• Journal of Information Technology Services
• /
• v.13 no.4
• /
• pp.397-410
• /
• 2014

For mobile service organizations, it is one of the most important tasks to reflect users' opinions rapidly and accurately. In this study, the process is defined to elicit requirements of software/system improvement for mobile application by extracting and refining from users' opinion in mobile app, and detailed activities procession method in this processing are also proposed. The process consists of 3 activities to get requirements of software/system improvement for mobile app. First activity is to transform mobile app to software structure and define term dictionary. Second activity is to elicit simple sentences based on software from users' opinion and refine them. The last activity is to integrate and adjust refined requirements. To verify the usability and validity of the proposed process and the methods, the outputs of manual processing and semi-automated processing were compared. As a result, efficiency and improvement possibility of the process were confirmed through extraction ratio of requirements, comparison of execution time, and analysis of agreement ratio.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.28 no.5
• /
• pp.1209-1223
• /
• 2018

Android apps are made up of bytecode, so they are vulnerable to reverse engineering, and protection services are emerging that robustly repackage the app to compensate. Unlike cryptographic algorithms, the robustness of these protection services depends heavily on hiding the protection scheme. Therefore, there are few systematic discussions about the protection method even if destruction techniques of the protection service are various. And it is implemented according to the intuition of the developer. There is a need to discuss systematic protection schemes for robust security chains, rather than simple deployment of techniques disrupting static or dynamic analysis. In this paper, we analyze bangcle, a typical commercial Android app protection service, to examine the protection structure and vulnerable elements. We propose the requirements for robust structure and principles of protection structure.

• Knowledge Management Research
• /
• v.18 no.1
• /
• pp.159-176
• /
• 2017

Recently, the popularity of smartphones has led to a dramatic increase in the frequency of use of App(Application) services. LBS (Location-Based Service) App service adopts various methods such as push marketing and useful information by region through providing location-based service based on the location of the consumer. In particular, an enterprise or an App management company can provide necessary information to the consumer through the necessary information among the customer related knowledge information obtained by utilizing the location information of the consumer in real time. Nevertheless, since LBS is a service that can be performed only when the company obtains consent to provide location information voluntarily by the consumer, there is a case of privacy infringement due to consumers' use of personal information. The purpose of this study is to identify the characteristics of privacy related variables and the knowledge structure for consumer value formation based on the theory of privacy calculation. We also compared the characteristics of Korea with those of China in privacy issue. As a result of the analysis, it was confirmed that factors such as information utilization ability and information control ability were influential as a key factor of privacy calculation. In addition, perceived value influences the reputation of the LBS App service.

• Biomolecules & Therapeutics
• /
• v.22 no.3
• /
• pp.232-238
• /
• 2014

Alzheimer's disease (AD) is the most common neurodegenerative disease without known ways to cure. A key neuropathologic manifestation of the disease is extracellular deposition of beta-amyloid peptide (Ab). Specific mechanisms underlying the development of the disease have not yet been fully understood. In this study, we investigated effects of 4-O-methylhonokiol on memory dysfunction in APP/PS1 double transgenic mice. 4-O-methylhonokiol (1 mg/kg for 3 month) significantly reduced deficit in learning and memory of the transgenic mice, as determined by the Morris water maze test and step-through passive avoidance test. Our biochemical analysis suggested that 4-O-methylhonokiol ameliorated $A{\beta}$ accumulation in the cortex and hippocampus via reduction in beta-site APP-cleaving enzyme 1 expression. In addition, 4-O-methylhonokiol attenuated lipid peroxidation and elevated glutathione peroxidase activity in the double transgenic mice brains. Thus, suppressive effects of 4-O-methylhonokiol on $A{\beta}$ generation and oxidative stress in the brains of transgenic mice may be responsible for the enhancement in cognitive function. These results suggest that the natural compound has potential to intervene memory deficit and progressive neurodegeneration in AD patients.

