• Journal of the Korean Society of Clothing and Textiles
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• v.37 no.7
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• pp.1000-1014
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• 2013

This study identifies the determinants that influence attitude toward and the intention to use mobile shopping services through fashion applications (apps) based on the technology acceptance model. In addition, gender and age group differences were examined. Data were collected from subjects who have used smartphone fashion related apps; subsequently, a total of 327 data were analyzed. About 46% of respondents were males, with a mean age of 34.4 years that ranged from 20 to 49 years old. Multiple regression models were developed based on the research model. Perceived usefulness, perceived ease of use, perceived enjoyment, perceived risks (security risk and quality risk), fashion involvement, and fashion app attributes (product attributes and service attributes) were employed as predictors of attitudes towards mobile shopping. Attitudes towards mobile shopping and subjective norms with the aforementioned variables measured the intention to use. Attitudes towards mobile shopping were predicted by perceived enjoyment, perceived usefulness, and service attributes. Attitudes toward mobile shopping and subjective norms were the most important predictors of the intention to use. Gender differences were found in that service attributes were significant for attitudes towards mobile shopping only in the male model. Age differences were also found and perceived usefulness was the most important predictor of attitudes toward mobile shopping among those in their 20's; however, perceived enjoyment was the most important among those in their 30's and 40's. Quality risk was only significant to explain intention to use among those in their 40's. The findings of this study are useful to understand the possibility of the adoption of mobile shopping though fashion apps and provide basic insight into market segmentation.

• Convergence Security Journal
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• v.12 no.4
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• pp.9-15
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• 2012

In recently years, repackaged malwares are becoming increased rapidly in Android smartphones. The repackaging is a technique to disassemble an app in a market, modify its source code, and then re-assemble the code, so that it is commonly used to make malwares by inserting malicious code in an app. However, it is impossible to collect all the apps in many android markets including too many apps. To solve the problem, we propose RePAD (RePackaged App Detector) scheme that is composed of a client and a remote server. In the smartphone-side, the client extracts the information of an app with low CPU overhead when a user installs the app. The remote server analyzes the information to decide whether the app is repackaged or not. Thus, the scheme reduces the time and cost to decide whether apps are repackaged. For the experiments, the client and server are implemented as an app on Galaxy TAB and PC respectively. We indicated that seven pairs of apps among ones collected in official and unofficial market are repackaged. Furthermore, RePAD only increases the average of CPU overhead of 1.9% and the maximum memory usage of 3.5 MB in Galaxy TAB.

• Convergence Security Journal
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• v.14 no.4
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• pp.61-71
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• 2014

In recent years, malwares in Android smartphones are becoming increased explosively. Since a great deal of appsare deployed day after day, detecting the malwares requires commercial anti-virus companies to spend much time and resources. Such a situation causes malwares to be detected after they have become already spread. We propose a scheme called TAU that dynamically tracks application behaviors to specify apps with potential security risks. TAU keeps track of how a user's interactions to smartphones incurs the app installation, the route of app spread, and the behavior of app execution. This tracking specifies apps that have the possibility of attacking the smartphones using the drive-by download and update attack schemes. Moreover, the tracked behaviors are used to decide whether apps are repackaged or not. Therefore, TAU allows anti-virus companies to detect malwares efficiently and rapidly by guiding to preferentially analyze apps with potential security risks.

• The Journal of Information Systems
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• v.27 no.2
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• pp.1-28
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• 2018

Purpose More than 7.6 million mobile apps could be approved on both Apple iTunes Store and Google Play. For managing those existed Apps, Apple Inc. established twenty-four primary categories, as well as Google Play had thirty-three primary categories. However, all of their categorizations have appeared more and more problems in managing and classifying numerous apps, such as app miscategorized, cross-attribution problems, lack of categorization keywords index, etc. The purpose of this study focused on introducing individual information cognitive processing as the classification criteria to update the current categorization on Apple iTunes Store. Meanwhile, we tried to observe the effectiveness of the new criteria from a classification process on Apple iTunes Store. Design/Methodology/Approach A research approach with four research stages were performed and a series of mixed methods was developed to identify the feasibility of adopting individual information cognitive processing as categorization criteria. By using machine-learning techniques with Term Frequency-Inverse Document Frequency and Singular Value Decomposition, keyword lists were extracted. By using the prior research results related to car app's categorization, we developed individual information cognitive processing. Further keywords extracting process from the extracted keyword lists was performed. Findings By TF-IDF and SVD, keyword lists from more than five thousand apps were extracted. Furthermore, we developed individual information cognitive processing that included a categorization teaching process and learning process. Three top three keywords for each category were extracted. By comparing the extracted results with prior studies, the inter-rater reliability for two different methods shows significant reliable, which proved the individual information cognitive processing to be reliable as criteria of categorization on Apple iTunes Store. The updating suggestions for Apple iTunes Store were discussed in this paper and the results of this paper may be useful for app store hosts to improve the current categorizations on app stores as well as increasing the efficiency of app discovering and locating process for both app developers and users.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.3
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• pp.525-532
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• 2013

Recently. smartphones have been popularized rapidly and now located deep in our daily life, providing a variety of services from banking, SNS (Social Network Service), and entertainment to smart-work mobile office through apps. Such smartphone apps can be easily downloaded from what is known as app store which, however, bears many security issues as software developers can just as easily upload to it. Military apps will be exposed to a myriad of security threats if distributed through internet-basis commercial app store. In order to mitigate such security concerns, this paper suggests a security guidelines for establishing a military-excusive app store and security verification system which prevent the security hazards that can occur during the process of development and distribution of military-use mobile apps.

