• Journal of the Korea Society of Computer and Information
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• v.26 no.6
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• pp.107-113
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• 2021

In this paper, we conducted an empirical study to investigate whether Android app descriptions provide enough permission usages for measuring app quality in terms of human writing and consistency between code and descriptions. Android app descriptions are analyzed for various purposes such as quality measurement, functionality recommendation, and malware detection. However, many app descriptions do not disclose permission usages, whether accidentally or on purpose. Most importantly, the previous studies could not precisely analyze app descriptions if permission usages cannot be completely introduced in app descriptions. To assess the consistency between permissions and app descriptions, we implemented a state-of-the-art method to predict Android permissions for 29,270 app descriptions. As a result, 25% of app descriptions may not contain any permission semantic, and 57% of app descriptions cannot accurately reflect permission usages.

• Knowledge Management Research
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• v.20 no.3
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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.

• Journal of Digital Convergence
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• v.12 no.8
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• pp.265-270
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• 2014

This study in coping with the current trend is to propose the environment for training 3-Tier App development being focused on the system to develop mobile applications where its related developers are scarce. Design and implementation of training system for the development of 3-Tier App in this paper is to realize the environment for software development for colleges as same as that in IT companies. For 3-Tier App development training system, 3 students with 3 computers work as a group. The above-mentioned 3 computers include a computer for development, Gateway server, and DB server together with legacy system. Also, each of the 3 students shall be given roles of the foregoing sections. We have educated 3-Tier App training system as a practical class for 64 students in junior students of computer information major. Through training session, it was confirmed that we can foster the students as custom-made talents who understand company's development environment. Also, the Comparison of 3-Tier and Stand-alone App Development Training System for 10 distinct description, we know that 3-Tier app development training system was very superior to stand-alone app development training system in the educational effects.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.1-17
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• 2012

Personalized smart devices such as smartphones and smart pads are widely used. Unlike traditional feature phones, theses smart devices allow users to choose a variety of functions, which support not only daily experiences but also business operations. Actually, there exist a huge number of applications accessible by smart device users in online and mobile application markets. Users can choose apps that fit their own tastes and needs, which is impossible for conventional phone users. With the increase in app demand, the tastes and needs of app users are becoming more diverse. To meet these requirements, numerous apps with diverse functions are being released on the market, which leads to fierce competition. Unlike offline markets, online markets have a limitation in that purchasing decisions should be made without experiencing the items. Therefore, online customers rely more on item-related information that can be seen on the item page in which online markets commonly provide details about each item. Customers can feel confident about the quality of an item through the online information and decide whether to purchase it. The same is true of online app markets. To win the sales competition against other apps that perform similar functions, app developers need to focus on writing app descriptions to attract the attention of customers. If we can measure the effect of app descriptions on sales without regard to the app's price and quality, app descriptions that facilitate the sale of apps can be identified. This study intends to provide such a quantitative result for app developers who want to promote the sales of their apps. For this purpose, we collected app details including the descriptions written in Korean from one of the largest app markets in Korea, and then extracted keywords from the descriptions. Next, the impact of the keywords on sales performance was measured through our econometric model. Through this analysis, we were able to analyze the impact of each keyword itself, apart from that of the design or quality. The keywords, comprised of the attribute and evaluation of each app, are extracted by a morpheme analyzer. Our model with the keywords as its input variables was established to analyze their impact on sales performance. A regression analysis was conducted for each category in which apps are included. This analysis was required because we found the keywords, which are emphasized in app descriptions, different category-by-category. The analysis conducted not only for free apps but also for paid apps showed which keywords have more impact on sales performance for each type of app. In the analysis of paid apps in the education category, keywords such as 'search+easy' and 'words+abundant' showed higher effectiveness. In the same category, free apps whose keywords emphasize the quality of apps showed higher sales performance. One interesting fact is that keywords describing not only the app but also the need for the app have asignificant impact. Language learning apps, regardless of whether they are sold free or paid, showed higher sales performance by including the keywords 'foreign language study+important'. This result shows that motivation for the purchase affected sales. While item reviews are widely researched in online markets, item descriptions are not very actively studied. In the case of the mobile app markets, newly introduced apps may not have many item reviews because of the low quantity sold. In such cases, item descriptions can be regarded more important when customers make a decision about purchasing items. This study is the first trial to quantitatively analyze the relationship between an item description and its impact on sales performance. The results show that our research framework successfully provides a list of the most effective sales key terms with the estimates of their effectiveness. Although this study is performed for a specified type of item (i.e., mobile apps), our model can be applied to almost all of the items traded in online markets.

