• Journal of the Korea Society of Computer and Information
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• v.27 no.5
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• pp.37-45
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• 2022

As of 2020, about 500 hours of videos are uploaded to YouTube, a representative online video platform, per minute. As the number of users acquiring information through various uploaded videos is increasing, online video platforms are making efforts to provide better recommendation services. The currently used recommendation service recommends videos to users based on the user's viewing history, which is not a good way to recommend videos that deal with specific purposes and interests, such as educational videos. The recent recommendation system utilizes not only the user's viewing history but also the content features of the item. In this paper, we extract the content features of educational video for educational video recommendation based on YouTube, design a recommendation system using it, and implement it as a web application. By examining the satisfaction of users, recommendataion performance and convenience performance are shown as 85.36% and 87.80%.

• Journal of the Korea Computer Graphics Society
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• v.28 no.1
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• pp.11-19
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• 2022

In this paper we present a new method for interactive segmentation of a triangle mesh by using the concavity-sensitive harmonic field and anisotropic geodesic. The proposed method only requires a single vertex in a desired feature region, while most of existing methods need explicit information on segmentation boundary. From the user-clicked vertex, a candidate region which contains the desired feature region is defined and concavity-senstive harmonic field is constructed on the region by using appropriate boundary constraints. An initial isoline is chosen from the uniformly sampled isolines on the harmonic field and optimal points on the initial isoline are determined as interpolation points. Final segmentation boundary is then constructed by computing anisotropic geodesics passing through the interpolation points. In experimental results, we demonstrate the effectiveness of the proposed method by selecting several features in various 3D models.

• Information Systems Review
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• v.19 no.3
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• pp.179-199
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• 2017

As users' cross-over shopping behaviors become more popular, many studies have attempted to describe a theoretical mechanism in multichannel environments. Apart from explaining a simplified multichannel user behavior, relevant researchers must deeply understand the mechanism of users' cross-over shopping behavior, which cannot be discovered by employing either existing theories or traditional research methods. Thus, this study explores why, how, and when users conduct cross-over shopping behaviors in multichannel environments by employing a grounded theory approach. In this study, we have interviewed 25 participants who have prior experiences in cross-over shopping. By analyzing the interview manuscripts using the grounded theory approach, we have extracted 118 codes in the coding steps and ultimately presented 28 categories by incorporating similar concepts from those codes. In this qualitative grounded theory study, we have discussed why, how, and when users do cross-over shopping behavior based on our selected codes and categories as well as by listening to the stories of our interviewees. By grounding our proposed framework, which can capture both dynamic information search and purchasing behavior, this study provides an alternative research approach to explain user behavior, thereby bolstering our current understanding of the cross-over shopping behavior of users in multichannel environments.

• Journal of Korea Society of Industrial Information Systems
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• v.21 no.5
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• pp.73-83
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• 2016

NAVER provides search frequency data of search terms via its DataLab service (http://datalab.naver.com/). Using this data, this paper examines the relation between the search frequency of firm's name and its future stock returns. Our results show that the search frequency of firm's name is a new investor attention measure, which is different from previously explored attention measures such as extreme returns, turnover, etc. Firms that go through higher search frequency this week tend to have higher returns in the next week. We do not find return reversal in the long run for the firms with higher search frequency. Furthermore, the extent to which search frequency affects stock returns becomes more pronounced following market-wide attention grabbing events. Our results indicate that search frequency incorporates information for future stock returns.

• Journal of the Korean Society for information Management
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• v.33 no.2
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• pp.135-156
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• 2016

Recently, as the importance of user-oriented archives management is becoming increasingly, government archives try to serve interactive services using social network service (SNS) beyond one-way approaches. This study aims to analyze usage of government archives service in social media and examine users' interestedness. We especially select "National Archives of Korea" and "Presidential Archives" as target government archives and collect tweets from 2010 to 15th April 2016. Our study adopts informetric approaches and social media analysis including buzz analysis, time series analysis. We differentiate between the tweet collection posted by government archives themselves and the other collection generated by general users. Furthermore we conduct correlation analysis of tweet and social issues and propose application plan for government archives services in social media environment.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.2
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• pp.260-267
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• 2011

This paper proposes a method for segmenting objects from the background in IR(Infrared) images based on GrabCut algorithm. The GrabCut algorithm needs the window encompassing the interesting known object. This procedure is processed by user. However, to apply it for object recognition problems in image sequences. the location of window should be determined automatically. For this, we adopted the Otsu' algorithm for segmenting the interesting but unknown objects in an image coarsely. After applying the Otsu' algorithm, the window is located automatically by blob analysis. The GrabCut algorithm needs the probability distributions of both the candidate object region and the background region surrounding closely the object for estimating the Gaussian mixture models(GMMs) of the object and the background. The probability distribution of the background is computed from the background window, which has the same number of pixels within the candidate object region. Experiments for various IR images show that the proposed method is proper to segment out the interesting object in IR image sequences. To evaluate performance of proposed segmentation method, we compare other segmentation methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.9B
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• pp.900-907
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• 2009

With the introduction of the concept such as QoS, QoE, and MOS for the multimedia services over the high-speed network the user's concern about the degradation in the perceived quality of service for the conventional data service has become a new problem to the Internet service providers. In this work we propose a method to evaluate the subjective QoE such as MOS for the high-speed Internet service, where the round trip time for the data transfer is taken into account as a performance parameter for the experienced delay. We develop a tool to evaluate an MOS that enables us to investigate the behavior of users for diverse delay times for the web access service, via which we analyze the sensitivity of users with regard to MOS as a function of delay.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.3 no.1
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• pp.7-13
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• 2009

Recently, the interest on walking assistances in order to assist aged people has increased due to the increase of the aged. However, most walking aid systems have a weakness for a slope because they don't have power. So, they have a weak point which makes users difficult to move when they are weak in the legs. That is why the interest on walking assistances with power has increased. The use of the walking aid systems should be easy because most users are old people. Thus, we produce module to grasp walking intent of users by using various sensors such as potentiometer, FSR(Force Sensing Resistance) Sensor and Stretch Sensor and calculate the response time to the module. Firstly, the response time of handlebar which is a kind of potentiometer is 420ms and Resilience of it is 140ms. Secondly, the response time of handlebar which use FSR Sensor is 320ms and Resilience of it is 220ms. Finally, the response time of the Stretch Sensor is 160ms and Resilience of it is 140ms. The performance of Stretch Sensor is the best among the three kind of sensors.

• 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.

• Journal of Internet Computing and Services
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• v.6 no.2
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• pp.69-84
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• 2005

As the Internet users are increasing rapidly, a cluster web server system is attracted by many researchers and Internet service providers. The cluster web server has been developed to efficiently support a larger number of users as well as to provide high scalable and available system. In order to provide the high performance in the cluster web server, efficient load distribution is important, and recently many content-aware request distribution techniques have been proposed. In this paper, we propose a new content-aware load balancing technique that can evenly distribute the workload to each node in the cluster web server. The proposed technique is based on the hash histogram transformation, in which each URL entry of the web log file is hashed, and the access frequency and file size are accumulated as a histogram. Each user request is assigned into a node by mapping of (hashed value-server node) in the histogram transformation. In the proposed technique, the histogram is updated periodically and then the even distribution of user requests can be maintained continuously. In addition to the load balancing, our technique can exploit the cache effect to improve the performance. The simulation results show that the performance of our technique is quite better than that of the traditional round-robin method and we can improve the performance more than $10\%$ compared with the existing workload-aware load balancing(WARD) method.