• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2614-2632
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• 2015

A multiple symbol detection (MSD) algorithm is proposed relying on soft information for ultra-wideband systems, where differential space-time block code is employed. The proposed algorithm aims to calculate a posteriori probabilities (APP) of information symbols, where a forward and backward message passing mechanism is implemented based on the BCJR algorithm. Specifically, an MSD metric is analyzed and performed for serving the APP model. Furthermore, an autocorrelation sampling is employed to exploit signals dependencies among different symbols, where the observation window slides one symbol each time. With the aid of the bidirectional message passing mechanism and the proposed sampling approach, the proposed MSD algorithm achieves a better detection performance as compared with the existing MSD. In addition, when the proposed MSD is exploited in conjunction with channel decoding, an iterative soft-input soft-output MSD approach is obtained. Finally, simulations demonstrate that the proposed approaches improve detection performance significantly.

• Journal of Information Processing Systems
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• v.17 no.1
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• pp.14-27
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• 2021

Strong earthquakes have caused substantial losses in recent years, and earthquake risk prevention has aroused a significant amount of attention. Earthquake risk prevention products can help improve the self and mutual-rescue abilities of people, and can create convenient conditions for earthquake relief and reconstruction work. At present, it is difficult for earthquake risk prevention information systems to meet the information requirements of multiple scenarios, as they are highly specialized. Aiming at mitigating this shortcoming, this study investigates and analyzes four user roles (government users, public users, social force users, insurance market users), and summarizes their requirements for earthquake risk prevention products in the whole disaster chain, which comprises three scenarios (pre-quake preparedness, in-quake warning, and post-quake relief). A targeted recommendation rule base is then constructed based on the case analysis method. Considering the user's location, the earthquake magnitude, and the time that has passed since the earthquake occurred, a targeted recommendation model is built. Finally, an Android APP is implemented to realize the developed model. The APP can recommend multi-form earthquake risk prevention products to users according to their requirements under the three scenarios. Taking the 2019 Lushan earthquake as an example, the APP exhibits that the model can transfer real-time information to everyone to reduce the damage caused by an earthquake.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.61-66
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• 2017

As the fourth industrial revolution becomes more popular and advanced services such as artificial intelligence and Internet of Things technology are widely commercialized, awareness of the importance of software is spreading. Recently, software education has been taught not only in elementary school and college but also in college. Also, there is a growing interest in computational thinking needed to solve problems through computing methodology and model. The purpose of this study is to design an app inventor course for non-majors software education based on computational thinking. As a result of the study, six detailed competencies of computational thinking were derived, and six detailed competencies were mapped to the app inventor learning elements. In addition, based on the computational thinking modeling, I designed an app inventor class for students who participated in IT curriculum of university liberal arts curriculum.

• Molecules and Cells
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• v.40 no.1
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• pp.73-81
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• 2017

The ${\gamma}$-secretase complex represents an evolutionarily conserved family of transmembrane aspartyl proteases that cleave numerous type-I membrane proteins, including the ${\beta}$-amyloid precursor protein (APP) and the receptor Notch. All known rare mutations in APP and the ${\gamma}$-secretase catalytic component, presenilin, which lead to increased amyloid ${\beta}$-peptide production, are responsible for early-onset familial Alzheimer's disease. ${\beta}$-amyloid protein precursor-like (APPL) is the Drosophila ortholog of human APP. Here, we created Notch- and APPL-based Drosophila reporter systems for in vivo monitoring of ${\gamma}$-secretase activity. Ectopic expression of the Notch- and APPL-based chimeric reporters in wings results in vein truncation phenotypes. Reporter-mediated vein truncation phenotypes are enhanced by the Notch gain-of-function allele and suppressed by RNAi-mediated knockdown of presenilin. Furthermore, we find that apoptosis partly contributes to the vein truncation phenotypes of the APPL-based reporter, but not to the vein truncation phenotypes of the Notch-based reporter. Taken together, these results suggest that both in vivo reporter systems provide a powerful genetic tool to identify genes that modulate ${\gamma}$-secretase activity and/or APPL metabolism.

