• 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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.6
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• pp.243-248
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• 2013

With rapid growth and fast diffusion of smartphone technologies, many users are deeply concerned about the smart applications and many mobile applications converged with various related technologies are rapidly disseminated. Especially, the convergence technologies like mobile apps that can establish the wireless ad hoc network between smartphone and other peripherals and exchange data are appear and progressed continuously. In this paper, we design and implement the smart app using bluetooth based wireless ad hoc sensor network that can connect smartphone with sensors and exchange data for various smart applications. The proposed smart application in this paper collects data obtained from more than 2 multi-sensors in real time and fulfills the decision making function by storing data at the database and analysing it. The smart application designed and implemented in this paper is the healthcare application that can analyze and evaluate the patient's health condition with sensing data from multi-sensors in real time through bluetooth module.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.3
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• pp.159-168
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• 2017

Smartphone malware has increased because Smartphone users has increased and smartphones are widely used in everyday life. Since 2012, Android has been the most mobile operating system. Owing to the open nature of Android, countless malware are in Android markets that seriously threaten Android security. Most of Android malware detection program does not detect malware to which bypass techniques apply and also does not detect unknown malware. In this paper, we propose lightweight method for detection of Android malware using static analysis and deep learning techniques. For experiments we crawl 7,000 apps from the Google Play Store and collect 6,120 malwares. The result show that proposed method can achieve 98.05% detection accuracy. Also, proposed method can detect about unknown malware families with good performance. On smartphones, the method requires 10 seconds for an analysis on average.

• Journal of the Korean Society of Safety
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• v.28 no.1
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• pp.24-28
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• 2013

This article evaluates the accuracy of noise measurements for 37 noise measurement applications for iPhone 4 and iPhone 3Gs. Noise levels were measured using simultaneously a precision sound level meter and iPhones installed noise measurement applications at the levels of 70 dB, 80 dB and 90 dB at 1,000 Hz. Measurement errors were estimated by subtracting two measurements between iPhone and sound level meter. It was found that measurement errors of 34 applications(89.2%) were greater than ${\pm}2$ dB which is the maximum allowable error range for the Type II sound level meter. It was only 4 applications that measurement errors lie within ${\pm}2$ dB error range. There was no significant differences among measurements with four iPhone 4s. However, there were significant differences between the measurements with iPhone 4 and iPhone 3Gs using the same application. It was due to the different hardware specifications such as microphone. Therefore, noise measurement applications, for example, which has to utilize hardware of the smartphone, should be programmed to identify hardware specifications and to adopt appropriate correction factors upon hardware specifications. In conclusion, it is necessary to check accuracy and validity before using the noise measurement applications for iPhones. Also, it was suggested that it should develop an evaluation guideline or protocol on accuracy testing for the measurement applications using a smartphone.

• Journal of Radiation Protection and Research
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• v.47 no.2
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• pp.67-76
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• 2022

Background: We conducted a cross-sectional study of residents within and outside Fukushima Prefecture to clarify their perceptions of the need for smartphone applications (apps) for explaining exposure doses. The results will lead to more effective methods for identifying target groups for future app development by researchers and municipalities, which will promote residents' understanding of radiological situations. Materials and Methods: In November 2019, 400 people in Fukushima Prefecture and 400 people outside were surveyed via a web-based questionnaire. In addition to basic characteristics, survey items included concerns about radiation levels and intention to use a smartphone app to keep track of exposure. The analysis was conducted by stratifying responses in each region and then cross-tabulating responses to concerns about radiation levels and intention to use an app by demographic variables. The intention to use an app was analyzed by binomial logistic regression analysis. Text-mining analyses were conducted in KH Coder software. Results and Discussion: Outside Fukushima Prefecture, concerns about the medical exposure of women to radiation exceeded 30%. Within the prefecture, the medical exposure of women, purchasing food products, and consumption of own-grown food were the main concerns. Within the prefecture, having children under the age of 18, the experience of measurement, and having experience of evacuation were significantly related to the intention to use an app. Conclusion: Regional and individual differences were evident. Since respondents differ, it is necessary to develop and promote app use in accordance with their needs and with phases of reconstruction. We expect that a suitable app will not only collect data but also connect local service providers and residents, while protecting personal information.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.2
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• pp.61-66
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• 2023

