• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.5
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• pp.1123-1129
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• 2015

Macro(automatic hunting) of mobile game is a program that touch the screen by defined rules like a game bot in PC online games, and it is used by make various ways like android application or windows application program. This gives honest users deprivation and make to lose their interest. Finally they would leave the game and gradually game life would be shorten. Although many studies to prevent these problems in PC online game are conducted, applying mobile game to PC's way is difficult because mobile games are limited to use the network and device performance is different with PC. In this paper, we propose a framework for macro detection by using the touch event information. A touch event on the mobile game is a necessary control command to the game. Because macro touches the screen with the same pattern, there is a difference between normal user's behavior and macro's operation. In mobile games that casual games are mostly, Touch event is the best difference that identify normal user against macro for a short period of time. As a result of detecting macros used in real mobile game by using the proposed framework it showed 100% accuracy and 0% false positive rate.

• Journal of Chest Surgery
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• v.31 no.3
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• pp.271-278
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• 1998

In order to access the value of computed tomography in mediastinal LN staging of NSCLCa, 581 LN stations of 77 patients were selected from 552 patients who were diagnosed as Lung Ca and operated in Seoul National University Hospital from 1992 to 1995. The selection criteria were as follows ; the patients 1) whose preoperative chest CTs were available; 2) underwent curative resection (lobectomy or more) with complete lymph node dissection; 3) whose final pathologic diagnosis were proven to be non-small cell lung cancer. We adopted Receiver Operating Characteristic curve method to determine a proper size criterion for diagnosing malignant mediastinal adenopathy. From curve analysis, we decided the size criterion of lymph node to 1 cm in their short axis. Using this size criterion, it's sensitivity was 43.9%, specificity was 87.4%, and accuracy was 83.1%. Eventhough we couldn't determine the precise size criterion for the adenoca, it seemed that shorter than 1 cm size criterion should be applied in that particular cell type. Lymph node stations associated with the tuberculosis or bronchiectasis tend to be overestimated in nodal staging and have relatively high false positive rate. The low sensitivity of CT scan suggest that radical and complete dissection or precise mediastinal lymph node evaluation through the surgical approach is mandatory.

• Journal of Broadcast Engineering
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• v.17 no.3
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• pp.519-528
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• 2012

Voice activity detection (VAD) is generally conducted by extracting features from the acoustic signal and a decision rule. The performance of such VAD algorithms driven by the input acoustic signal highly depends on the acoustic noise. When video signals are available as well, the performance of VAD can be enhanced by using the visual information which is not affected by the acoustic noise. Previous visual VAD algorithms usually use single visual feature to detect the lip activity, such as active appearance models, optical flow or intensity variation. Based on the analysis of the weakness of each feature, we propose to combine intensity change measure and the optical flow in the mouth region, which can compensate for each other's weakness. In order to minimize the computational complexity, we develop simple measures that avoid statistical estimation or modeling. Specifically, the optical flow is the averaged motion vector of some grid regions and the intensity variation is detected by simple thresholding. To extract the mouth region, we propose a simple algorithm which first detects two eyes and uses the profile of intensity to detect the center of mouth. Experiments show that the proposed combination of two simple measures show higher detection rates for the given false positive rate than the methods that use a single feature.

• Journal of Korea Multimedia Society
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• v.14 no.10
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• pp.1252-1261
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• 2011

In this paper, we propose the comparison method of pattern similarity for video segmentation algorithm. The shot boundary type is categorized as 2 types, abrupt change and gradual change. The representative examples of gradual change are dissolve, fade-in, fade-out or wipe transition. The proposed method consider the problem to detect shot boundary as 2-class problem. We concentrated if the shot boundary event happens or not. It is essential to define similarity between frames for shot boundary detection. We proposed 2 similarity measures, within similarity and between similarity. The within similarity is defined by feature comparison between frames belong to same shot. The between similarity is defined by feature comparison between frames belong to different scene. Finally we calculated the statistical patterns comparison between the within similarity and between similarity. Because this measure is robust to flash light or object movement, our proposed algorithm make contribution towards reducing false positive rate. We employed color histogram and mean of sub-block on frame image as frame feature. We performed the experimental evaluation with video dataset including set of TREC-2001 and TREC-2002. The proposed algorithm shows the performance, 91.84% recall and 86.43% precision in experimental circumstance.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.3
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• pp.2059-2065
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• 2013