• Journal of the Korean Chemical Society
• /
• v.19 no.2
• /
• pp.134-141
• /
• 1975

DL-1-aminoethylphosphonic acid(1-AEP) and DL-1-aminopropylphosphonic acid(1-APP) were synthesized from the propionic acid and butyric acid respectively using the modified cutius degradation. The following unreported derivatives containing peptide linkage were synthesized: N-phthalylglycyl-1-AEP, N-phthalyl-dl-alanyl-1-AEP, N-phthalyl-dl-phenylalanyl-1-AEP, N-phthalylglycyl-1-APP, N-tosylglycyl-1-AEP and N-tosyl-dl-alanyl-1-AEP These compounds were characterized and identified by means of elemental analysis, potentiometric titration, infrared spectroscopy and ninhydrin test.

• Journal of Arbitration Studies
• /
• v.31 no.1
• /
• pp.173-194
• /
• 2021

This study was conducted to investigate the effect of the factors of delivery app service failure on consumption well conflict emotion and complaining behavior targeting consumers with experience using delivery apps, and the results of the study are summarized as follows. First, when looking at the demographic analysis, the proportion of men was 58.1% and women were 41.9%, and the age group was the highest with 91.9% in their 20s. Second, as a result of testing hypothesis 1 that the factor of delivery app service failure will have a significant influence on consumer conflict emotions, failure to provide app information and failure to deliver/food service showed a significant positive (+) relationship. Third, as a result of the verification of Hypothesis 2, that consumer conflict emotions will have a significant effect on the consumer's continued use intention, it was found to be insignificant and rejected. Lastly, hypothesis 3 that consumer conflict emotions will have a significant effect on consumer complaints behavior is that negative word of mouth behavior, complaint behavior from service providers, and complaint behavior from service managers were adopted, but the complaint behavior was rejected by third parties.

• ETRI Journal
• /
• v.43 no.1
• /
• pp.95-108
• /
• 2021

Application (app) ratings are feedback provided voluntarily by users and serve as important evaluation criteria for apps. However, these ratings can often be biased owing to insufficient or missing votes. Additionally, significant differences have been observed between numeric ratings and user reviews. This study aims to predict the numeric ratings of Google apps using machine learning classifiers. It exploits numeric app ratings provided by users as training data and returns authentic mobile app ratings by analyzing user reviews. An ensemble learning model is proposed for this purpose that considers term frequency/inverse document frequency (TF/IDF) features. Three TF/IDF features, including unigrams, bigrams, and trigrams, were used. The dataset was scraped from the Google Play store, extracting data from 14 different app categories. Biased and unbiased user ratings were discriminated using TextBlob analysis to formulate the ground truth, from which the classifier prediction accuracy was then evaluated. The results demonstrate the high potential for machine learning-based classifiers to predict authentic numeric ratings based on actual user reviews.

• Composites Research
• /
• v.30 no.3
• /
• pp.223-228
• /
• 2017

The article explains about development of flame retardant self-reinforced composites (FR-SRC) through compression molding technique by utilizing Polypropylene (PP), Ammonium polyphosphate (APP) and chitosan. The effect of APP and chitosan on mechanical, thermal and flame retardant properties in FR-SRC were studied. The mechanical strength of FR-SRC is enhanced than Pure SRC. However, the strength is decreased significantly with increasing the concentration of both flame retardant fillers. But comparison, chitosan filled FR-SRC is stronger than APP filled FR-SRC. In case of flame retardancy, APP is more effective than chitosan. These results can be applicable to provide light weight and recyclable flame retardant self-reinforced composites for automobile and packaging industries, etc.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.4
• /
• pp.17-23
• /
• 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 the Korea Convergence Society
• /
• v.9 no.10
• /
• pp.61-67
• /
• 2018

This study covers the results obtained after three years of development work of real - time gas safety monitoring app. In order to reduce the risk of accidents in the gas workplace, it is essential to provide information so that operators can recognize the location of dangerous substances, dangerous facilities, safety procedures, and the temperature and humidity of the workplace. Therefore, this study was conducted to develop a monitoring app that allows the main system to efficiently receive the information in the field where the information is continuously updated. As a result of quantitative and qualitative research and analysis on app scenarios based on the research done in the past and web screen of the main system, it was modified and improved in terms of ease of use, readability, and intuitiveness. Especially, we made many improvements at low depth, which is a sub-area of ease of use. As a result of conducting a user satisfaction survey on the finally derived app scenarios, the average score of 98.5 was obtained in terms of functionality, convenience and readability.