• Journal of the Korean Institute of Intelligent Systems
• /
• v.18 no.1
• /
• pp.66-71
• /
• 2008

Recent multimedia technology has supported various application services based on the development of effective movie compression and network techniques. On-line video conference system is a typical example that use theses two technologies effectively. On-line video conference system can be characterized into an effective conferencing method for long-distanced on-line conference members. But, unfortunately, off-line conference with face-to-face meeting is more frequent than on-line conference and their support systems have not been sufficiently considered. In this paper, we propose a recording support system for off-Line conference using face and speaker recognition. This system finds the speaker in the conference by using three microphones and three webcam cameras. And analysis is done with face region information that gathered by currently active webcam camera, and recognizes the identity of face. Finally, the system tracks speaker and records conference with extract speaker information.

• Journal of Digital Contents Society
• /
• v.11 no.4
• /
• pp.607-614
• /
• 2010

History centered knowledge based story-telling project planning tool supports the process of story creation in narrative genre about history of families or individuals. Narrative fields not only include drama, mythology, legend, history but also non-verbal epics such as movie, play, ballet and opera. But as verbal epic, this research paper focuses on the family history and individual history of each household. This story-telling planning tool redevelops each genre of story-telling about family history through sampleDB and informationDB, and it is widely applicable in concreting high quality stories in both its content and value. Reduces the time of planning story-telling, and impose minimum expenses in human resources. Content about family history is one of the most the fundamental and renowned contents in Story-telling but planning tool that is easily applicable in creating such content does not exist in statue quo. In this current system lacking creative infra, this research paper seeks to provide a planning tool that public can easily utilize, and by systemizing the tool. it aims to create a creative contents tool model applicable in variety of genres.

• The KIPS Transactions:PartC
• /
• v.13C no.4 s.107
• /
• pp.389-396
• /
• 2006

P2P(Peer To Peer) system, a most attractive file sharing system, is the largest channel of contents distribution and it takes 50% of network traffic. But P2P systems are infamous for used to illegal contents distribution channel not only in music industry, but also in movie industry. But, DRM(Digital Right Management) enabled P2P models are not suggested until now that interrupting illegal contents distribution and keeping advantage of P2P. So in this paper, we suggest a DRM enabled P2P model that can support distributed processing ability and high scalability with no modification in exist P2P model or architecture.

• Journal of Korea Entertainment Industry Association
• /
• v.15 no.5
• /
• pp.33-43
• /
• 2021

Kakao M and Kakao page have been merged to form contents corporation, Kakao entertainment. Kakao M has 15 contents management agencies and 4 music labels, in addition to movie and drama productions. Kakao Page currently holds IP rights for about 8,500 content stories. This study explores the relationship between M&A for absorptive capacity and content value chain by considering the factors that determine boundary spanning behaviors. Using the Kakao entertainment in-depth case study as the practical lens, research results of this study are suggested. Kakao's effective M&A activities are critical key factor for absorptive capacity in the entertainment industry and has a strong network with advantage assets. Also, as the contents business becomes even more competitive, Kakao need to venture beyond entertainment boundaries to seize creative opportunities. Kakao entertainment with absorptive capacity and boundary spanning behaviors through M&A and contents value chain best qualified for entertainment dominant strategy.

• Journal of Korea Society of Industrial Information Systems
• /
• v.15 no.2
• /
• pp.31-39
• /
• 2010

VoD(Video-on-Demand) servers have to provide timely processing guarantees for continuous media and reduce the storage and bandwidth requirements for continuous media. The compression techniques make the bit rates of compressed video data significantly variable from frame to frame. A VoD system should be able to provide the client with interactive operations such as fast forward and fast rewind in addition to normal playback of movie. However, interactive operations require additional resources such as storage space, disk bandwidth, memory and network bandwidth. In a stored video application such as VoD system, it is possible that a priori disk access patterns can be used to reserve the system resources in advance. In addition, clients of VoD server spend most of their time in playback mode and the period of time spent in interactive mode is relatively small. In this paper, I present the new buffer management scheme that provides efficient support for interactive operations in a VoD server using variable bit rate continuous media. Simulation results show that our strategy achieves 34% increase of the number of accepted clients over the LRU strategy.

• Journal of Intelligence and Information Systems
• /
• v.23 no.2
• /
• pp.19-38
• /
• 2017