This goal of this research was to evaluate the cost-effectiveness of the National Cancer Screening Program (NCSP) for breast cancer in the Republic of Korea from a government expenditure perspective. In 2002-2003 (baseline), a total of 8,724,860 women aged 40 years or over were invited to attend breast cancer screening by the NCSP. Those who attended were identified using the NCSP database, and women were divided into two groups, women who attended screening at baseline (screened group) and those who did not (non-screened group). Breast cancer diagnosis in both groups at baseline, and during 5-year follow-up was identified using the Korean Central Cancer Registry. The effectiveness of the NCSP for breast cancer was estimated by comparing 5-year survival and life years saved (LYS) between the screened and the unscreened groups, measured using mortality data from the Korean National Health Insurance Corporation and the National Health Statistical Office. Direct screening costs, indirect screening costs, and productivity costs were considered in different combinations in the model. When all three of these costs were considered together, the incremental cost to save one life year of a breast cancer patient was 42,305,000 Korean Won (KW) (1 USD=1,088 KW) for the screened group compared to the non-screened group. In sensitivity analyses, reducing the false-positive rate of the screening program by half was the most cost-effective (incremental cost-effectiveness ratio, ICER=30,110,852 KW/LYS) strategy. When the upper age limit for screening was set at 70 years, it became more cost-effective (ICER=39,641,823 KW/LYS) than when no upper age limit was set. The NCSP for breast cancer in Korea seems to be accepted as cost-effective as ICER estimates were around the Gross Domestic Product. However, cost-effectiveness could be further improved by increasing the sensitivity of breast cancer screening and by setting appropriate age limits.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.24 no.4
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• pp.739-751
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• 2014

We are using our smartphone for our business as well as ours lives. Thus, user's privacy data and a company secret are stored at smartphone. By the way, the saved data on smartphone database can be exposed to a malicous attacker when a malicous app is installed in the smartphone or a user lose his/her smartphone because all data are stored as form of plaintext in the database. To prevent this disclosure of personal information, we need a database encryption method. However, if a database is encrypted, it causes of declining the performance. For example, when we search specific data in condition with encrypted database, we should decrypt all data stored in the database or search sequentially the data we want with accompanying overhead[1]. In this paper, we propose an efficient and searchable encryption method using variable length bloom filter under limited resource circumstances(e.g., a smartphone). We compare with existing searchable symmetric encryption. Also, we implemented the proposed method in android smartphone and evaluated the performance the proposed method. As a result through the implementation, We can confirm that our method has over a 50% improvement in the search speed compared to the simple search method about encrypted database and has over a 70% space saving compared to the method of fixed length bloom filter with the same false positive rate.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.125-141
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• 2012

