• The Journal of the Korea Contents Association
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• v.16 no.8
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• pp.517-528
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• 2016

This study attempted to explore and examine a new user experience (UX) research method for IoT products which are becoming widely used but lack practical user research. While user experience research has been traditionally opted for survey or observation methods, this paper utilized big data analysis method for user online reviews on an intelligent agent IoT product, Amazon's Echo. The results of topic modelling analysis extracted user experience elements such as features, conversational interaction, and updates. In addition, regression analysis showed that the topic of updates was the most influential determinant of user satisfaction. The main implication of this study is the new introduction of big data analysis method into the user experience research for the intelligent agent IoT products.

• Journal of Distribution Science
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• v.13 no.8
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• pp.73-80
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• 2015

Purpose - This study's objective is to analyze the content of the communications between users and producers based on the construal level theory. User generated content refers to content created in an online-based service where users and producers communicate interactively with each other. In a user generated content platform, the messages sent and received between the many players, the users and producers who use the content, may be analyzed at the psychological level based on construal level theory. Research design, data, and methodology - This study gathered user and producer participation through a snow-bowling sampling method. The data analyzed includes 125 video clips and 2,912 comments. The period of the data collection was from September 2014 to December 2014. The collected data was analyzed using a t-test and two-way ANOVA. Results - This study obtained the following research results. First, users who were a short social distance from producers responded to user participatory activities stated in concrete language rather than abstract language. In contrast, users who were at a longer social distance from producers tended to respond to the content requesting user participation through abstract language. Second, if users and producers were at a short social distance from each other, user preference increased more when a producer response to user participation was expressed concretely rather than when it was expressed abstractly. In contrast, if the users were at a longer social distance, users' preferences increased more when producer response was expressed abstractly rather than when it was expressed concretely. Conclusion - This study found that the effect of suitability, in which the social distance and the content were in congruence at the construal level, could be observed. Therefore, based on this, academic and practical implications were drawn. The three main insights of the study are as follows. First, firms can use psychological factors to analyze the message content of users in their distribution platforms. This study reveals managerial implications for marketing managers who want to take make use of this analysis of user and producer communications. This study indicates that the main factors include the concrete and abstract scores and social distance between users and producers. Second, we also provide the strategic guidelines to maximizing user preferences and other outcomes. The main dependent variable in this study is the user preference shift; the variable increases through the congruence effect; and the construal level is determined by the social distance between the users and producers and the type of producer response. The outcomes here from users can be utilized to develop several systemic strategies. One process to use the outcomes could be: (1) firms could measure the users and producers social distance; (2) calculate the concreteness or abstractness of the messages; and, (3) predict the user preference outcomes by the congruence between user and producer social distance and the abstractness or concreteness of the message content.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.4
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• pp.340-349
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• 2017

This paper is proposed Hadoop security protocol to protect a reply attack and impersonation attack. The proposed hadoop security protocol is consists of user authentication module, public key based data node authentication module, name node authentication module, and data node authentication module. The user authentication module is issued the temporary access ID from TGS after verifing user's identification on Authentication Server. The public key based data node authentication module generates secret key between name node and data node, and generates OTKL(One-Time Key List) using Hash-chain. The name node authentication module verifies user's identification using user's temporary access ID, and issues DT(Delegation Token) and BAT(Block Access Token) to user. The data node authentication module sends the encrypted data block to user after verifing user's identification using OwerID of BAT. Therefore the proposed hadoop security protocol dose not only prepare the exposure of data node's secret key by using OTKL, timestamp, owerID but also detect the reply attack and impersonation attack. Also, it enhances the data access of data node, and enforces data security by sending the encrypted data.

• Journal of the Korea Society of Computer and Information
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• v.21 no.3
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• pp.97-104
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• 2016

In this paper, we propose spatial information based simulator for user experience optimization and minimize real space complexity. We focus on developing simulator how to design virtual space model and to implement virtual character using real space data. Especially, we use expanded events-driven inference model for SVM based on machine learning. Our simulator is capable of feature selection by k-fold cross validation method for optimization of data learning. This strategy efficiently throughput of executing inference of user behavior feature by virtual space model. Thus, we aim to develop the user experience optimization system for people to facilitate mapping as the first step toward to daily life data inference. Methodologically, we focus on user behavior and space modeling for implement virtual space.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3319-3338
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• 2016