• Journal of the Korea Society of Computer and Information
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• v.20 no.4
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• pp.17-23
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• 2015

In this paper, we propose a mobile app clustering method using word clusters. Considering the quick change of mobile app trends, the proposed method divides the mobile apps into some semantically similar mobile apps by applying a clustering algorithm to the mobile app set, rather than the predefined category system. In order to alleviate the data sparseness problem in the short mobile app description texts, the proposed method additionally utilizes the unigram, the bigram, the trigram, the cluster of each word. For the purpose of accurately clustering mobile apps, the proposed method manages to avoid exceedingly small or large mobile app clusters by using the word clusters. Experimental results show that the proposed method improves 22.18% from 57.48% to 79.66% on overall accuracy by using the word clusters.

• Journal of Korea Multimedia Society
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• v.20 no.7
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• pp.1054-1064
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• 2017

With the development of cloud computing, internet technology and Internet of Things(IoT), most of applications are being smarter and changing from native application to SaaS (Software as a Service) application. New versatile SaaS applications are being released through various app portals (e.g. appstore, googleplay, T-Store, and so on). However, a user has a difficulty in searching, choosing an suitable application to him. It is also hard for him to know what functions of each SaaS application are useful. He wants to be recommended something inter-operated SaaS service according to his personality and his situation. Therefore, this paper presents a way of making mash-up of SaaS applications in order to provide the most convenient inter-operated SaaS service to user. This paper also presents SaaS Application Mash-up Framework (SAMF), complex context and rule matrix. The proposed SAMF is a main system that totally manage SaaS application mash-up service. Complex context and rule matrix are key components in order to recommend what SaaS applications are needed and how those SaaS applications are inter-operated. The SAMF collects complex contexts (User Description, Status Description, SaaS Service Description) in order to choose which SaaS applications are useful, analyze what functions to use, how to mash-up.

• Nutrition Research and Practice
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• v.10 no.4
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• pp.424-432
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• 2016

BACKGROUND/OBJECTIVES: Most consumers are able to recognize allergenic foods. However, the frequency of checking such foods is reportedly low, resulting in higher prevalence of food-related allergic reactions in Korea compared to other countries. Thus, this study was performed to investigate the overall perception of allergenic food labeling and its practice level in food manufacturing company employees. SUBJECTS/METHODS: The survey was administered to food safety employees and food development teams at food companies located in metropolitan areas. A total of 399 (93.8%) valid samples were used in the final analysis. Statistical analyses, including Frequency Analysis, t-test, Anova, PCA (Principal Component Analysis), and Pearson Correlation Analysis using SPSS ver. 21.0, were performed. RESULTS: The correct answer rate in the analysis of allergy-related knowledge level ranged from 15.0% to 89.7%. Analysis of differences in allergy-related perception by knowledge level showed significant differences in introduction of a food recall system, strengthening of relevant laws and regulations, content labeling, description of substitutional food, and differentiated package by age. CONCLUSIONS: It can be concluded that labeling of allergenic foods should be made easier and more convenient for checking by employees, developers, and consumers, and it is necessary to provide contents through the development of publicity, guidelines, or APP along with labeling.