These days, the malicious attacks and hacks on the networked systems are dramatically increasing, and the patterns of them are changing rapidly. Consequently, it becomes more important to appropriately handle these malicious attacks and hacks, and there exist sufficient interests and demand in effective network security systems just like intrusion detection systems. Intrusion detection systems are the network security systems for detecting, identifying and responding to unauthorized or abnormal activities appropriately. Conventional intrusion detection systems have generally been designed using the experts' implicit knowledge on the network intrusions or the hackers' abnormal behaviors. However, they cannot handle new or unknown patterns of the network attacks, although they perform very well under the normal situation. As a result, recent studies on intrusion detection systems use artificial intelligence techniques, which can proactively respond to the unknown threats. For a long time, researchers have adopted and tested various kinds of artificial intelligence techniques such as artificial neural networks, decision trees, and support vector machines to detect intrusions on the network. However, most of them have just applied these techniques singularly, even though combining the techniques may lead to better detection. With this reason, we propose a new integrated model for intrusion detection. Our model is designed to combine prediction results of four different binary classification models-logistic regression (LOGIT), decision trees (DT), artificial neural networks (ANN), and support vector machines (SVM), which may be complementary to each other. As a tool for finding optimal combining weights, genetic algorithms (GA) are used. Our proposed model is designed to be built in two steps. At the first step, the optimal integration model whose prediction error (i.e. erroneous classification rate) is the least is generated. After that, in the second step, it explores the optimal classification threshold for determining intrusions, which minimizes the total misclassification cost. To calculate the total misclassification cost of intrusion detection system, we need to understand its asymmetric error cost scheme. Generally, there are two common forms of errors in intrusion detection. The first error type is the False-Positive Error (FPE). In the case of FPE, the wrong judgment on it may result in the unnecessary fixation. The second error type is the False-Negative Error (FNE) that mainly misjudges the malware of the program as normal. Compared to FPE, FNE is more fatal. Thus, total misclassification cost is more affected by FNE rather than FPE. To validate the practical applicability of our model, we applied it to the real-world dataset for network intrusion detection. The experimental dataset was collected from the IDS sensor of an official institution in Korea from January to June 2010. We collected 15,000 log data in total, and selected 10,000 samples from them by using random sampling method. Also, we compared the results from our model with the results from single techniques to confirm the superiority of the proposed model. LOGIT and DT was experimented using PASW Statistics v18.0, and ANN was experimented using Neuroshell R4.0. For SVM, LIBSVM v2.90-a freeware for training SVM classifier-was used. Empirical results showed that our proposed model based on GA outperformed all the other comparative models in detecting network intrusions from the accuracy perspective. They also showed that the proposed model outperformed all the other comparative models in the total misclassification cost perspective. Consequently, it is expected that our study may contribute to build cost-effective intelligent intrusion detection systems.

• The Korean Journal of Cytopathology
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• v.3 no.1
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• pp.1-11
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• 1992

From the one hundred forty eight patients with evidence of biliary tract obstruction, 275 bile samples were obtained from percutaneously placed biliary drainage catheters. Of the 148 patients, ova of Clonorchis sinensis were demonstrated in 17 patients (11.5%), with the epithelial cells. Among them, one case also demonstrated coexisting adenocarcinoma. In 105 patients, the medical records were available for review and the clinical diagnoses were malignancy in 99 patients and benign lesion in 6 patients. Of the 99 patients in which clinico-radiologic diagnosis were malignant, cytologic results were positive in 23.2%. Dividing the patients Into two groups, the ones with tumor of bile duct origin (group I) and the others with tumors producing extrinsic compression of bile duct, such as periampullary carcinoma, pancreas head carcinoma or metastatic carcinoma in lymph nodes from tumors of adjacent organs (group II), the cytologic results were positive in 37% and 11.6%, respectively. In patients with histologic confirmation, the positive correlation was found in 50% and 20% in group I and group II, respectively, with remarkable difference between two groups. There were no false positives in cytologic diangosis. The overall concordance rate of cytologic diagnosis with diagnosis of clinical investigation in both benign and malignant lesions was 27.6% and the diagnostic specificity was 100%.