Cloud storage is becoming more and more popular because of its elasticity and pay-as-you-go storage service manner. In some cloud storage scenarios, the data that are stored in the cloud may be shared by a group of users. To verify the integrity of cloud data in this kind of applications, many auditing schemes for shared cloud data have been proposed. However, all of these schemes do not consider the access authorization problem for users, which makes the revoked users still able to access the shared cloud data belonging to the group. In order to deal with this problem, we propose a novel public auditing scheme for shared cloud data in this paper. Different from previous work, in our scheme, the user in a group cannot any longer access the shared cloud data belonging to this group once this user is revoked. In addition, we propose a new random masking technique to make our scheme preserve both data privacy and identity privacy. Furthermore, our scheme supports to enroll a new user in a group and revoke an old user from a group. We analyze the security of the proposed scheme and justify its performance by concrete implementations.

• The Transactions of the Korea Information Processing Society
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• v.6 no.5
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• pp.1189-1202
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• 1999

As compared with VOD data, NOD article data has the following characteristics: it is created at any time, has a short life cycle, is selected as not one article but several articles by a user, and has high access locality in time. Because of these intrinsic features, user access patterns of NOD article data are different from those of VOD. Thus, building NOD system using the existing techniques of VOD system leads to poor performance. In this paper, we analysis the log file of a currently running electronic newspaper, show that the popularity distribution of NOD articles is different from Zipf distribution of VOD data, and suggest a new popularity model of NOD article data MS-Zipf(Multi-Selection Zipf) distribution and its approximate solution. Also we present a life cycle model of NOD article data, which shows changes of popularity over time. Using this life cycle model, we develop LLBF (Largest Life-cycle Based Frequency) prefetching algorithm and analysis he performance by simulation. The developed LLBF algorithm supports the similar level in hit-ratio to the other prefetching algorithms such as LRU(Least Recently Used) etc, while decreasing the number of data replacement in article prefetching and reducing the overhead of the prefetching in system performance. Using the accurate user access patterns of NOD article data, we could analysis correctly the performance of NOD server system and develop the efficient policies in the implementation of NOD server system.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.9 no.2
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• pp.104-109
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• 2010

In this paper, we propose a user authentication technology to protect wiretapping and attacking from others in the telematics environment, which users in vehicle can use internet service in local area network via mobile device. In the proposed user authentication technology, the packet speech data is encrypted by speech-based biometric key, which is generated from the user's speech signal. Thereafter, the encrypted data packet is submitted to the information communication server(ICS). At the ICS, the speech feature of the user is reconstructed from the encrypted data packet and is compared with the preregistered speech-based biometric key for user authentication. Based on implementation of our proposed communication method, we confirm that our proposed method is secure from various attack methods.

• Journal of Korea Multimedia Society
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• v.10 no.12
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• pp.1655-1662
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• 2007

Although the PKl-based user authentication solution has been widely used, the security of it can be deteriorated by a simple password. This is because a long and random private key may be protected by a short and easy-to-remember password. To handle this problem, many biometric-based user authentication solutions have been proposed. However, protecting biometric data is another research issue because the compromise of the biometric data will be permanent. In this paper, we present an implementation to improve the security of the typical PKI-based authentication by protecting the private key with a fingerprint. Compared to the unilateral authentication provided by the typical biometric-based authentication, the proposed solution can provide the mutual authentication. In addition to the increased security, this solution can alleviate the privacy issue of the fingerprint data by conglomerating the fingerprint data with the private key and storing the conglomerated data in a user-carry device such as a smart card. With a 32-bit ARM7-based smart card and a Pentium 4 PC, the proposed fingerprint-based PKI authentication can be executed within 1.3second.

• Journal of Korean Library and Information Science Society
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• v.51 no.1
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• pp.255-279
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• 2020

Recently, as the range of users utilizing open data has expanded from experts to students, and general citizens, the role of open data portals has changed. In the past, portals have neglected to increase data utilization through citizen participation by focusing on the role of simple data repository, but now they tend to focus on understanding, collaboration and sharing values so that users can actively use data. To meet these social trends, open data portals need to seek ways to improve user-centered services that can encourage citizen participation. The purpose of this study is to identify the main functions for citizen participation in open data portals, to analyze the current status of open data portal user services and to suggest ways to improve them. Through the literature research, we investigated the functions provided by portal services for citizen participation, deduced the types of user services, and analyzed open data portal user services. Furthermore, we suggested user-centered public data portal services improvement plans for citizen participation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.