• Journal of the Korea Institute of Information Security & Cryptology
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• v.31 no.3
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• pp.433-441
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• 2021

Android applications are released across various categories, including productivity apps and games, and users are exposed to various applications and even malware depending on their usage patterns. On the other hand, most analysis engines train using existing datasets and do not reflect user patterns even if periodic updates are made. Thus, the detection rate for known malware is high, while types of malware such as adware are difficult to detect. In addition, existing engines incur increased service provider costs due to the cost of server farm, and the user layer suffers from problems where availability and real-timeness are not guaranteed. To address these problems, we propose an analysis system that performs on-device malware detection through transfer learning, which requires only one-time communication with the server. In addition, The system has a complete process on the device, including decompiler, which can distribute the load of the server system. As an evaluation result, it shows 90.3% accuracy without transfer learning, while the model transferred with adware catergories shows 95.1% of accuracy, which is 4.8% higher compare to original model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.6
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• pp.907-913
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• 2022

Approximately 70% of smartphone users around the world use Android operating system-based smartphones, and malicious apps targeting these Android platforms are constantly increasing. Google has provided "Google Play Protect" to respond to the increasing number of Android targeted malware, preventing malicious apps from being installed on smartphones, but many malicious apps are still normal. It threatens the smartphones of ordinary users registered in the Google Play store by disguising themselves as apps. However, most people rely on antivirus programs to detect malicious apps because the average user needs a great deal of expertise to check for malicious apps. Therefore, in this paper, we propose a method to classify unnecessary malicious permissions of apps by using only the categories and permissions that can be easily confirmed by the app, and to easily detect malicious apps through the classified permissions. The proposed method is compared and analyzed from the viewpoint of undiscovered rate and false positives with the "commercial malicious application detection program", and the performance level is presented.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.1-9
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• 2022

UML class diagrams are used to visualize the static aspects of a software system and are involved from analysis and design to documentation and testing. Software modeling using class diagrams is essential for software development, but it may be not an easy activity for inexperienced modelers. The modeling productivity could be improved with a dataset of class diagrams which are classified by domain categories. To this end, this paper provides a classification method for a dataset of class diagram images. First, real class diagrams are selected from collected images. Then, class names are extracted from the real class diagram images and the class diagram images are classified according to domain categories. The proposed classification model has achieved 100.00%, 95.59%, 97.74%, and 97.77% in precision, recall, F1-score, and accuracy, respectively. The accuracy scores for the domain categorization are distributed between 81.1% and 95.2%. Although the number of class diagram images in the experiment is not large enough, the experimental results indicate that it is worth considering the proposed approach to class diagram image classification.

• Journal of Convergence for Information Technology
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• v.12 no.5
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• pp.92-99
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• 2022

This study show the empirical results and the models that explain the content creator's personal brand value in the user-generated content platform. Producer's brand value performance could have enhancement and dilution by their activities for the long-term and repetitive change. Therefore, for the measure and analysis, the models have to catch the effect from producer's the diverse activities. This study would find the guideline by competitive analysis between (1) the impact of in-group user's self-motivated participation and (2) the impact of the social links from the outside platform. Based on the analysis results, producer's creation activity as focused on the specific and professional category increase their brand value for the long-term. However, producers would have to upload diverse category, after users are bored to their similar videos' as before. These empirical results would be a guidelines to the content management strategies for producers and the platform.

• Journal of the Korean Society for Library and Information Science
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• v.57 no.3
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• pp.279-301
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• 2023

The data-driven research environment is rapidly changing. Accordingly, domestic university libraries are also preparing to establish and operate research data management services to support university researchers. This study was designed to propose a research data management service to support researchers in science and technology specialized university libraries. In order to propose the service, 11 universities specializing in science and technology were selected from overseas and domestic universities and their research data management services were analyzed. Key categories were derived from analysis results, research data management, electronic research notebooks, and RDM training. In particular, the 'research data management' category included DMP, data collection, data management, data preservation, data sharing and publishing, data reuse, infrastructure and tools. And it consists of RDM guides and policies. The results of this study will be helpful in introducing and operating research data management services in science and technology specialized university libraries.

• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.498-509
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• 2021

The wake turbulence at the wingtip of preceding aircraft may affect the normal operation of following aircraft. Aircraft are classified into four categories according to their maximum take-off weight, and horizontal separation is applied with this category matrix. The FAA and EUROCONTROL revealed that the magnitude and effect of preceding aircraft wake turbulence were smaller than the current distance separation minima suggest. This new information presents the opportunity for revising wake turbulence minima into seven categories (RECAT). This paper confirms the feasibility of implementing RECAT at major airports in South Korea using the draft of ICAO Doc. 10122. The paper also calculates the ultimate runway capacity of Incheon International Airport in South Korea using the Harris Model and comparatively analyzes the amount of runway capacity. As a result of the analysis, it was confirmed that the implementation of RECAT could increase the ultimate runway capacity of Incheon International Airport. This paper's calculation methods and results can be used as primary data for implementing RECAT in other airports.

