• The Journal of the Society of Korean Medicine Diagnostics
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• v.16 no.3
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• pp.23-32
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• 2012

Objectives: The objective of this study is to investigate and valuate currently provided database management systems (DBMS) of traditional Korean medical knowledge. Methods: We searched DBMS on the web and smart device application markets (Apple App Store and Google Play Store). Key words for searching were 'traditional medicine', 'acupuncture', 'moxibustion', 'herbal medicine', and '한의학'. We looked into each DBMS to find out its scopes and limits, and each was valuated according to its functionality, accessibility, and utility. Results: 186 DBMS of traditional Korean medical knowledge were investigated and 91% of them were applications for smart devices. Almost all DBMS provided acupuncture and herb information, and a small amount of DMBS provided prescription and research paper information. Functionality, accessibility, and utility valuation were performed by using scoring system from 0 to 2. Mean values of functionality, accessibility, and utility were 0.86, 1.29, and 1.09. Conclusions: On the whole, high accessibility and low functionality were found, and various data-calculating functions were not implemented. Further researches and developments about traditional Korean medical knowledge DBMS are necessary to provide correct traditional Korean medical information and to support the studies about Korean medicine.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.1
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• pp.195-203
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• 2019

Mobile applications stores such as Google Play Store and Apple App Store, are widely used to distribute a variety of applications including finance, shopping, and entertainment. Recently, however, vulnerabilities of the mobile applications are likely to violate users' privacy such as personal information leakage. In this paper, we classify mobile applications that can be download from mobile stores, and analyze the personal information that could be leaked when users are using the mobile applications. As a result of analysis, we found that personal information are leaked in some widely used mobile applications in practice. On the basis of our experiment results, we propose some mitigations to enhance security of the mobile applications and prevent leakage of personal information.

• The Journal of Society for e-Business Studies
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• v.25 no.3
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• pp.15-40
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• 2020

Due to the explosive growth of mobile application services, categorizing mobile application services is in need in practice from both customers' and developers' perspectives. Despite the fact, however, there have been limited studies regarding systematic categorization of mobile application services. In response, this study proposed a method for categorizing mobile application services, and suggested a service taxonomy based on the network clustering results. Total of 1,607 mobile healthcare services are collected through the Google Play store. The network analysis is conducted based on the similarity of descriptions in each application service. Modularity detection analysis is conducted to detects communities in the network, and service taxonomy is derived based on each cluster. This study is expected to provide a systematic approach to the service categorization, which is helpful to both customers who want to navigate mobile application service in a systematic manner and developers who desire to analyze the trend of mobile application services.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.23 no.6
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• pp.1017-1023
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• 2013

With the growing use of smartphones, many secure applications are performed on smartphones such as banking, payment, authentication. To provide security services, cryptographic algorithms are performed on smartphones' CPU. However, smartphone's CPU has no considerations against side-channel attacks including Electromagnetic Analysis (EMA). In DesignCon 2012, G. Kenworthy introduced the risk of cryptographic algorithms operated on smartphone against EMA. In this paper, using improved experimental setups, we performed EMA experiments on androin smartphones' commercial secure applications. As a result, we show that the weakness of real application. According to the experimental setups, we picked up the operation of w-NAF scalar multiplication from the operation of Google's Play Store application using radiated EM signal. Also, we distinguished scalar values (0 or not) of w-NAF scalar multiplication.

• 한국어정보학회:학술대회논문집
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• 2016.10a
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• pp.291-295
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• 2016

본 논문에서는 한국어 기본 유니트 단위로 WPM을 활용한 구글 플레이 스토어 앱의 댓글 감정분석을 수행하였다. 먼저 자동 띄어쓰기 시스템을 적용한 후, 어절단위, 형태소 분석기, WPM을 각각 적용하여 모델을 생성하고, 로지스틱 회귀(Logistic Regression), 소프트맥스 회귀(Softmax Regression), 서포트 벡터머신(Support Vector Machine, SVM)등의 알고리즘을 이용하여 댓글 감정(긍정과 부정)을 비교 분석하였다. 그 결과 어절단위, 형태소 분석기보다 WPM이 최대 25%의 향상된 결과를 얻었다. 또한 분류 과정에서 로지스틱회귀, 소프트맥스 회귀보다는 SVM 성능이 우수했으며, SVM의 기본 파라미터({'kernel':('linear'), 'c':[4]})보다 최적의 파라미터를 적용({'kernel': ('linear','rbf', 'sigmoid', 'poly'), 'C':[0.01, 0.1, 1.4.5]} 하였을 때, 최대 91%의 성능이 나타났다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4856-4872
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• 2018

The mobile internet has grown ubiquitous across the globe with the widespread use of smart devices. However, the designs of modern mobile operating systems and their applications limit content retrieval with mobile applications. The mobile internet is not as accessible as the traditional web, having more man-made restrictions and lacking a unified approach for crawling and content retrieval. In this study, we propose an automatic and scalable mobile application content crawler, which can recognize the interaction paths of mobile applications, representing them as interaction graphs and automatically collecting content according to the graphs in a parallel manner. The crawler was verified by retrieving content from 50 non-game applications from the Google Play Store using the Android platform. The experiment showed the efficiency and scalability potential of our crawler for large-scale mobile internet content retrieval.

