• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.30 no.4
• /
• pp.657-667
• /
• 2020

The Android framework allows apps to take full advantage of personal information through granting single permission, and does not determine whether the data being leaked is actual personal information. To solve these problems, we propose a tool with static/dynamic analysis. The tool analyzes the Source and Sink used by the target app, to provide users with information on what personal information it used. To achieve this, we extracted the Source and Sink through Control Flow Graph and make sure that it leaks the user's privacy when there is a Source-to-Sink flow. We also used the sensitive permission information provided by Google to obtain information from the sensitive API corresponding to Source and Sink. Finally, our dynamic analysis tool runs the app and hooks information from each sensitive API. In the hooked data, we got information about whether user's personal information is leaked through this app, and delivered to user. In this process, an automated Source/Sink classification model was applied to collect latest Source/Sink information, and the we categorized latest release version of Android(9.0) with 88.5% accuracy. We evaluated our tool on 2,802 APKs, and found 850 APKs that leak personal information.

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

• ETRI Journal
• /
• v.35 no.2
• /
• pp.292-300
• /
• 2013

Augmented reality (AR) is a popular service in mobile devices, and many AR applications can be downloaded from app stores. As TV broadcasting has continued to integrate with the Internet, it has become an area in which the AR concept is able to reside, although in a simple form, such as an advertisement placed in the static region of a scene. There are some restrictions against TV broadcasting realizing AR since TVs are fixed devices and typically do not have GPS, geomagnetic, or acceleration sensors, which are standard equipment in mobile devices. However, the desire to experience AR on a large TV screen has triggered research and development for an ideal AR business model and service type. This paper introduces several use cases for augmented broadcasting services and also presents an augmented broadcasting metadata scheme designed for a broadcasting environment. We also verify some of the use cases and an augmented broadcasting metadata scheme in an implemented augmented broadcasting system based on a hybrid TV platform.

• Journal of the Society of Disaster Information
• /
• v.20 no.1
• /
• pp.206-212
• /
• 2024

Purpose: This study conducted research on the sharing of information to enhance the survival rate of emergency patients by swiftly transferring them to appropriate hospitals through sharing the patients' conditions, treatment histories, and transportation information with the Maritime Police Agency and relevant agencies when emergencies occur in the marine environment. Methods: In this study, emergency patient information classified in a smartphone app was received, stored, and transmitted using the LoRa communication method by electronic triage tags, and the transmitted emergency patient information was implemented to be collected in real-time through a hybrid triage system along with LoRa receivers. Results: Through the hybrid triage system, it was possible to receive emergency patient information according to the distance or confirm delayed reception. It was observed that most data were received when the distance was short, while data reception was unsuccessful in relatively longer distances. Conclusion: It was confirmed that in mass disaster environments where internet communication is impossible, rapid and accurate understanding of casualty information at disaster sites and appropriate disaster responses can be achieved using self-networking methods such as LoRa communication. However, limitations inherent in communication methods were also recognized. Further research on various communication methods is required to collect emergency patient information and transfer them to appropriate hospitals in situations where internet communication is unavailable.

• Journal of the Korean BIBLIA Society for library and Information Science
• /
• v.23 no.4
• /
• pp.71-91
• /
• 2012

As the smartphone market has expanded and university students in their twenties account for the largest portion of the market, university libraries need to provide mobile library services to keep up with students' growing demand in mobile services. The purpose of this study is to analyze the status and trends of mobile services in Korean university libraries. This study investigated mobile services of 434 university libraries in Korea and 114 ones which are members of ARL during the months of September and November in 2012. As a result, this study found that university libraries in Korea were offering 55 mobile Web and 29 mobile application services with 11.2 and 10.7 contents respectively. In addition, on the basis of the identified current problems, this study suggested further steps for developing and improving mobile services in Korean university libraries.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.16 no.1
• /
• pp.101-108
• /
• 2021

This paper builds a real-time home security system based on the OneNet cloud platform to control the status of the house through a smartphone. The system consists of a local part and a cloud part. The local part has I/O devices, router and Raspberry Pi (RPi) that collects and monitors sensor data and sends the data to the cloud, and the Flask web server is implemented on a Rasberry Pi. When a user is at home, the user can access the Flask web server to obtain the data directly. The cloud part is OneNet in China Mobile, which provides remote access service. The hybrid App is designed to provide the interaction between users and the home security system in the smartphone, and the EDP and RTSP protocol is implemented to transmit data and video stream. Experimental results show that users can receive sensor data and warning text message through the smartphone and monitor, and control home status through OneNet cloud.

• Smart Media Journal
• /
• v.11 no.4
• /
• pp.9-18
• /
• 2022

Recently, research and system using AI is rapidly increasing in various fields. Smart farm using artificial intelligence and information communication technology is also being studied in agriculture. In addition, data-based precision agriculture is being commercialized by convergence various advanced technology such as autonomous driving, satellites, and big data. In Korea, the number of commercialization cases of facility agriculture among smart agriculture is increasing. However, research and investment are being biased in the field of facility agriculture. The gap between research and investment in facility agriculture and open-air agriculture continues to increase. The fields of fruit trees and plant factories have low research and investment. There is a problem that the big data collection and utilization system is insufficient. In this paper, we are proposed the system for determining the fruit tree growth stage using a deep learning-based object detection model. The system was proposed as a hybrid app for use in agricultural sites. In addition, we are implemented an object detection function for the fruit tree growth stage determine.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.3
• /
• pp.459-464
• /
• 2023

The mobile house of this article is provided with stand-alone power system that uses photovoltaic energy and enables sensing and environmental monitoring. Excess power generated is stored in lithium batteries, which enable smooth operation of the mobile house even in environment in which solar energy cannot be used. The house has been designed that its systems can be operated continuously by diesel power generation even when photovoltaic energy cannot be generated due to long rainy season or heavy snow. BMS (batter management system) has been constructed for photovoltaic and power management, and monitors the charge/discharge and usage amount of photovoltaic energy. Various sensing data are recorded and transmitted automatically, and the design allows for wireless monitoring by means of computer and smartphone app. The container house proposed in this study enables efficient energy management by performing optimal energy operation in remote areas, parks, event venues, and construction sites where there is no system power source.