• Annual Conference of KIPS
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• 2024.05a
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• pp.53-56
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• 2024

Though lots of approaches to develop mobile apps are suggested up to now, developers have difficulties selecting a right one. This study compares native and cross-platform application development approaches, particularly focusing on the shift in preference from Java to Kotlin and the increasing use of Flutter. This research offers practical insights into factors influencing developers' choice of programming languages and frameworks in mobile application development by creating identical applications using Java, Kotlin, and Dart (Flutter). Furthermore, this study explores the best practices for development by examining the quality of code in 45 open-source GitHub repositories. The study evaluates LOC and code smells using semi-automated SonarQube assessments to determine the effects of selecting a specific language or framework on code maintainability and development efficiency. Preliminary findings show differences in the quality of the code produced by the two approaches, offering developers useful information on how to best optimize language and framework selection to reduce code smells and improve project maintainability.

• International journal of advanced smart convergence
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• v.13 no.3
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• pp.176-182
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• 2024

Looking at the recent game market, classic games released in the past are being re-released with high-quality visuals, and users are generally satisfied. It can be said that the realization of realistic digital actors, which was not possible in the past, is now becoming a reality. Epic Games launched the MetaHuman Creator website in September 2021, allowing anyone to easily create realistic human characters. Since then, the number of animations created using MetaHumans has been increasing. As the characters become more realistic, the movement and expression animations expected by the audience must also be convincingly realized. Until recently, traditional methods were the primary approach for producing realistic character animations. For facial animation, Epic Games introduced an improved method on the Live Link app in 2023, which provides the highest quality among mobile-based techniques. In this context, this paper compares the results of animation produced using both keyframe facial capture and mobile-based capture. After creating an emotional expression animation with four sentences, the results were compared using Unreal Engine. While the facial capture method is more natural and easier to use, the precise and exaggerated expressions possible with the keyframe method cannot be overlooked, suggesting that a hybrid approach using both methods will likely continue for the foreseeable future.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.343-352
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• 2021

A convergence study was conducted to develop a smartphone application for self-practice of core nursing skills and evaluate its usefulness for undergraduate nursing students. Mobile Application Rating Scale and seven essay questionnaire were used to for usability evaluation among 22 undergraduate nursing students. The score of the information domain was the highest with 4.19(SD 0.79). The subjective quality domain showed the lowest score of 3.08(SD 0.87). Participants' performance confidence score was 8.23(SD 1.60), and learning satisfaction score was 7.89(SD 0.87). Participants reported that the convenience and repetitive self-learning were the strengths of the app. In addition, design and technical supplementation, and lecturer-feedback would improve effectiveness of the current educational app. Findings of this convergent study would be helpful to promote the application of mobile apps for effective self-learning of core nursing skills in undergraduate nursing education. Future resesarch is needed to examine effectiveness study of mobile app on the performance of core nursing skills.

• Proceedings of the Korea Database Society Conference
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• 2010.06a
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• pp.223-234
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• 2010

This study is aimed at proposing a new research model in which application store intention to acceptance influence factors (i.e.. facilitating condition, mobile computing self-efficacy, service compatibility, and system quality) affect TAM (i.e., perceived usefulness and perceived ease of use) positively, leading to application store intention to acceptance eventually. This study developed a research model to explain the use of Apple's App Store, and collected 228 survey responses from the undergraduate students who had experiences with such application store services as game and personal information management application download. To prove the validity of the proposed research model, PLS analysis is applied with valid 228 questionnaires. By employing PLS technique, the measurement reliability and validity of research variables are tested and the path analysis is conducted to do the hypothesis testing. In brief, the finding of this study suggests that application store intention to acceptance influence factors affect TAM positively, and application store intention to acceptance as well.

• Journal of Information Technology Applications and Management
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• v.24 no.1
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• pp.111-128
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• 2017

Existing studies for recommender have focused on recommending an appropriate item based on the customer preference. However, it has not yet been studied actively to recommend purchase timing for the repurchase product despite of its importance. This study aims to propose MLP and RNN models based on the only simple purchase history data to predict the timing of customer repurchase and compare performances in the perspective of prediction accuracy and quality. As an experiment result, RNN model showed outstanding performance compared to MLP model. The proposed model can be used to develop CRM system which can offer SMS or app based promotion to the customer at the right time. This model also can be used to increase sales for repurchase product business by balancing the level of order as well as inducing repurchase of customer.