• Annual Conference on Human and Language Technology
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• 2016.10a
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• pp.291-295
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• 2016

본 논문에서는 한국어 기본 유니트 단위로 WPM을 활용한 구글 플레이 스토어 앱의 댓글 감정분석을 수행하였다. 먼저 자동 띄어쓰기 시스템을 적용한 후, 어절단위, 형태소 분석기, WPM을 각각 적용하여 모델을 생성하고, 로지스틱 회귀(Logistic Regression), 소프트맥스 회귀(Softmax Regression), 서포트 벡터머신(Support Vector Machine, SVM)등의 알고리즘을 이용하여 댓글 감정(긍정과 부정)을 비교 분석하였다. 그 결과 어절단위, 형태소 분석기보다 WPM이 최대 25%의 향상된 결과를 얻었다. 또한 분류 과정에서 로지스틱회귀, 소프트맥스 회귀보다는 SVM 성능이 우수했으며, SVM의 기본 파라미터({'kernel':('linear'), 'c':[4]})보다 최적의 파라미터를 적용({'kernel': ('linear','rbf', 'sigmoid', 'poly'), 'C':[0.01, 0.1, 1.4.5]} 하였을 때, 최대 91%의 성능이 나타났다.

• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.1-14
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• 2019

Because of the characteristics of game software, it is important to quickly identify and reflect users' needs into game software after its launch. However, most sites such as the Google Play Store, where users can download games and post reviews, provide only very limited and ambiguous classification categories for game reviews. Therefore, in this paper, we develop an automatic classification system for game reviews that categorizes reviews into categories that are clearer and more useful for game providers. The developed system converts words in reviews into vectors using word2vec, which is a representative word embedding model, and classifies reviews into the most relevant categories by measuring the similarity between those vectors and each category. Especially, in order to choose the best similarity measure that directly affects the classification performance of the system, we have compared the performance of three representative similarity measures, the Euclidean similarity, cosine similarity, and the extended Jaccard similarity, in a real environment. Furthermore, to allow a review to be classified into multiple categories, we use a threshold-based multi-category classification method. Through experiments on real reviews collected from Google Play Store, we have confirmed that the system achieved up to 95% accuracy.

• Smart Media Journal
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• v.10 no.2
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• pp.76-83
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• 2021

With the development and use of smart devices such as smartphones and tablets increases, the mobile application market based on mobile devices is growing rapidly. Mobile application users write reviews to share their experience in using the application, which can identify consumers' various needs and application developers can receive useful feedback on improving the application through reviews written by consumers. However, there is a need to come up with measures to minimize the amount of time and expense that consumers have to pay to manually analyze the large amount of reviews they leave. In this work, we propose to collect delivery application user reviews from Google PlayStore and then use machine learning and deep learning techniques to classify them into four categories like application feature advantages, disadvantages, feature improvement requests and bug report. In the case of the performance of the Hugging Face's pretrained BERT-based Transformer model, the f1 score values for the above four categories were 0.93, 0.51, 0.76, and 0.83, respectively, showing superior performance than LSTM and GRU.

• Journal of Distribution Science
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• v.11 no.2
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• pp.45-55
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• 2013

Purpose - The development and implementation of OSS (Open Source Software) led to a dramatic change in corporate IT infrastructure, from system server to smart phone, because the performance, reliability, and security functions of OSS are comparable to those of commercial software. Today, OSS has become an indispensable tool to cope with the competitive business environment and the constantly-evolving IT environment. However, the use of OSS is insufficient in small and medium-sized companies and software houses. This study examines the need for OSS Intermediaries in the Software Distribution Channel. It is expected that the role of the OSS Intermediary will be reduced with the improvement of the distribution process. The purpose of this research is to prove that OSS Intermediaries increase the efficiency of the software distribution market. Research design, Data, and Methodology - This study presents the analysis of data gathered online to determine the extent of the impact of the intermediaries on the OSS market. Data was collected using an online survey, conducted by building a personal search robot (web crawler). The survey period lasted 9 days during which a total of 233,021 data points were gathered from sourceforge.net and Apple's App store, the two most popular software intermediaries in the world. The data collected was analyzed using Google's Motion Chart. Results - The study found that, beginning 2006, the production of OSS in the Sourceforge.net increased rapidly across the board, but in the second half of 2009, it dropped sharply. There are many events that can explain this causality; however, we found an appropriate event to explain the effect. It was seen that during the same period of time, the monthly production of OSS in the App store was increasing quickly. The App store showed a contrasting trend to software production. Our follow-up analysis suggests that appropriate intermediaries like App store can enlarge the OSS market. The increase was caused by the appearance of B2C software intermediaries like App store. The results imply that OSS intermediaries can accelerate OSS software distribution, while development of a better online market is critical for corporate users. Conclusion - In this study, we analyzed 233,021 data points on the online software marketplace at Sourceforge.net. It indicates that OSS Intermediaries are needed in the software distribution market for its vitality. It is also critical that OSS intermediaries should satisfy certain qualifications to play a key role as market makers. This study has several interesting implications. One implication of this research is that the OSS intermediary should make an effort to create a complementary relationship between OSS and Proprietary Software. The second implication is that the OSS intermediary must possess a business model that shares the benefits with all the participants (developer, intermediary, and users).The third implication is that the intermediary provides an OSS of high quality like proprietary software with a high level of complexity. Thus, it is worthwhile to examine this study, which proves that the open source software intermediaries are essential in the software distribution channel.