• Smart Media Journal
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• v.13 no.4
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• pp.57-64
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• 2024

Attributes such as material, color, and fit in fashion images are important factors for consumers to purchase clothing. However, the process of classifying clothing attributes requires a large amount of manpower and is inconsistent because it relies on the subjective judgment of human operators. To alleviate this problem, there is a need for research that utilizes artificial intelligence to classify clothing attributes in fashion images. Previous studies have mainly focused on classifying clothing attributes for either tops or bottoms, so there is a limitation that the attributes of both tops and bottoms cannot be identified simultaneously in the case of full-body fashion images. In this study, we propose a deep learning model that can distinguish between tops and bottoms in fashion images and classify the category of each item and the attributes of the clothing material. The deep learning models ResNet and EfficientNet were used in this study, and the dataset used for training was 1,002,718 fashion images and 125 labels including clothing categories and material properties. Based on the weighted F1-Score, ResNet is 0.800 and EfficientNet is 0.781, with ResNet showing better performance.

• Journal of the Korean Society of Radiology
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• v.83 no.2
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• pp.331-343
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• 2022

Purpose To systematically compare the performance of liver imaging reporting and data system treatment response (LR-TR) with the modified Response Evaluation Criteria in Solid Tumors (mRECIST) for diagnosing viable hepatocellular carcinoma (HCC) treated with locoregional therapy (LRT). Materials and Methods Original studies of intra-individual comparisons between the diagnostic performance of LR-TR and mRECIST using dynamic contrast-enhanced CT or MRI were searched in MEDLINE and EMBASE, up to August 25, 2021. The reference standard for tumor viability was surgical pathology. The meta-analytic pooled sensitivity and specificity of the viable category using each criterion were calculated using a bivariate random-effects model and compared using bivariate meta-regression. Results For five eligible studies (430 patients with 631 treated observations), the pooled per-lesion sensitivities and specificities were 58% (95% confidence interval [CI], 45%-70%) and 93% (95% CI, 88%-96%) for the LR-TR viable category and 56% (95% CI, 42%-69%) and 86% (95% CI, 72%-94%) for the mRECIST viable category, respectively. The LR-TR viable category provided significantly higher pooled specificity (p < 0.01) than the mRECIST but comparable pooled sensitivity (p = 0.53). Conclusion The LR-TR algorithm demonstrated better specificity than mRECIST, without a significant difference in sensitivity for the diagnosis of pathologically viable HCC after LRT.

• Journal of the Korean Society for Library and Information Science
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• v.31 no.4
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• pp.187-214
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• 1997

This study surveys science information resources and science information resource services institutions existing in the field of scientific research to analyse their strengths and weaknesses, which provide theoretical foundation of planning an effective and efficient nation-wide science information transfer system for Korea. This study also suggests right directions of developing a centralised system of science information for Korea.

• 한국정보교육학회:학술대회논문집
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• 2004.08a
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• pp.381-388
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• 2004

가까운 미래의 가정에서는 여러 기능을 가진 로봇이 등장하여 사람과 늘기도 하고 집도 지키는 방범활동도 수행하는 등 공상과학영화 속의 일들이 현실로 다가올 것이다. 핵가족화와 맞벌이 부부의 증가로 어린이들이 혼자 집에 있는 시간이 많아졌으며 이에 불안을 느낀 학부모들은 자녀들을 학원 등의 사설교육기관에 맡겨 사교육비의 증가가 사회문제로 대두되고 있다. 그러나 가정에서 부모와 떨어져 있는 동안의 어린이의 생활을 보여주거나 학습을 도와주는 로봇이 가정에 있다면 부모들은 안심하고 직장생활에 더욱 충실하게 될 것이며 사교육비의 감소현상에도 기여할 수 있을 것이다. 이에 본 논문에서는 가정용 로봇이 서비스하게 될 기능들 중에 초등학교 어린이의 학습을 도울 수 있는 기능에 초점을 맞추어 설문을 통해 요구를 조사하고 분석하였으며 홈 로봇과 사용자간의 메뉴를 크게 일정관리, 학습관리, 학습, 메신저 등의 카테고리로 분류하여 각 항목별로 세부 서비스 내용을 체계화하였다. 로봇을 활용한 학습 도우미 시스템의 설계는 앞으로 무한한 부가가치를 창조하게 될 로봇산업의 발달과 새로운 형태의 교육을 가능하게 하는데 큰 기여를 하게 될 것이다.