• Journal of KIISE
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• v.44 no.9
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• pp.871-877
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• 2017

A vast amount of research has been carried out for executing compute-intensive applications on resource-constrained mobile devices. Computation offloading is a method in which heavy computations are dynamically migrated from a mobile device to a server, exploiting the powerful hardware of the server to perform complex computations. An important issue for offloading is the complexity of reconciling the execution state of applications between the server and the client. To address this issue, snapshot-based offloading has recently been proposed, which utilizes the snapshot of a web app as the portable description of the execution state. However, for web applications using the HTML5 canvas, snapshot-based offloading does not function correctly, because the snapshot cannot capture the state of the canvas. In this paper, we propose a code generation technique to save the canvas state as part of a snapshot, so that the snapshot-based offloading can be applied to web applications using the canvas.

• Journal of Nutrition and Health
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• v.53 no.6
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• pp.688-701
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• 2020

Purpose: This study was performed to evaluate the importance and satisfaction of the selective attributes of delivery food and to analyze the factors affecting customer satisfaction. Methods: A total of 574 responses were collected from customers who had ordered delivery food for data analysis. Statistical analyses were conducted using the SPSS program (ver. 25.0) for frequency analysis, χ2 tests, t-test, factor analysis, Pearson correlation, multiple regression analysis, and Importance-Performance Analysis (IPA). Results: The importance of delivery food selection attributes was higher in the order of 'hygiene control level (4.72)', 'taste of food (4.64)', and 'delivery accuracy (4.40)'. Satisfaction assessment was higher in the order of 'taste of food (4.32)', 'delivery accuracy (4.26)', and 'convenience of using the delivery app (4.21)'. According to the results of IPA, items that were priorities for improvement were charges for delivery, discount offers, sufficient description of the menu, and rapid handling of customer complaints. On an average, overall customer satisfaction score of delivery food was 4.01 out of 5 points. Additionally, five satisfaction factors were extracted by exploratory factor analysis. According to the results of multiple regression analysis, quality of delivery platform (p < 0.001), quality of delivery service (p < 0.001), convenience and diversity (p < 0.001), quality of delivery food (p < 0.001), and health and safety (p < 0.001) had significant positive effects on overall customer satisfaction. Conclusion: To increase customer satisfaction among delivery food customers, restaurant or delivery platform managers should consistently improve not only the quality of the delivery platform but also the quality of the delivery food and service.

• The Journal of Korean Institute of Next Generation Computing
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• v.14 no.6
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• pp.30-43
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• 2018

Many studies have been carried out to retrieve images using colors, shapes, and textures which are characteristic of images. In addition, there is also progress in research related to the disease images of the crop. In this paper, to be a help to identify the disease occurred in crops grown in the agricultural field, we propose a similarity-based crop disease search system using the diseases image of horticulture crops. The proposed system improves the similarity retrieval performance compared to existing ones through the combination descriptor without using a single descriptor and applied the weight based calculation method to provide users with highly readable similarity search results. In this paper, a total of 13 Descriptors were used in combination. We used to retrieval of disease of six crops using a combination Descriptor, and a combination Descriptor with the highest average accuracy for each crop was selected as a combination Descriptor for the crop. The retrieved result were expressed as a percentage using the calculation method based on the ratio of disease names, and calculation method based on the weight. The calculation method based on the ratio of disease name has a problem in that number of images used in the query image and similarity search was output in a first order. To solve this problem, we used a calculation method based on weight. We applied the test image of each disease name to each of the two calculation methods to measure the classification performance of the retrieval results. We compared averages of retrieval performance for two calculation method for each crop. In cases of red pepper and apple, the performance of the calculation method based on the ratio of disease names was about 11.89% on average higher than that of the calculation method based on weight, respectively. In cases of chrysanthemum, strawberry, pear, and grape, the performance of the calculation method based on the weight was about 20.34% on average higher than that of the calculation method based on the ratio of disease names, respectively. In addition, the system proposed in this paper, UI/UX was configured conveniently via the feedback of actual users. Each system screen has a title and a description of the screen at the top, and was configured to display a user to conveniently view the information on the disease. The information of the disease searched based on the calculation method proposed above displays images and disease names of similar diseases. The system's environment is implemented for use with a web browser based on a pc environment and a web browser based on a mobile device environment.