Collaborative filtering (CF) algorithm has been popularly used for implementing recommender systems. Until now, there have been many prior studies to improve the accuracy of CF. Among them, some recent studies adopt 'hybrid recommendation approach', which enhances the performance of conventional CF by using additional information. In this research, we propose a new hybrid recommender system which fuses CF and the results from the social network analysis on trust and distrust relationship networks among users to enhance prediction accuracy. The proposed algorithm of our study is based on memory-based CF. But, when calculating the similarity between users in CF, our proposed algorithm considers not only the correlation of the users' numeric rating patterns, but also the users' in-degree centrality values derived from trust and distrust relationship networks. In specific, it is designed to amplify the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the trust relationship network. Also, it attenuates the similarity between a target user and his or her neighbor when the neighbor has higher in-degree centrality in the distrust relationship network. Our proposed algorithm considers four (4) types of user relationships - direct trust, indirect trust, direct distrust, and indirect distrust - in total. And, it uses four adjusting coefficients, which adjusts the level of amplification / attenuation for in-degree centrality values derived from direct / indirect trust and distrust relationship networks. To determine optimal adjusting coefficients, genetic algorithms (GA) has been adopted. Under this background, we named our proposed algorithm as SNACF-GA (Social Network Analysis - based CF using GA). To validate the performance of the SNACF-GA, we used a real-world data set which is called 'Extended Epinions dataset' provided by 'trustlet.org'. It is the data set contains user responses (rating scores and reviews) after purchasing specific items (e.g. car, movie, music, book) as well as trust / distrust relationship information indicating whom to trust or distrust between users. The experimental system was basically developed using Microsoft Visual Basic for Applications (VBA), but we also used UCINET 6 for calculating the in-degree centrality of trust / distrust relationship networks. In addition, we used Palisade Software's Evolver, which is a commercial software implements genetic algorithm. To examine the effectiveness of our proposed system more precisely, we adopted two comparison models. The first comparison model is conventional CF. It only uses users' explicit numeric ratings when calculating the similarities between users. That is, it does not consider trust / distrust relationship between users at all. The second comparison model is SNACF (Social Network Analysis - based CF). SNACF differs from the proposed algorithm SNACF-GA in that it considers only direct trust / distrust relationships. It also does not use GA optimization. The performances of the proposed algorithm and comparison models were evaluated by using average MAE (mean absolute error). Experimental result showed that the optimal adjusting coefficients for direct trust, indirect trust, direct distrust, indirect distrust were 0, 1.4287, 1.5, 0.4615 each. This implies that distrust relationships between users are more important than trust ones in recommender systems. From the perspective of recommendation accuracy, SNACF-GA (Avg. MAE = 0.111943), the proposed algorithm which reflects both direct and indirect trust / distrust relationships information, was found to greatly outperform a conventional CF (Avg. MAE = 0.112638). Also, the algorithm showed better recommendation accuracy than the SNACF (Avg. MAE = 0.112209). To confirm whether these differences are statistically significant or not, we applied paired samples t-test. The results from the paired samples t-test presented that the difference between SNACF-GA and conventional CF was statistical significant at the 1% significance level, and the difference between SNACF-GA and SNACF was statistical significant at the 5%. Our study found that the trust/distrust relationship can be important information for improving performance of recommendation algorithms. Especially, distrust relationship information was found to have a greater impact on the performance improvement of CF. This implies that we need to have more attention on distrust (negative) relationships rather than trust (positive) ones when tracking and managing social relationships between users.

• Cartoon and Animation Studies
• /
• s.20
• /
• pp.1-14
• /
• 2010

The success of a three-dimensional blockbuster movie, AVARTA, brought an public attention on the expansion of three-dimensional computer applications, and it allows experts predict further hardware technology developments to support the such applications. Futhermore, an internet based infra structure and three-dimensional structure, third generation network community, advanced computer networks have influenced advancement in computer technology within the 3D game industry and the spread of 2D computer animation technologies. This advancement of computer technologies allow the industry to overcome a limitation of generating cultural design contexts existed within 2D network community. However, despite of the expansion of 2D and 3D computer technologies, a limitation of analysing designers' intentions on morphology of digital contents and user interface still exists. Therefore, the purpose of this study is to analyze (1) present conditions of the 3D industry and (2) protocols of designers' cognitive design processes based on their design communication, contents, and tools. Analysis was conducted based on literature reviews and case precedent analyses. For the analysis, a 2D Avarta sketch character was designed and then applied into a 3D game system. Observations how designers solve cultural problem within the structure via Avarta were conducted. Outcomes were then coded for further analysis.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.8
• /
• pp.4831-4843
• /
• 2014

Magazines on specific genres have been operating collaborative, co-working and collective production systems for value maximization using an adaptation strategy on the dynamic, complex and uncertain value network of the magazine industry. The study used a case study method, and data collection was performed by observational research, depth interviews and survey research. The subjects of the study were 'magazine industry', 'magazine firm and community', and 'collaboration system within creative users'. According to the research results, the ecosystem of magazines on a specific genre has been evolving into an innovative value network system, which is combined with the magazine firm, community users and magazine platform. Second, the rapid introduction of smart device environment changes the way of the collaborating system, in which an action and interaction came out within the community, creative users and magazine firms. Third, the production agency shows strong action and interaction, which fits the magazine platform within the ecosystem of a magazine on a specific genre well. This model has a similar fractal structure to the game, publishing, drama, movie, comic, and animation contents industry, converging to an innovative technology-based-creative-industry.

• Journal of Intelligence and Information Systems
• /
• v.20 no.2
• /
• pp.137-148
• /
• 2014

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