• Childhood Kidney Diseases
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• v.5 no.2
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• pp.156-163
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• 2001

Purpose We performed urinary mass screening(UMS) program for 2,804 children of second grade elemantary school 8 years of age in Paju city with cooperation of Paju City Health Center to determine the prevalence of asymptomatic proteinuria and hematuria, and to estimate the risk of incipient renal diseases. Also we attempted to evaluate the significance of hematuria in UMS in addidtion to proteinuria. Methods : 2,804 children of the 2nd grade of elementary school who lived in Paju city were included to our UMS program in 2000. They were constituted with 1,428 boys and 1,376 girls. The screening program was carried out in 3 steps The 1st screenig test was performed at schools and then students with abnormal results were examined repeatedly at Paju City Health Center and our hospital. Those students who showed proteinuria and/or hematuria in the 1st and 2nd test were referred to our hospital to undertake the 3rd close examination including physical examination, laboratory tests and radiologic tests. Results : (I) The prevalence of urinary abnormality in the 1st screening test was $8.3\%$(233 students), comprised of $5.9\%$ of boys, $10.8\%$ of girls. (2) Among 2,804 children tested in the first screening, prevalences of asymptomatic proteinuria and isolated hematuria were 64($2.3\%$), 163($5.8\%$) respectively, and the prevalence of proteinuria with hematuria was 6($0.2\%$). (3) Among 233 students with urinary abnormalities at the 1st screening test, 102 students applied to the 2nd test. 32 children, about one third of them, were also found to have abnormal urinary findings ; isolated hematuria 30, proteinuria with hematuria 2. (4) Those findings of clinical evaluation for children with isolated hematuria at the hospital showed as follows: idiopathic isolated microscopic hematuria 21, normal 6, urinary tract infection 1, idiopathic hypercalciuria 1 and simple renal cyst 1. Those 2 students with proteinuria and hematuria seemed to have chronic glomerulonephritis. Conclusion : (1) The clinical evaluation for children who showed positive results at the 1st screening test should be done judiciously. Because of high false positive rate, almost who showed positive results was normal, only a few of them had pathologic conditions. In this study, actual incidence of incipient renal diseases in children of 8 year old was calculated to be $0.4\%$. (2) The definite conclusion whether a urinary mass screening test can alter the prognosis of incipient renal diseases could not be drawn with this study. Further study must be necessary (3) We could acknowledge the significance of hematuria in UMS, but it is necessary that one should be judicious in managing and follow-up those that show abnormal results. (J Korean Soc Pediatr Nephrol 2001;5 : 156-63)

• Journal of Korea Water Resources Association
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• v.57 no.3
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• pp.181-193
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• 2024

The increasing atmospheric imbalance caused by climate change leads to an elevation in precipitation, resulting in a heightened frequency of flooding. Consequently, there is a growing need for technology to detect and monitor these occurrences, especially as the frequency of flooding events rises. To minimize flood damage, continuous monitoring is essential, and flood areas can be detected by the Synthetic Aperture Radar (SAR) imagery, which is not affected by climate conditions. The observed data undergoes a preprocessing step, utilizing a median filter to reduce noise. Classification techniques were employed to classify water bodies and non-water bodies, with the aim of evaluating the effectiveness of each method in flood detection. In this study, the Otsu method and Support Vector Machine (SVM) technique were utilized for the classification of water bodies and non-water bodies. The overall performance of the models was assessed using a Confusion Matrix. The suitability of flood detection was evaluated by comparing the Otsu method, an optimal threshold-based classifier, with SVM, a machine learning technique that minimizes misclassifications through training. The Otsu method demonstrated suitability in delineating boundaries between water and non-water bodies but exhibited a higher rate of misclassifications due to the influence of mixed substances. Conversely, the use of SVM resulted in a lower false positive rate and proved less sensitive to mixed substances. Consequently, SVM exhibited higher accuracy under conditions excluding flooding. While the Otsu method showed slightly higher accuracy in flood conditions compared to SVM, the difference in accuracy was less than 5% (Otsu: 0.93, SVM: 0.90). However, in pre-flooding and post-flooding conditions, the accuracy difference was more than 15%, indicating that SVM is more suitable for water body and flood detection (Otsu: 0.77, SVM: 0.92). Based on the findings of this study, it is anticipated that more accurate detection of water bodies and floods could contribute to minimizing flood-related damages and losses.