As the use of smartphones expands to various areas and the level of multitasking increases, the support of swap is becoming increasingly important. However, swap support in smartphones is known to cause excessive storage traffic, resulting in memory thrashing. In this paper, we analyze how the thrashing of swaps that occurred in early smartphones has changed with the advancement of smartphone hardware. As a result of this analysis, we show that the swap thrashing problem can be resolved to some extent when the memory size increases. However, we also show that thrashing still occurs when the number of running apps continues to increase. Based on further analysis, we observe that this thrashing is caused by some hot data and suggest a way to solve this through an NVM-based architecture. Specifically, we show that a small size NVM with judicious management can resolve the performance degradation caused by smartphone swap.

• Culinary science and hospitality research
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• v.22 no.2
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• pp.234-251
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• 2016

The purpose of this study is to verify the effects of service quality and usage review of smartphone Maiib application(apps) on satisfaction, and reuse intention, convenience sampling method was employed and survey was conducted during the 15th of September, 2015 to the 30th on October as perceived by smartphone Maiib app users. Total of 312 responses were collected, and 295 usable data were used for statistical analysis excluding missing data. Descriptive analysis, factor analysis, and SEM were used to verify the hypothesis. The results from this study are as follows: first, reliability, empathy, usefulness of service quality significantly impact on Majib app satisfaction except informativeness and mobility; second, review assentation of the usage review characteristics significantly impact on Majib app satisfaction but review usefulness of the usage review characteristics significantly did not influence on Majib app satisfaction; third, smartphone Majib app satisfaction critically influences on reuse intention. Based on these results, current study can contribute to verify useful information is very important antecedent to construct the effective marketing strategy by smartphone app.

• Culinary science and hospitality research
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• v.22 no.5
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• pp.64-81
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• 2016

The study, based on existing studies, explored influencing relationship, suggesting app service quality and user reviews as previous elements to affect use satisfaction about users' comments based on big data and reuse intention. The study includes a comparative analysis of existing studies. Based on such analysis results, the authors looked into app service quality elements perceived by gourmet restaurant app users and the role of user reviews, and suggested practical implications that can help the development and operation of gourmet restaurant app contents. The study subjects were male and female consumers who over 20 years old throughout Korea who had not a searched smartphone gourmet restaurant app in the three months preceding the survey. The subjects were selected from consumers who search the restaurantsby using restaurant apps like Mango plate, Dining code, Hot place, and selecting restaurants. Among them, consumers with experience using restaurants were finally selected for the survey. According to the results, reliability, informativity, and system capability, among service quality, had positive influences on app use satisfaction, while design and mobility had no effect. App use satisfaction had positive influences on app reuse intention. User comment informativity played a controlling role. The study explored the importance of app service quality and user review informativity as elements that affect continued use of gourmet restaurant apps by dining-out consumers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.3
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• pp.1210-1230
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• 2015

Android is the world's most utilized smartphone OS which consequently, also makes it an attractive target for attackers. The most representative method of hacking used against Android apps is known as repackaging. This attack method requires extensive knowledge about reverse engineering in order to modify and insert malicious codes into the original app. However, there exists an easier way which circumvents the limiting obstacle of the reverse engineering. We have discovered a method of exploiting the Android code-signing process in order to mount a malware as an example. We also propose a countermeasure to prevent this attack. In addition, as a proof-of-concept, we tested a malicious code based on our attack technique on a sample app and improved the java libraries related to code-signing/verification reflecting our countermeasure.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.10a
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• pp.763-766
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• 2015

Smart Healthcare System as an Open Platform (Shesop) is an integrated healthcare system and have several features, one of them is Steppy Application. Steppy does count your step and display on Shesop website. In this system security issues are not properly addressed, while Personal Health Record (PHR) patient stored in the cloud platform could be at risk. In fact, the huge electronic information available online, people needs reliable and effective technique for privacy preserving. In order to improve the security of data which are displayed on the Shesop website, so that anyone who access could not tamper without permission. Recently Xu et al. showed a pseudonym scheme using smart card as a solution in e-health systems which uses discrete logarithm problem with cyclic group. In this paper, we adopt their scheme and use it application into smartphone using Near Field Communication (NFC) to construct security in Steppy apps.