• Korean Journal of Veterinary Research
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• v.60 no.3
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• pp.133-137
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• 2020

Diarrhea is the most common cause of death in calves, and remains a major health challenge. Although there are many studies on the related pathogens, the understanding of the clinicopathological changes is limited. This study aimed to identify the pathogens and observe the clinicopathological changes in electrolytes and acute phase proteins (APPs) associated with diarrhea. Blood samples and fecal samples were collected from 141 calves for the determination of APPs, electrolyte and acid-base status and identification of enteropathogens, respectively. Single or co-infections with enteropathogens, including virus (bovine viral diarrhea virus, coronavirus, and rotavirus), Eimeria, Cryptosporidium, and Escherichia coli K99 were detected in both non-diarrheic and diarrheic calves. Levels of APPs such as serum amyloid A, haptoglobin and fibrinogen were comparable between diarrheic and non-diarrheic calves. Hypoglycemia, high blood urea, electrolytes and acid-base imbalance (hyponatremia, hypochloremia, and decreased bicarbonate), and strong ion difference (SID) acidosis showed a significant association in diarrheic calves (p < 0.01). Particularly, significant hyponatremia, bicarbonate loss, SID acidosis, hypoglycemia, and elevated blood urea nitrogen were found in rotavirus-infected calves. Monitoring the clinicopathological parameters of APPs and electrolyte levels could be vital in the clinical management of diarrheic calves.

• Journal of KIISE
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• v.43 no.2
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• pp.212-221
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• 2016

Android apps suffer from intent vulnerabilities in that they abnormally stop execution when Android components such as, activity, service, and broadcast receiver, take malformed intents. This paper proposes a method to prevent intent vulnerabilities by allowing programmers to write a specification on intents that a component expects to have, and by checking intents against the specification in runtime. By declaring intent specifications, we can solve the problem that one may miss writing conditional statements, which check the validity of intents, or one may mix those statements with another regular code, so making it difficult to maintain them. We perform an experiment by applying the proposed method to 7 Android apps, and confirm that many of abnormal termination of the apps because of malformed intents can be avoided by the intent specification based runtime assertion.

• Journal of the Korea Convergence Society
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• v.8 no.3
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• pp.49-61
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• 2017

Recently, convenience and usability are increasing with the development and deployment of various mobile applications on the Android platform. However, important information stored in the smartphone is leaked to the outside without knowing the user since the malicious mobile application is continuously increasing. A variety of mobile vaccines have been developed for the Android platform to detect malicious apps. Recently discovered server-based polymorphic(SSP) malicious mobile apps include obfuscation techniques. Therefore, it is not easy to detect existing mobile vaccines because some other form of malicious app is newly created by using SSP mechanism. In this paper, we analyze the correlation between the similarity of the method in the DEX file constituting the core malicious code and the permission similarity measure through APK de-compiling process for the SSP malicious app. According to the analysis results of DEX method similarity and permission similarity, we could extract the characteristics of SSP malicious apps and found the difference that can be distinguished from the normal app.

• Journal of Korean Academy of Nursing
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• v.46 no.3
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• pp.390-399
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• 2016

Purpose: The purpose of this study was to determine the effects of smartphone apps applying BodyThink program on BMI, percentage of body fat, skeletal muscle rate, body image, and self-esteem of adolescent girls. Methods: Sixty-eight high school girls with a BMI of over $25kg/m^2$ were recruited to participate in this study. Girls from four schools were divided into two groups: the experimental group, which used the smartphone apps applying BodyThink program, and the control group, which used smartphone apps and small group counseling. The experimental group received the BodyThink program 6 times, scheduled once a week, with each session lasting 40~50 minutes. Test measures were completed before and after the 6 week intervention period for all participants. Collected data was analyzed using Shapiro-Wilk test, descriptive statistics, ${\chi}^2$ test, independent t-test, Mann-Whitney U test with the SPSS/WIN 18.0 program. Results: The girls in the experimental group significantly improved their results in BMI(Z=-1.67, p=.042), percentage of body fat (Z=-3.01, p=.001), skeletal muscle rate (t=-3.50, p<.001), and self-esteem (t=2.66, p=.005) after the program, compared to the girls in the control group. Conclusion: Mobile applications applying psychological and emotional intervention programs have the potential to be effective alternative methods to improve the body composition and self-esteem of obese adolescent girls.

• Journal of Internet of Things and Convergence
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• v.6 no.2
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• pp.1-10
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• 2020

The mobile application based on the Android platform is simple to decompile, making it possible to create malicious applications similar to normal ones, and can easily distribute the created malicious apps through the Android third party app store. In this case, the Android malicious application in the smartphone causes several problems such as leakage of personal information in the device, transmission of premium SMS, and leakage of location information and call records. Therefore, it is necessary to select a optimal model that provides the best performance among the machine learning techniques that have published recently, and provide a technique to automatically identify malicious Android apps. Therefore, in this paper, after adopting the feature engineering to Android apps on official test set, a total of four performance evaluation experiments were conducted to select the machine learning model that provides the optimal performance for Android malicious app detection.