Resolving the 'Gray sheep' Problem Using Social Network Analysis (SNA) in Collaborative Filtering (CF) Recommender Systems (소셜 네트워크 분석 기법을 활용한 협업필터링의 특이취향 사용자(Gray Sheep) 문제 해결)
- Kim, Minsung;Im, Il
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- Journal of Intelligence and Information Systems
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- v.20 no.2
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- pp.137-148
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- 2014
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Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used
. Past studies to improve CF performance typically used additional information other than users' evaluations such as demographic data. Some studies applied SNA techniques as a new similarity metric. This study is novel in that it used SNA to separate dataset. This study shows that performance of CF can be improved, without any additional information, when SNA techniques are used as proposed. This study has several theoretical and practical implications. This study empirically shows that the characteristics of dataset can affect the performance of CF recommender systems. This helps researchers understand factors affecting performance of CF. This study also opens a door for future studies in the area of applying SNA to CF to analyze characteristics of dataset. In practice, this study provides guidelines to improve performance of CF recommender systems with a simple modification.
HEVC Encoder Optimization using Depth Information (깊이정보를 이용한 HEVC의 인코더 고속화 방법)
- Lee, Yoon Jin;Bae, Dong In;Park, Gwang Hoon
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- Journal of Broadcast Engineering
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- v.19 no.5
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- pp.640-655
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- 2014
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Many of today's video systems have additional depth camera to provide extra features such as 3D support. Thanks to these changes made in multimedia system, it is now much easier to obtain depth information of the video. Depth information can be used in various areas such as object classification, background area recognition, and so on. With depth information, we can achieve even higher coding efficiency compared to only using conventional method. Thus, in this paper, we propose the 2D video coding algorithm which uses depth information on top of the next generation 2D video codec HEVC. Background area can be recognized with depth information and by performing HEVC with it, coding complexity can be reduced. If current CU is background area, we propose the following three methods, 1) Earlier stop split structure of CU with PU SKIP mode, 2) Limiting split structure of CU with CU information in temporal position, 3) Limiting the range of motion searching. We implement our proposal using HEVC HM 12.0 reference software. With these methods results shows that encoding complexity is reduced more than 40% with only 0.5% BD-Bitrate loss. Especially, in case of video acquired through the Kinect developed by Microsoft Corp., encoding complexity is reduced by max 53% without a loss of quality. So, it is expected that these techniques can apply real-time online communication, mobile or handheld video service and so on.
3D Massively Multiplayer Online Role Playing Game (MMORPG) Based Lecturing System (3차원 다중 사용자 온라인 게임 기반 강의 시스템)
- Lim, Nak-Kwon;Lee, Hae-Young
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- Journal of the Korea Computer Graphics Society
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- v.16 no.1
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- pp.21-27
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- 2010
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Today the lectures are usually practiced in a teacher-led traditional classroom system or a student-led e-learning system. Students passively follow the teacher's lectures in both systems, though. Also due to the advances in 3D Computer Graphics and Game technologies, there are trials to exploit the positive effect of games in learning. The serious games, specifically designed games for an educational goal, or existing games for a special class have been used as lectures. Still these games have a great difficulty in being integrated into the educational system technically and economically. Therefore a new 3D MMORPG based lecturing system is presented in this paper. In our new lecturing system, the characteristics of a 3D MMORPG, achievement, sociality, and immersion, are provided to motivate students to participate actively in a lecture. A teacher and students interact with each other in realtime as 3D characters in a 3D virtual classroom on-line. An ordinary teacher can also easily apply our new system to existing classes since a teacher only needs to specify a slide file to prepare a lecture. For the future work, a user study and the effect of our new lecturing system will be performed.
MORPHEUS: A More Scalable Comparison-Shopping Agent (MORPHEUS: 확장성이 있는 비교 쇼핑 에이전트)
- Yang, Jae-Yeong;Kim, Tae-Hyeong;Choe, Jung-Min
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- Journal of KIISE:Software and Applications
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- v.28 no.2
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- pp.179-191
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- 2001
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Comparison shopping is a merchant brokering process that finds the best price for the desired product from several Web-based online stores. To get a scalable comparison shopper, we need an agent that automatically constructs a simple information extraction procedure, called a wrapper, for each semi-structured store. Automatic construction of wrappers for HTML-based Web stores is difficult because HTML only defines how information is to be displayed, not what it means, and different stores employ different ways of manipulating customer queries and different presentation formats for displaying product descriptions. Wrapper induction has been suggested as a promising strategy for overcoming this heterogeneity. However, previous scalable comparison-shoppers such as ShopBot rely on a strong bias in the product descriptions, and as a result, many stores that do not confirm to this bias were unable to be recognized. This paper proposes a more scalable comparison-shopping agent named MORPHEUS. MORPHEUS presents a simple but robust inductive learning algorithm that antomatically constructs wrappers. The main idea of the proposed algorithm is to recognize the position and the structure of a product description unit by finding the most frequent pattern from the sequence of logical line information in output HTML pages. MORPHEUS successfully constructs correct wtappers for most stores by weakening a bias assumed in previous systems. It also tolerates some noises that might be present in production descriptions such as missing attributes. MORPHEUS generates the wrappers rapidly by excluding the pre-processing phase of removing redundant fragments in a page such as a header, a tailer, and advertisements. Eventually, MORPHEUS provides a framework from which a customized comparison-shopping agent can be organized for a user by facilitating the dynamic addition of new stores.
