• Journal of Information Processing Systems
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• v.7 no.1
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• pp.121-136
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• 2011

With the rapid growth of GPS-enable Smartphones, the interest on using Location Based Services (LBSs) has increased significantly. The evolution in the functionalities provided by those smartphones has enabled them to accurately pinpoint the location of a user. Because location information is what all LBSs depend on to process user's request, it should be properly protected from attackers or malicious service providers (SP). Additionally, maintaining user's privacy and confidentiality are imperative challenges to be overcome. A possible solution for these challenges is to provide user anonymity, which means to ensure that a user initiating a request to the SP should be indistinguishable from a group of people by any adversary who had access to the request. Most of the proposals that maintain user's anonymity are based on location obfuscation. It mainly focuses on adjusting the resolution of the user's location information. In this paper, we present a new protocol that is focused on using cryptographic techniques to provide anonymity for LBSs users in the smartphone environment. This protocol makes use of a trusted third party called the Anonymity Server (AS) that ensures anonymous communication between the user and the service provider.

• Journal of Information Processing Systems
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• v.4 no.4
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• pp.159-168
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• 2008

Human-Robot Interaction (HRI), based on already well-researched Human-Computer Interaction (HCI), has been under vigorous scrutiny since recent developments in robot technology. Robots may be more successful in establishing common ground in project-based education or foreign language learning for children than in traditional media. Backed by its strong IT environment and advances in robot technology, Korea has developed the world's first available e-Learning home robot. This has demonstrated the potential for robots to be used as a new educational media - robot-learning, referred to as 'r-Learning'. Robot technology is expected to become more interactive and user-friendly than computers. Also, robots can exhibit various forms of communication such as gestures, motions and facial expressions. This study compared the effects of non-computer based (NCB) media (using a book with audiotape) and Web-Based Instruction (WBI), with the effects of Home Robot-Assisted Learning (HRL) for children. The robot gestured and spoke in English, and children could touch its monitor if it did not recognize their voice command. Compared to other learning programs, the HRL was superior in promoting and improving children's concentration, interest, and academic achievement. In addition, the children felt that a home robot was friendlier than other types of instructional media. The HRL group had longer concentration spans than the other groups, and the p-value demonstrated a significant difference in concentration among the groups. In regard to the children's interest in learning, the HRL group showed the highest level of interest, the NCB group and the WBI group came next in order. Also, academic achievement was the highest in the HRL group, followed by the WBI group and the NCB group respectively. However, a significant difference was also found in the children's academic achievement among the groups. These results suggest that home robots are more effective as regards children's learning concentration, learning interest and academic achievement than other types of instructional media (such as: books with audiotape and WBI) for English as a foreign language.

• Journal of the Korea Fashion and Costume Design Association
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• v.17 no.4
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• pp.201-217
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• 2015

Instagram, image based visual SNS platform is regarded as one of the most effective marketing channels and the number of users are getting increased. To enhance of our understanding of general current use and customer attitude through Instagram accounts owned by fashion brands depending on user attributes, the present study investigates factors that current status of use, purpose of use, brand attitude, purchase intension, and recommend intension. A survey method was conducted for empirical test. A convenience sample of 100 participants was used for data analysis. SPSS 18.0 was used for statistical analysis. Findings of this study suggest that first, the very purpose of access is to grasp fashion trend, second, a group of the high interest in fashion accesses to Instagram account of fashion brands more often, uses Instagram for information search more than other group and has a positive influence on recommend intension, third, a group of the high frequent access accesses Instagram for purpose of information search more and has a positive influent on values on customer attitude and the last, partial purposes of utilization and benefits sought of Instagram of fashion brands has a positive influence on customer attitude.

• Journal of KIISE:Information Networking
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• v.29 no.2
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• pp.188-196
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• 2002

As networked virtual environment (NVE) scales in terms of users and network latency, a key aspect to consider is scalability for interactive performance because a large number of objects likely impose heavy burden especially on the network and computational resources. To improve the scalability, various relevance-filtering mechanism have been proposed. However, the existing filtering mechanism do not scale well in terms of interactive performance as the number of users increase and crowds in a specific place. In this paper, we propose a new scalable filtering scheme which reduces the number of messages by dynamically grouping users based on their interests and distance. Within a group, members communicate with each other with high fidelity. However, a representative sends up-to-dated group information of members with low transmission frequency when they are not of immediate interest but are still within the interest area. The representative is elected from members of the group in distributed manner. The proposed scheme enhances the interactive performance scalability of large-scale NVE systems as much as 18% compared with the existing approach.

