• 한국초등과학교육학회지:초등과학교육
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• 제42권1호
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• pp.17-33
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• 2023

본 연구는 초등 예비교사들이 제작한 앱의 특징과 앱 제작 교육에 대한 초등교사의 생각을 살펴보고 이를 통해 초등 예비교사를 위한 TPACK 교육에 시사점을 도출하는 데에 그 목적이 있다. 과학교육 앱 제작 경험을 제공한 사례를 수집하고, 3명의 초등교사를 대상으로 해당 사례에 관한 생각을 조사하였다. 예비교사들이 제작한 앱의 특징을 분석한 결과는 다음과 같다. 첫째, 예비교사들이 앱을 제작하면서 의도한 교육목표는 탐구가 가장 높게 나타났고, 도구형과 학습자·교수자간 상호작용이 일어나는 앱을 제작한 경우가 상대적으로 높게 나타났다. 둘째, 대부분의 예비교사들이 교육과정 목표에 부합하도록 앱을 제작하였으나 건설적 차원과 협력적 차원에서는 낮은 수준의 유형에 해당하는 앱의 특징을 보여 주었다. 예비교사들이 제작한 앱과 앱 제작 교육에 대한 초등교사들의 생각을 분석한 결과는 다음과 같다. 첫째, 초등교사들은 예비교사들이 제작한 앱의 효과성에 가장 낮은 점수를 주었고, 이를 해결하기 위해 교육과정 성취기준 분석과 기 개발된 앱 평가 및 수정 활동을 제안하였다. 둘째, 초등교사들은 예비교사의 TPACK 향상을 위하여 앱을 직접 제작해보는 경험을 제공하는 것이 적절하다고 응답하였다. 셋째, 초등교사들은 앱 인벤터를 활용하여 앱을 제작할 수 있는 블록 코딩 문해력 정도가 예비교사에게 필요하다고 생각하였다. 넷째, 예비교사의 TPACK을 향상시키기 위해 앱 제작 교육에서 모의수업과 앱을 통해 데이터를 수집하고 다루어 보는 경험을 강조할 필요가 있다고 하였다.

• 한국정보시스템학회지:정보시스템연구
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• 제27권4호
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• pp.129-147
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• 2018

Purpose The purpose of this study is to identify the effects of users' perceived value of food delivery Apps and information quality on acceptance and intention to reuse of food delivery Apps. Design/methodology/approach This study made a design of the research model by integrating the factors of perceived value and information quality with the acceptance and continued usage of delivery Apps on the basis of the TAM (Technology Acceptance Model). Findings The results of this study suggests that perceived emotional value and monetary value, accuracy and timeliness of information have significant positive effects on usefulness and easiness of food delivery Apps. Meanwhile, both usefulness and easiness of food delivery Apps have significant positive influences on users' continually use intention. Furthermore, implications in terms of the findings of this study are discussed.

• 한국멀티미디어학회논문지
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• 제23권5호
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• pp.641-649
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• 2020

As the usage of mobile devices extremely increases, malicious mobile apps(applications) that target mobile users are also increasing. It is challenging to detect these malicious apps using traditional malware detection techniques due to intelligence of today's attack mechanisms. Deep learning (DL) is an alternative technique of traditional signature and rule-based anomaly detection techniques and thus have actively been used in numerous recent studies on malware detection. In order to develop DL-based defense mechanisms against intelligent malicious apps, feeding recent datasets into DL models is important. In this paper, we develop a DL-based model for detecting intelligent malicious apps using KU-CISC 2018-Android, the most up-to-date dataset consisting of benign and malicious Android apps. This dataset has hardly been addressed in other studies so far. We extract OPcode sequences from the Android apps and preprocess the OPcode sequences using an N-gram model. We then feed the preprocessed data into LSTM and apply the concept of Information Gain to improve performance of detecting malicious apps. Furthermore, we evaluate our model with numerous scenarios in order to verify the model's design and performance.

