perspective of TBM-based tunneling, one of the main challenges is to drive the machine optimally according to varying geological conditions, which could significantly lead to saving highly expensive costs by reducing the total operation time. Generally, drilling investigations are conducted to survey the geological ground before the TBM tunneling. However, it is difficult to provide the precise ground information over the whole tunnel path to operators because it acquires insufficient samples around the path sparsely and irregularly. To overcome this issue, in this study, we proposed a geological type classification system using the TBM operating data recorded in a 5 s sampling rate. We first categorized the various geological conditions (here, we limit to granite) as three geological types (i.e., rock, soil, and mixed type). Then, we applied the preprocessing methods including outlier rejection, normalization, and extracting input features, etc. We adopted a deep neural network (DNN), which has 6 hidden layers, to classify the geological types based on TBM operating data. We evaluated the classification system using the 10-fold cross-validation. Average classification accuracy presents the 75.4% (here, the total number of data were 388,639 samples). Our experimental results still need to improve accuracy but show that geology information classification technique based on TBM operating data could be utilized in the real environment to complement the sparse ground information.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.395-400
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• 2020

Pigs have been used widely in biomedical research owing to their physiologic and anatomic similarities to humans. Analysis of the hematologic and biochemical values in pigs is an important basis for biomedical research and veterinary clinical diagnosis, but research on transgenic pigs has been sparse. This study was conducted to obtain basic data on transgenic pigs and to describe and compare the reference values for hematologic and biochemical parameters in human tissue plasminogen activator (htPA) transgenic pigs vs normal pigs. Blood samples were obtained from 7 normal LY (Landrace-Yorkshire crossbred) pigs and 8 transgenic pigs and 16 hematologic and 15 serum biochemical parameters were tested. Among the hematologic parameters tested, significant differences were observed in the red blood cells (RBC), mean red blood cell hemoglobin (MCH), and lymphocytes (LYM), between the non-transgenic and transgenic pigs. Among the biochemical parameters tested, the blood urea nitrogen (BUN), total protein (TP), cholesterol (CHOL), alanine aminotransferase (ALT), creatinine (CREA), gamma glutamyl transpeptidase (GGT), globin (GOB), and amylase (AMYL) showed significant differences between the two groups. Thus, the values determined in this study can be used as basic reference values for transgenic pigs and will contribute to their use in biomedical research.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.82-88
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• 2021

Sun-induced fluorescence (SIF) retrieval using remote sensing technique has been used in an effort to understand the photosynthetic efficiency and stress condition of vegetation. Although optical devices and SIF retrieval methodologies were established in order to retrieve SIF, the SIF measurements are domestically sparse. SIF data of paddy rice w as measured in Naju, South Korea from June 10, 2020 to October 5, 2020. The SIFs based red (O2A) and far-red (O2B) w ere retrieved using a spectral fitting method and an improved Fraunhofer line depth, and photosynthetically active radiation was also produced. In addition, the SIF data was filtered considering solar zenith angle, saturation conditions, the rapid and sudden change of solar irradiance, and sun glint. The provided SIF data can help to understand a SIF product and the filtering method of SIF data can contribute to producing high-quality SIF data.

• Animal Bioscience
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• v.35 no.11
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• pp.1787-1799
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• 2022

Objective: Choline deficiency, one main trigger for nonalcoholic fatty liver disease (NAFLD), is closely related to lipid metabolism disorder. Previous study in a choline-deficient model has largely focused on gene expression rather than gene structure, especially sparse are studies regarding to alternative splicing (AS). In modern life science research, primary hepatocytes culture technology facilitates such studies, which can accurately imitate liver activity in vitro and show unique superiority. Whereas limitations to traditional hepatocytes culture technology exist in terms of efficiency and operability. This study pursued an optimization culture method for duck primary hepatocytes to explore AS in choline-deficient model. Methods: We performed an optimization culture method for duck primary hepatocytes with multi-step digestion procedure from Pekin duck embryos. Subsequently a NAFLD model was constructed with choline-free medium. RNA-seq and further analysis by rMATS were performed to identify AS events alterations in choline-deficency duck primary hepatocytes. Results: The results showed E13 (embryonic day 13) to E15 is suitable to obtain hepatocytes, and the viability reached over 95% by trypan blue exclusion assay. Primary hepatocyte retained their biological function as well identified by Periodic Acid-Schiff staining method and Glucose-6-phosphate dehydrogenase activity assay, respectively. Meanwhile, genes of alb and afp and specific protein of albumin were detected to verify cultured hepatocytes. Immunofluorescence was used to evaluate purity of hepatocytes, presenting up to 90%. On this base, choline-deficient model was constructed and displayed significantly increase of intracellular triglyceride and cholesterol as reported previously. Intriguingly, our data suggested that AS events in choline-deficient model were implicated in pivotal biological processes as an aberrant transcriptional regulator, of which 16 genes were involved in lipid metabolism and highly enriched in glycerophospholipid metabolism. Conclusion: An effective and rapid protocol for obtaining duck primary hepatocytes was established, by which our findings manifested choline deficiency could induce the accumulation of lipid and result in aberrant AS events in hepatocytes, providing a novel insight into various AS in the metabolism role of choline.