• Journal of the Korea Society of Computer and Information
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• v.28 no.12
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• pp.259-266
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• 2023

In this paper, we propose an algorithm that can improve the accuracy performance of collaborative filtering using attribute-based opinion mining (ABOM). For the experiment, a total of 1,227 online consumer review data about smartphone apps from domestic smartphone users were used for analysis. After morpheme analysis using the KKMA (Kkokkoma) analyzer and emotional word analysis using KOSAC, attribute extraction is performed using LDA topic modeling, and the topic modeling results for each weighted review are used to add up the ratings of collaborative filtering and the sentiment score. MAE, MAPE, and RMSE, which are statistical model performance evaluations that calculate the average accuracy error, were used. Through experiments, we predicted the accuracy of online customers' app ratings (APP_Score) by combining traditional collaborative filtering among the recommendation algorithms and the attribute-based opinion mining (ABOM) technique, which combines LDA attribute extraction and sentiment analysis. As a result of the analysis, it was found that the prediction accuracy of ratings using attribute-based opinion mining CF was better than that of ratings implementing traditional collaborative filtering.

• Journal of the Korea Knowledge Information Technology Society
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• v.13 no.1
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• pp.11-23
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• 2018

This paper empirically investigates the factors that influence the consumer choice of an app market based on the rational choice theory. The app market is the only channel where a consumer can buy smartphone apps, which give various functional convenience and are considered to be a major contributor to the proliferation of smartphones. Analyses of 281 questionnaires show that usability and structural guarantees as benefit factors significantly influence the app market choice. From the cost perspectives, both monetary and non-monetary conversion costs are found to significantly influence the app market choice. On the other hand, customer trust, information quality, and market image were found to have no significant effect on app market selection. In particular, Korean app market platform providers (KT, LG U +) seem to be superior in terms of structural guarantees, such as customer center operation and damage compensation regulations, compared to overseas app market platform operators (Google). However, in the case of the Google App Market, it is pre-installed on all Android phones, so it is not inconvenient to install additional apps to use other app market. This is disadvantageous to domestic app market platform operators, and it is necessary to establish a policy solution point. In terms of operator costs, both monetary and non-monetary conversion costs have a significant impact on app market choice. In particular, non-monetary conversion costs have a negative impact on Korean app market platform operators. It can be explained that the service expectation level of the domestic app market is low and it is recognized that the time cost factor such as membership is large for new users to use. It seems to be necessary to improve the domestic app market business. Meanwhile, extant research on smartphone apps focuses on the purchase of apps themselves, but not on the selection of the app market itself. In order to fill in this gap, this study focuses on the determinants of app market selection, including the characteristics of an app market and the switching costs.

• The Journal of Korean Association of Computer Education
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• v.19 no.6
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• pp.45-54
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• 2016

The purpose of this study is to investigate the effects of Android application programming education to control Arduino using App Inventor on industrial high school students' creative and integrative thinking ability. We developed an instructional content based on integrative learning and creative problem-solving model and taught a class on it. The result of this study showed that there was a significant improvement in divergent thinking and motivation items among the sub elements of creative problem solving. In addition, students' survey on the integrated thinking has shown that many students think that they could design an IoT system applied to everyday life based on the knowledge they have learned in this class. Therefore, it can be confirmed that physical computing education using App Inventor and Arduino has a positive effect on students' creative and integrative thinking ability.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.11
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• pp.3723-3737
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• 2022

Google Play is one of the largest Android phone app markets and it contains both free and paid apps. It provides a variety of categories for every target user who has different needs and purposes. The customer's rate every product based on their experience of apps and based on the average rating the position of an app in these arch varies. Fraudulent behaviors emerge in those apps which incorporate search rank maltreatment and malware proliferation. To distinguish the fraudulent behavior, a novel framework is structured that finds and uses follows left behind by fraudsters, to identify both malware and applications exposed to the search rank fraud method. This strategy correlates survey exercises and remarkably joins identified review relations with semantic and behavioral signals produced from Google Play application information, to distinguish dubious applications. The proposed model accomplishes 90% precision in grouping gathered informational indexes of malware, fakes, and authentic apps. It finds many fraudulent applications that right now avoid Google Bouncers recognition technology. It also helped the discovery of fake reviews using the reviewer relationship amount of reviews which are forced as positive reviews for each reviewed Google play the android app.

• Journal of Digital Convergence
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• v.14 no.5
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• pp.157-163
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• 2016

With the rapid expansion of smart-phone based brand app and its marketing implementations, the marketers stake much resources in the success of brand apps so that they are able to reinforce the user satisfaction and intention for repeat use. The purpose of this paper is to understand the characteristics underlying the brand applications and to analyze the usage and attitudes of the users. Self-administered survey on the research variables for respondents of 20-49 age group was conducted and the results as follow. The results are as follows the consumers are more satisfied with its usefulness, entertainment, and design attributes of the brand app. They tend to keep using a brand app that contains useful contents and great entertainment elements. Marketers thus need to reflect those elements when developing and distributing a brand app in order to meet the needs of their customers. The study results would help marketing firms find new competitive edges by providing basic insight for new competition strategies for brand app development.