• Journal of Information Technology Applications and Management
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• v.25 no.2
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• pp.73-90
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• 2018

Despite of increasing studies for product recommendation, the recommendation of product repurchase timing has not yet been studied actively. This study aims to propose deep neural network models usingsimple purchase history data to predict the repurchase timing of each customer and compare performances of the models from the perspective of prediction quality, including expected ROI of promotion, variability of precision and recall, and diversity of target selection for promotion. As an experiment result, a recurrent neural network (RNN) model showed higher promotion ROI and the smaller variability compared to MLP and other models. The proposed model can be used to develop a CRM system that can offer SMS or app-based promotionsto the customer at the right time. This model can also be used to increase sales for product repurchase businesses by balancing the level of ordersas well as inducing repurchases by customers.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.3
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• pp.479-491
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• 2024

Android manufacturers collect diagnostic data to improve the quality of service to users around the world. The content and frequency of diagnostic data collected by these Android manufacturers is unknown. We analyze the diagnostic data collection behavior of Samsung smartphones, which has the largest share of the Android market among smartphone manufacturers, to explain which diagnostic data is communicated to the server via network packets, how the system app that collects the diagnostic data works, and whether the diagnostic data violates user privacy.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• Journal of Food Hygiene and Safety
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• v.16 no.2
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• pp.96-102
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• 2001

Bacteriological quality of beef carcass and distributions of pathogens in beef processing environments were investigated to improve the hygienic quality of fresh beef. Total bacterial contamination of carcass surface in slaughtering process and cutting board in cut-meat process showed 10$^{5}$ -10$^{6}$ CFU/$\textrm{cm}^2$ and 10$^{5}$ CFU/$\textrm{cm}^2$ in summer, respectively. The viable bacterial count of cotton glove was similar to that of cutting board during and entire period of year. Microbial contamination of carcass surface, cutting board, cotton glove and deboned meat showed the highest in summer and the lowest in winter during the year. Escherichia coli O157, Pseudomonas aeruginosa, Klebsiella. ornithinolytica, Staphylococcus aureus, E coli, Tatumella. ptyseos, Serratia odorifera, Aero-monas sobria, Enterobacter cloacae and Flavimonas oryzihabitans were isolated from carcass surface during slaughter treatments. S. aureus, Listeria grayi and L. monocytogenes were isolated from cutting board and L. grayi, Erwinia spp. Salmonella app. and S. aureus were isolated from cotton glove in cut-meat process environments. Citrobacter freundii; L. monocytogenes; and S. aureus were isolated from deboned meat.

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.2
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• pp.397-404
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• 2009

For standardization of LMK02 quality, Ginsenoside Rg3 of Red Ginseng and Decursin of Angelica gigas Nakai in the constituents of LMK02 were estimated as indicative components. From LMK02 water extract, has been used in vitro test for its beneficial effects on neuronal survival and neuroprotective functions, particularly in connection with APP-related dementias and Alzheimer's disease (AD). $A{\beta}$ oligomer derived from proteolytic processing of the ${\beta}$-amyloid precursor protein (APP), including the amyloid-${\beta}$ peptide ($A{\beta}$), play a critical role in the pathogenesis of Alzheimer's dementia. We determined that oligomer amyloid-${\beta}$ ($A{\beta}$) have a profound attenuation in the increase in rat hippocampus H19-7 cells from. Experimental evidence indicates that LMK02 protects against neuronal damage from cells, but its cellular and molecular mechanisms remain unknown. Using a hippocampus cell line on $A{\beta}$ oligomer-induced neuronal cytotoxicity, we demonstrated that LMK02 inhibits formation of $A{\beta}$ oligomer, which are the behavior, and possibly causative, feature of AD. In the Red Ginseng, the average amounts of Ginsenoside Rg3 were $47.04{\mu}g/g$ and $42.3{\mu}g/g$, 90 % of its weight were set as a standard value. And, in the Angelica gigas Nakai, the average amounts of Decursin were 2.71 mg/g and 2.44mg/g, 90 % of its weight were also set as a standard value. The attenuated $A{\beta}$ oligomer in the presence of LMK02 was observed in the conditioned medium of this $A{\beta}$ oligomer-induced cells under in vitro. In the cells, LMK02 significantly activated antiapoptosis and decreased the production of ROS. These results suggest that neuronal damage in AD might be due to two factors: a direct $A{\beta}$ oligomer toxicity and multiple cellular and molecular neuroprotective mechanisms, including attenuation of apoptosis and direct inhibition of $A{\beta}$ oligomer, underlie the neuroprotective effects of LMK02 treatment.