• 한국의류산업학회지
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• 제15권3호
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• pp.393-405
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• 2013

This study considers the effect of smartphone involvement for purchase intentions in fashion shopping malls based on mobile web apps and on PC web apps; in addition, it investigates the correlation between purchase intention in fashion shopping malls (based on activated PC web apps) and those of fashion shopping malls (based on newly created mobile web apps). The results of this study are: First, the analysis of smartphones factors showed that smartphones consist of 6 kinds of dimensions of 'appearance involvement', 'time and information search involvement', 'application involvement', 'entertainment involvement', 'communication involvement', and 'transaction involvement'. Second, the higher the entertainment and transaction involvement among descriptive variables for smartphone involvement were due to the positive responses displayed towards fashion shopping mall purchases based on mobile web apps. Third, a higher application and entertainment involvement in the descriptive variables for smartphones resulted in a positive response displayed for purchases in fashion shopping malls based on PC web apps shown in a regression analysis that verified the smartphone and purchase intention relationship in fashion shopping malls based on PC web apps. Fourth, consumers with high purchase intention in the existing PC web app based fashion shopping malls were shown to have a high purchase intention in mobile web app based fashion shopping malls due to the results of a correlation analysis that analyzed the relationship between purchase intention in PC web app based fashion shopping malls and mobile web app based fashion shopping malls.

• 정보보호학회논문지
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• 제27권6호
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• pp.1491-1498
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• 2017

스마트폰의 사용 및 다양한 앱들의 출시 등이 급격하게 증가됨에 따라 악성 앱들도 많이 증가하였고 이로 인한 피해가 속출하고 있다. 안드로이드 앱들이 등록되는 구글 마켓은 앱 등록 규정이 있음에도 불구하고 정상적인 앱들과 악성 앱들이 불가피하게 동시에 존재한다. 특히, 소셜 네트워크가 활성화됨에 따라 안드로이드 구글 마켓에서도 다양한 형태로 보이지 않게 사용자들이 소셜 정보망을 맺고 평점, 다운로드 수 및 인지도 정보 등이 참고 되어 앱 다운로드 수에 반영되고 있다. 결과, 일반 사용자들이 단순히 평점, 인기도, 인기 있는 댓글 및 인지도 높은 카테고리 앱 등만 반영하여 앱을 선택하게 되면 악성 앱 다운로드로 인해 때로는 큰 피해를 볼 수 있다. 따라서 본 연구는 실제 운용되고 있는 안드로이드 마켓에서 장기간 소셜 정보를 직접 크롤링하고 분석하여 악성 앱의 경향성을 처음으로 분석했다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권6호
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• pp.2354-2376
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• 2020

SmartThings is one of the most popular open platforms for home automation IoT solutions that allows users to create their own applications called SmartApps for personal use or for public distribution. The nature of openness demands high standards on the quality of SmartApps, but there have been few studies that have evaluated this thoroughly yet. As part of software quality practice, code reviews are responsible for detecting violations of coding standards and ensuring that best practices are followed. The purpose of this research is to propose systematically designed quality metrics under the well-known Goal/Question/Metric methodology and to evaluate the quality of SmartApps through automatic code reviews using a static analysis. We first organize our static analysis rules by following the GQM methodology, and then we apply the rules to real-world SmartApps to analyze and evaluate them. A study of 105 officially published and 74 community-created real-world SmartApps found a high ratio of violations in both types of SmartApps, and of all violations, security violations were most common. Our static analysis tool can effectively inspect reliability, maintainability, and security violations. The results of the automatic code review indicate the common violations among SmartApps.

• 한국의류산업학회지
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• 제18권6호
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• pp.825-832
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• 2016

The purposes of this study was to investigate effects of fashion brand apps on smartphones flow and consumer satisfaction in mobile shopping. These findings provide new information to marketers for the marketing strategy for dealing with mobile shopping mall. A total of 233 usable questionnaires were obtained from men and women ages 20-30 who have mobile shopping purchasing experience. Data were analyzed by frequency analysis, factor analysis using SPSS for Window 22.0 and confirmatory factor analysis and structural equation model analysis by AMOS 22.0. The results were as follows. First, the analysis of fashion brand apps factors showed that it consist of 2 kinds of dimensions of basic information type and additional information type. Second, basic information type of fashion brand apps have had a direct impact on consumer satisfaction and did not affect the smartphones flow. Additional information type influence consumer satisfaction through a smartphones flow. Basic information type of fashion brand apps showed a greater impact on consumer satisfaction than a smartphone flow. A managerial implication was discussed for fashion brand marketers to develop strategies on fashion brand apps that could stimulate and increase the consumer flow and satisfaction.