Weighted Window Assisted User History Based Recommendation System (가중 윈도우를 통한 사용자 이력 기반 추천 시스템)
- Hwang, Sungmin;Sokasane, Rajashree;Tri, Hiep Tuan Nguyen;Kim, Kyungbaek
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- KIPS Transactions on Software and Data Engineering
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- v.4 no.6
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- pp.253-260
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- 2015
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When we buy items in online stores, it is common to face recommended items that meet our interest. These recommendation system help users not only to find out related items, but also find new things that may interest users. Recommendation system has been widely studied and various models has been suggested such as, collaborative filtering and content-based filtering. Though collaborative filtering shows good performance for predicting users preference, there are some conditions where collaborative filtering cannot be applied. Sparsity in user data causes problems in comparing users. Systems which are newly starting or companies having small number of users are also hard to apply collaborative filtering. Content-based filtering should be used to support this conditions, but content-based filtering has some drawbacks and weakness which are tendency of recommending similar items, and keeping history of a user makes recommendation simple and not able to follow up users preference changes. To overcome this drawbacks and limitations, we suggest weighted window assisted user history based recommendation system, which captures user's purchase patterns and applies them to window weight adjustment. The system is capable of following current preference of a user, removing useless recommendation and suggesting items which cannot be simply found by users. To examine the performance under user and data sparsity environment, we applied data from start-up trading company. Through the experiments, we evaluate the operation of the proposed recommendation system.
The risk of the Information-oriented society and the role of private security (정보화 사회의 위험적 요소와 민간시큐리티의 역할)
- Gong, Bae Wan
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- Journal of the Society of Disaster Information
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- v.8 no.1
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- pp.1-9
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- 2012
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Informatization of society through the computer and the Internet, because large amounts of information production and exchange and new way of communicating is born. Passive way past the one-sided information flows actively interact to evolve in a manner of information producers and information consumers distinction and personal relationships that enhance the online Social Networking Service (SNS) has developed into the social structure of. Thus, the spread of information work closely with the social network structure spark social conflict may act as a factor, and systems and the environment, personal and cultural adaptation of speed to keep up with the rapid development of science and technology as the inability conflict and confusion should lead to even. This paper the characteristics of the information society, with a look at the evolution of social risk factors as the wavelength of information about this look at the role of private security sought to evaluate. Information Society in time and space by shrinking the area of human life that has brought the convenience and simplicity, whereas the non-performance due to the nature of anonymous raises many social side-effects are. This made the preparation of national regulatory measures, but for the protection of personal protection devices in the private sector has not yet been discussed. Way of life and property of the purchaser to protect an individual's private security will have to charge it.
Using the fusion of spatial and temporal features for malicious video classification (공간과 시간적 특징 융합 기반 유해 비디오 분류에 관한 연구)
- Jeon, Jae-Hyun;Kim, Se-Min;Han, Seung-Wan;Ro, Yong-Man
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- The KIPS Transactions:PartB
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- v.18B no.6
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- pp.365-374
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- 2011
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Recently, malicious video classification and filtering techniques are of practical interest as ones can easily access to malicious multimedia contents through the Internet, IPTV, online social network, and etc. Considerable research efforts have been made to developing malicious video classification and filtering systems. However, the malicious video classification and filtering is not still being from mature in terms of reliable classification/filtering performance. In particular, the most of conventional approaches have been limited to using only the spatial features (such as a ratio of skin regions and bag of visual words) for the purpose of malicious image classification. Hence, previous approaches have been restricted to achieving acceptable classification and filtering performance. In order to overcome the aforementioned limitation, we propose new malicious video classification framework that takes advantage of using both the spatial and temporal features that are readily extracted from a sequence of video frames. In particular, we develop the effective temporal features based on the motion periodicity feature and temporal correlation. In addition, to exploit the best data fusion approach aiming to combine the spatial and temporal features, the representative data fusion approaches are applied to the proposed framework. To demonstrate the effectiveness of our method, we collect 200 sexual intercourse videos and 200 non-sexual intercourse videos. Experimental results show that the proposed method increases 3.75% (from 92.25% to 96%) for classification of sexual intercourse video in terms of accuracy. Further, based on our experimental results, feature-level fusion approach (for fusing spatial and temporal features) is found to achieve the best classification accuracy.