• KIPS Transactions on Software and Data Engineering
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• v.6 no.3
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• pp.161-166
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• 2017

In recent times, many people have problems of nutritional imbalance; lack or surplus intake of a specific nutrient despite the variety of available foods. Accordingly, the interest in health and diet issues has increased leading to the emergence of various mobile applications. However, most mobile applications only record the user's diet history and show simple statistics and usually provide only general information for healthy diet. It is necessary for users interested in healthy eating to be provided recommendation services reflecting their food interest and providing customized information. Hence, we propose a menu recommendation method which includes calculating the recommended calorie amount based on the user's physical and activity profile to assign to each food group a substitution unit. In addition, our method also analyzes the user's food preferences using food intake history. Thus it satisfies recommended intake unit for each food group by exchanging the user's preferred foods. Also, the excellence of our proposed algorithm is demonstrated through the calculation of precision, recall, health index and the harmonic average of the 3 aforementioned measures. We compare it to another method which considers user's interest and recommended substitution unit. The proposed method provides menu recommendation reflecting interest and personalized health status by which user can improve and maintain a healthy dietary habit.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.157-176
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• 2011

An interest-based P2P constructs the peer connections based on similarities for efficient search of resources. A clustering technique using peer similarities as data is an effective approach to group the most relevant peers. However, the separation of groups produced from clustering lowers the scalability of a P2P network. Moreover, the interest-based approach is only concerned with user-level grouping where topology-awareness on the physical network is not considered. This paper proposes an efficient scalable search for the interest-based P2P system. A scalable multi-ring (SMR) based on fuzzy clustering handles the grouping of relevant peers and the proposed scalable search utilizes the SMR for scalability of peer queries. In forming the multi-ring, a minimized route function is used to determine the shortest route to connect peers on the physical network. Performance evaluation showed that the SMR acquired an accurate peer grouping and improved the connectivity rate of the P2P network. Also, the proposed scalable search was efficient in finding more replicated files throughout the peer network compared to other traditional P2P approaches.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.415-424
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• 2005

The rapid growth of blog has caused information overload where bloggers in the virtual community space are no longer able to effectively choose the blogs they are exposed to. Recommender systems have been widely advocated as a way of coping with the problem of information overload in e-business environment. Collaborative Filtering (CF) is the most successful recommendation method to date and used in many of the recommender systems. Therefore, we propose a CF-based recommender system for bloggers in the virtual community space. Our proposed methodology consists of three main phases: In the first phase, we apply the "Interest Value" to a recommender system. The Interest Value is a quantity value about user preference in virtual community, and can measure the opinion of users accurately. Next phase, we generate the neighborhood group based on the Interest Value. In the final phase, we use the Community Likeness Score (CLS) to generate the top-n recommendation list. The methodology is explained step by step with an illustrative example and is verified with real data of a blog service provider.

• Journal of the Korean Society for Library and Information Science
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• v.52 no.3
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• pp.5-33
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• 2018

In this paper, we analyze a user's usage behavior, identify and track search intention and interest field based on the National Science and Technology Standard Classification, and use it to personalize science and technology information. In other words, we sought to satisfy both efficiency and satisfaction in searching for information that users want by improving scientific information search performance. We developed the personalization service of science and technology information and evaluated the suitability and usefulness of personalized information by comparing the search performance between expert experimental group and control group. As a result, the personalization service proposed in this study showed better search performance than comparative service and proved to provide higher usability.

• The Journal of the Korea Contents Association
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• v.18 no.3
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• pp.11-18
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• 2018

To interact with a virtual object in immersive virtual environment, the target object should be selected quickly and accurately. Conventional 3D ray casting method using a direction of user's hand or head allows the user to select an object quickly. However, accuracy problem occurs when selecting an object using conventional methods among occlusion of objects. In this paper, we propose a region of interest based selection method that enables to select an object among occlusion of objects using a combination of gaze tracking and hand gesture recognition. When a user looks at a group of occlusion of objects, the proposed method recognizes user's gaze input, and then region of interest is set by gaze input. If the user wants to select an object among them, the user gives an activation hand gesture. Then, the proposed system relocates and visualizes all objects on a virtual active window. The user can select an object by a selecting hand gesture. Our experiment verified that the user can select an object correctly and accurately.

• The KIPS Transactions:PartD
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• v.11D no.5
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• pp.1205-1212
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• 2004

As electronic commerce systems have been widely used, the necessity of adaptive e-commerce agent systems has been increased. These kinds of adaptive e-commerce agents can monitor customer's behaviors and cluster thou in similar categories, and include user's preference from each category. In order to implement our adaptive e-commerce agent system, in this paper, we propose an adaptive e-commerce agent systems consider customer's information of interest and goodwill ratio about preference goods. Proposed system build user's profile more accurately to get adaptability for user's behavior of buying and provide useful product information without inefficient searching based on such user's profile. The proposed system composed with three parts , Monitor Agent which grasps user's intension using monitoring, similarity reference Agent which refers to similar group of behavior pattern after teamed behavior pattern of user, Interest Analyzing Agent which personalized behavior DB as a change of user's behavior.