• 지식경영연구
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• 제20권3호
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• pp.139-154
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• 2019

With the increasing use of smartphone apps, many apps are coming out in various fields. In order to analyze the current status and trends of apps in a specific field, it is necessary to establish a classification scheme. Various schemes considering users' behavior and characteristics of apps have been proposed, but there is a problem in that many apps are released and a fixed classification scheme must be updated according to the passage of time. Although it is necessary to consider many aspects in establishing classification scheme, it is possible to grasp the trend of the app through the proposal of a classification scheme according to the characteristic of the app. This research proposes a method of establishing an app classification scheme through the description of the app written by the app developers. For this purpose, we collected explanations about apps in the tourism field and identified major categories through topic modeling. Using only the apps corresponding to the topic, we construct a network of words contained in the explanatory text and identify subcategories based on the networks of words. Six topics were selected, and Clauset Newman Moore algorithm was applied to each topic to identify subcategories. Four or five subcategories were identified for each topic.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.286-293
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• 2021

Recently, the quick development rate of apps in the Android platform has led to an accelerated increment in creating malware applications by cyber attackers. Numerous Android malware detection tools have utilized conventional signature-based approaches to detect malware apps. However, these conventional strategies can't identify the latest apps on whether applications are malware or not. Many new malware apps are periodically discovered but not all malware Apps can be accurately detected. Hence, there is a need to propose intelligent approaches that are able to detect the newly developed Android malware applications. In this study, Radial Basis Function (RBF) networks are trained using known Android applications and then used to detect the latest and new Android malware applications. Initially, the optimal permission features of Android apps are selected using Information Gain Ratio (IGR). Appropriately, the features selected by IGR are utilized to train the RBF networks in order to detect effectively the new Android malware apps. The empirical results showed that RBF achieved the best detection accuracy (97.20%) among other common machine learning techniques. Furthermore, RBF accomplished the best detection results in most of the other measures.

• 한국조리학회지
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• 제22권2호
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• pp.206-221
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• 2016

스마트 폰 확산과 배달 앱의 이용 증가로 인해 배달 앱 이용 시 소비자가 지각하는 감정반응과 앱 이용의도에 관한 연구의 필요성을 제시하였다. 따라서 본 연구에서는 배달 앱에 대한 이용의도를 높일 수 있는 변수로 배달 앱의 서비스품질과 PAD의 2가지 감정차원, 각성과 즐거움을 제시하여 그 역할을 규명하고자 하였다. 이러한 분석결과를 토대로 배달 앱 콘텐츠 개발과 운영에 필요한 기초적인 자료를 제공하고자 하였다. 연구의 조사대상과 공간적 범위는 최근 3개월이내 배달 앱을 이용한 경험이 있는 전국의 20세 이상 남녀 내국인 거주자로 정하였으며, 조사대상자 중에서 배달의 민족, 요기요, 배달통을 통해 음식을 주문한 경험이 있는 외식소비자들을 대상으로 설문조사를 하였다. 연구결과, 배달 앱 서비스품질의 디자인과 공감성은 각성에 긍정적인 영향을 미치며, 디자인과 신뢰성은 즐거움에 긍정적 영향을 미치는 것으로 나타났다. 배달 앱 서비스품질의 정보성과 이동성은 앱 이용의도에 긍정적인 영향을 미치는 것으로 나타났다. PAD의 즐거움은 앱 이용의도에 긍정적인 영향을 미치나, 각성은 아무 영향력이 없는 것으로 나타났다. 외식소비자들이 배달 앱을 지속적으로 이용하도록 유도하기 위해서는 디자인과 신뢰성, 정보성, 이동성이 매우 중요한 요인임을 제시하였다.