An Information Management Strategy Over Entire Life Cycles of Hazardous Waste Streams (유해폐기물 생애 전주기 흐름 기반 정보 관리 전략)
- Lee, Sang-hun;Kim, Jungeun
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- Clean Technology
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- v.26 no.3
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- pp.228-236
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- 2020
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Korea has an economy based on manufacturing industrial fields, which produce high amounts of hazardous wastes, in spite of few landfill candidates, and a significant concern for fine airborne particulates; therefore, traditional waste management is difficult to apply in this country. Moreover, waste collection and accumulation have recently been intensified by the waste import prohibitions or regulations in developing nations, the universalization of delivery services in Korea, and the global COVID-19 crisis. This study thus presents a domestic waste management strategy that aims to address the recent issues on waste. The contents of the strategy as the main results of the study include the (1) improvement of the compatibility of the classification codes between the domestic hazardous waste and the international ones such as those of the Basel Convention; (2) consideration of the mixed hazard indices to represent toxicity from low-content components such as rare earth metals often contained in electrical and electronic equipment waste; (3) management application based on risks throughout the life cycles of waste; (4) establishment of detailed material flow information of waste by integrating the Albaro system, Pollutant Release and Transfer Register (PRTR) system, and online trade databases; (5) real-time monitoring and prediction of the waste movement or discharge using positional sensors and geographic information systems, among others; and (6) selection and implementation of optimal treatment or recycling practices through Life Cycle Assessment (LCA) and clean technologies.
Design and Implementation of Integrated E-Coaching system Based on Synchronous and Asynchronous (동기/비동기 기반의 통합 E-코칭 시스템 설계 및 구현)
- Kim, DoYeon;Kim, DoHyeun
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- The Journal of the Institute of Internet, Broadcasting and Communication
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- v.15 no.4
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- pp.1-7
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- 2015
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Until now, face to face coaching has been applied almost for completing the goal in various field. Face to face coaching is difficult always to reach each other anywhere, anytime due to the availability of internet and mobile devices. Recently, e-coaching is attempted to expend. But current e-coaching is supporting the secondary role for face to face coaching. E-coaching system has many benefits to use advancement technologies in internet. Therefore, the development of e-coaching system based on horizontal relationships between coach and coachee needs to communication anytime and anywhere in Internet. Usually previous online coaching systems have four types of interactions i.e. electronic mail, video chat, text chat, phone call. Most of the e-coaching approaches are easy to access and provide communication synchronous; video chat is an excellent visibility, whereas e-mail is asynchronous and document-centric. In this paper, we design and implement the integration e-coaching system based on synchronous and asynchronous. This system provides the asynchronous coaching offered by way of e-mail, and the synchronous coaching used P2P (Peer to Peer) video chat and text group chat. This system allows simultaneously asynchronous and synchronous coaching, and supports individual and group communication for periodical or informal coaching.
Survey on the Education System and National Licensing Examination for Fostering Competent Medical Technologists (우수한 임상병리사 양성을 위한 교육제도 및 국가면허시험제도에 대한 설문조사 분석)
- Kim, Hong Sung;Kwon, Pil Seung;Kang, Ji-Hyuk;Yang, Man-Gil;Park, Jong O;Kim, Dae-Joong;Kim, Won Shik;Joo, Sei Ick;Kim, Eun-Joong;Lee, Sun Kyung;Lee, Sang Hee;Jekal, Seung-Joo
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- Korean Journal of Clinical Laboratory Science
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- v.49 no.2
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- pp.161-170
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- 2017
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This study aimed at characterizing policy directions to foster competent medical technologists by analyzing the opinions of professors and medical technologists regarding university education and national licensing systems. An online survey questionnaire was distributed to 255 professors and 4,000 medical technologists in August of 2016. Fifty-nine professors (23%) and 1,099 medical technologists (27.7%) responded to the survey. The results were evaluated using descriptive statistics and comparative analysis. Professors and medical technologists agreed that there needs to be an improvement and standardization in both education at universities and practical training at hospitals. Moreover, both groups also thought that it was necessary to reform practical examinations and make improvements in the current licensing system. According to the survey results, professors and medical technologists thought that, the improvement of the quality of university education and hospital practical training should be essential, and the reform of existing national licensing examination should be necessary.
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