• Journal of Biomedical Engineering Research
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• v.35 no.6
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• pp.219-226
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• 2014

This study is to develop a basic life support (BLS) app using the android based smartphone and to evaluate the function of the app. Suggested app contains chest compression feedback function, the map of automated external defibrillator (AED), direct emergency call and the basic knowledge of BLS. Using the accelerometer of the smartphone, we implemented a real-time algorithm that estimates the chest compression depth and rate for high quality cardiopulmonary resuscitation (CPR). The accuracy of algorithm was evaluated by manikin experiment. We made contents which were easy to learn the BLS for the layperson and implemented a function that provides the AED location information based on the user's current location. From the manikin experiment, the chest compression depth and rate were no significant differences between the manikin data and the app's feedback data (p > 0.05). Developed BLS app was uploaded on Google Play Store and it was free to download. We expected that this app is useful to learn the BLS for the layperson.

• Spatial Information Research
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• v.21 no.1
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• pp.1-13
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• 2013

This paper describes a GIS framework for geo-semantic information retrieval in mobile computing environments. We built geographic ontologies of POI (point of interest) and weather information for use in the combination of semantic, spatial, and temporal functions in a fully integrated database. We also implemented a geo-semantic app for Android-based smartphones that can extract more appropriate POIs in terms of user contexts and geographic ontologies and can visualize the POIs using Google Maps API (application programming interface). The feasibility tests showed our geo-semantic app can provide pertinent POI information according to mobile user contexts such as location, time, schedule, and weather. We can discover a baking CVS (convenience store) in the test of bakery search and can find out a drive-in theater for a not rainy day, which are good examples of the geo-semantic query using semantic, spatial, and temporal functions. As future work, we should need ontology-based inference systems and the LOD (linked open data) of various ontologies for more advanced sharing of geographic knowledge.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.201-203
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• 2019

Apps and games continue to grow in size as new content comes and compete on Google Play. As apps and games grow in size, app installs through the Google Play store are decreasing. The article talks about the structure and limitations of the existing support model, APK, and discusses the new support model, the Android App Bundle (AAB) structure. We will also look into future prospects.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1158-1159
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• 2013

It gives to users a difficulty for searching between this huge numbers of programs. Searching the best applications for our needs is a big challenge today. In this paper, we propose a study on collaborative filtering based app recommendation system. The proposed method is composed of three steps. In the first step, we extract the data set from the target website. In the second step, we parse the extracted raw data according to the types, and store in a database. In the third, we perform recommendations based on the stored data in database.

• Journal of Internet Computing and Services
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• v.20 no.4
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• pp.13-19
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• 2019

In order to register an application with Apple's App Store, it must pass a rigorous verification process through the Apple verification center. That's why spyware applications are difficult to get into the App Store. However, malicious code can also be executed through normal application vulnerabilities. To prevent such attacks, research is needed to detect and analyze early to patch potential vulnerabilities in applications. To prove a potential vulnerability, it is necessary to identify the root cause of the vulnerability and analyze the exploitability. A tool for analyzing iOS applications is the debugger named LLDB, which is built into Xcode, the development tool. There are various functions in the LLDB, and these functions are also available as APIs and are also available in Python. Therefore, in this paper, we propose a method to efficiently analyze potential vulnerabilities of iOS application by using LLDB API.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.2
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• pp.8-16
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• 2015

How to measure and evaluate the performance of managing a store? Although it is important for a retailer to execute good management in a store, there are few efficient measurement tools and methods for In-store management. Also few people are trying to deal with variety of goods (number of categories), depth of a catagory (number of stock-keeping units within a category), and stock level (the number of individual items of a particular SKU) in a store. To solve the problem, this paper suggests the Customer-Retailer Collaboration (CRC) model that utilizes Gamification. By embedding gaming elements, the store management activities can be viewed as more game-like processes. Customers find some problems they encountered in the store and send the related signals via mobile APP, and the relevant store personnel copes with the signals. As the return for their collaboration, they both will obtain points and badge. This paper designs the CRC model and shows the flow of the model briefly.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.12
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• pp.6105-6110
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• 2012

Smart devices have made our daily life more intelligent and provided convenient life styles in which advanced interface and high speed LTE enable us to have proliferation of empirical Apps. This research has analyzed and evaluated Unstructured data from the users who experienced 'Hair Style Magic Mirror App' by focusing on emotional information. Further this study suggests some directions that the application development industry should take in the near future. The findings through Chi-Square analysis indicate that the application has not affected the overall satisfaction of the consumers; consumers received higher satisfaction in paid version of applications than free versions. Emotional Information analysis shows that users value the practicality of each application the most. Thus, the application developers should focus on practicality and sensitivity side corresponding to the demand of consumers.

• The Journal of the Convergence on Culture Technology
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• v.3 no.1
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• pp.35-41
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• 2017

Smart phone based IT support programs are faced with difficulties due to the following reasons first, long development period is required as separate developments are necessary respectively depending on the operating system of Smart phone second, it is also difficult to secure high development cost for the outsourcing of the development. It is a big problem for improving app developing productivity. Smart App Factory which is suggested in this thesis is the business strategy to surpass the Android market of Google and App Store of Apple within short period of time based on App productivity of Smart App Authoring Tool and to accomplish the materialization of App market which is in the 1st global position and all potential customers who need programs for their works regardless of budget, scope, complexity and scale will be implemented by unfolding unprecedented low price policy and global online marketing activities for App development.

• Journal of Information Technology Services
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• v.12 no.2
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• pp.269-289
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• 2013

The tremendous advancement of technology sparked a lot of opportunities for developers and consumers to pave way to a dynamic application market in smartphones. This study focuses on the users' perspective, that is, the preference between two application markets that varies in many perspectives of its features. Hence, the purpose of this study is to provide a comparative study on two mobile application stores in smartphones; Google Play and T-Store. A survey was conducted to compare the markets, and the results showed the different influencing factors on choosing and using each application store. In addition, the results somehow revealed the harmony of co-existence in smartphones.

• ETRI Journal
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• v.43 no.1
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• pp.95-108
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• 2021

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.