Showing papers by "Geun-Sik Jo published in 2012"

Social network analysis in a movie using character-net

Abstract: There have been various approaches to analyzing movie stories using social networks. Social network analysis is an effective means to extract semantic information from movies. Movie analysis through social relationships among characters can support various types of information retrieval better than audio-visual feature analysis. The relationships among characters form the main structure of the story. Therefore, through social network analysis among characters, movie story information such as the major roles and the corresponding communities can be determined. Progression of most movie stories is done by characters, and the scriptwriter or director narrates the story and relationships among characters using character dialogs. A dialog has a direction and time that supplies information. Therefore, the dialog is better for constructing social networks of characters than the co-appearance. Additionally, through social networks using the dialog, we can extract accurate movie stories such as classification of major, minor or extra roles, community clustering, and sequence detection. To achieve this, we propose a Character-net that can represent the relationships between characters using dialogs, and a method that can extract the sequences via clustering communities composed of characters. Our experiments show that our proposed method can efficiently detect sequences.

Advanced Methods for Computational Collective Intelligence

Abstract: The book consists of 35 extended chapters which have been selected and invited from the submissions to the 4th International Conference on Computational Collective Intelligence Technologies and Applications (ICCCI 2012) held on November 28-30, 2012 in Ho Chi Minh City, Vietnam. The book is organized into six parts, which are semantic web and ontologies, social networks and e-learning, agent and multiagent systems, data mining methods and applications, soft computing, and optimization and control, respectively. All chapters in the book discuss theoretical and practical issues connected with computational collective intelligence and related technologies. The editors hope that the book can be useful for graduate and Ph.D. students in Computer Science, in particular participants in courses on Soft Computing, Multiagent Systems, and Data Mining. This book can be also useful for researchers working on the concept of computational collective intelligence in artificial populations. It is the hope of the editors that readers of this volume can find many inspiring ideas and use them to create new cases of intelligent collectives. Many such challenges are suggested by particular approaches and models presented in individual chapters of this book. The editors hope that readers of this volume can find many inspiring ideas and influential practical examples and use them in their future work.

Social filtering using social relationship for movie recommendation

Abstract: Traditional recommendation systems provide appropriate information to a target user after analyzing user preferences based on user profiles and rating histories. However, most of people also consider the friend's opinions when they purchase some products or watch the movies. As social network services have been recently popularized, many users obtain and exchange their opinions on social networks. This information is reliable because they have close relationships and trust each other. Most of the users are satisfied with the information. In this paper, we propose a recommendation system based on advanced user modeling using social relationship of users. For the user modeling, both direct and indirect relations are considered and the relation weight between users is calculated by using six degrees of Kevin Bacon. From the experimental results, our proposed social filtering method can achieve better performance than a traditional user-based collaborative filtering method.

A Mobile Landmarks Guide : Outdoor Augmented Reality based on LOD and Contextual Device

Abstract: In recent years, mobile phone has experienced an extremely fast evolution. It is equipped with high-quality color displays, high resolution cameras, and real-time accelerated 3D graphics. In addition, some other features are includes GPS sensor and Digital Compass, etc. This evolution advent significantly helps the application developers to use the power of smart-phones, to create a rich environment that offers a wide range of services and exciting possibilities. To date mobile AR in outdoor research there are many popular location-based AR services, such Layar and Wikitude. These systems have big limitation the AR contents hardly overlaid on the real target. Another research is context-based AR services using image recognition and tracking. The AR contents are precisely overlaid on the real target. But the real-time performance is restricted by the retrieval time and hardly implement in large scale area. In our work, we exploit to combine advantages of location-based AR with context-based AR. The system can easily find out surrounding landmarks first and then do the recognition and tracking with them. The proposed system mainly consists of two major parts-landmark browsing module and annotation module. In landmark browsing module, user can view an augmented virtual information (information media), such as text, picture and video on their smart-phone viewfinder, when they pointing out their smart-phone to a certain building or landmark. For this, landmark recognition technique is applied in this work. SURF point-based features are used in the matching process due to their robustness. To ensure the image retrieval and matching processes is fast enough for real time tracking, we exploit the contextual device (GPS and digital compass) information. This is necessary to select the nearest and pointed orientation landmarks from the database. The queried image is only matched with this selected data. Therefore, the speed for matching will be significantly increased. Secondly is the annotation module. Instead of viewing only the augmented information media, user can create virtual annotation based on linked data. Having to know a full knowledge about the landmark, are not necessary required. They can simply look for the appropriate topic by searching it with a keyword in linked data. With this, it helps the system to find out target URI in order to generate correct AR contents. On the other hand, in order to recognize target landmarks, images of selected building or landmark are captured from different angle and distance. This procedure looks like a similar processing of building a connection between the real building and the virtual information existed in the Linked Open Data. In our experiments, search range in the database is reduced by clustering images into groups according to their coordinates. A Grid-base clustering method and user location information are used to restrict the retrieval range. Comparing the existed research using cluster and GPS information the retrieval time is around 70~80ms. Experiment results show our approach the retrieval time reduces to around 18~20ms in average. Therefore the totally processing time is reduced from 490~540ms to 438~480ms. The performance improvement will be more obvious when the database growing. It demonstrates the proposed system is efficient and robust in many cases.

Construction of semantic user profile for personalized web search

Abstract: User profile is an essential component for accessing the personalized information from the Web. Efficiency of personalized accessed information highly depends on how to model the user details to construct user profile. Previously, user profile was constructed by collecting list of keywords to inferring user interests. These kinds of approaches are not sufficient for many applications. In this paper, we have proposed a new method for constructing semantic user profile for personalized information access. User's query is extended using ontological profile for generation of personalized search context. Experimental results show that our method of constructing semantic profile is effective for searching information with individual needs.

Mobile landmark guide system based on LOD and SURF

Abstract: In this paper, we present a mobile collaborative outdoor augmented reality system. The system will provide a user who captures image of a building facade with related introduction. The proposed system mainly consists of two major parts - annotation module and AR browsing module. Annotation module works with existing standards and linked data to allow users create annotations of the building. Annotation data in our system which only contains URI and metadata, such cluster ID, cluster centroid and the image feature vectors. When user using the system browsing the landmark in outdoor, the system first recognize the landmark and calculate the camera pose from the matching, and then the related introduction is retrieved from linked data through its URI. Experiments show that the proposed augmented reality system is efficient and robust in many cases.

Building a Semantic Social Network Based on Interpersonal Relationships

Abstract: With the emergence of the Smart phone, people can use Online Social Network Services ubiquitously, leading to a significant increase of the number of participants in online social networks. Under these circumstances, online users will require an intelligent and intuitive social relationship management system such as the ontology-driven browsing method. In this paper, to build a user-centered semantic social network and to represent entities and relationships with ontology to improve retrieval performance of the semantic social network, we will design our ontology extended from FOAF, RELATIONSHIP and propose a new method to compute closeness among friends using resources on social networks. Furthermore, we evaluate our ontology-driven browsing on via implementing a prototype system.

E-Commerce video annotation using goodrelations-based LODs with faceted search in smart TV environment

Abstract: TV-commerce is a new form of shopping that allows consumer to view, select and buy products from Smart TV. To do so, sellers annotate videos and associate it with information from online e-commerce systems in a semantic manner. In this work, we propose an e-commerce information derivation mechanism for video annotation using Linked Open Data (LOD) with faceted search. Annotation information is derived from e-commerce LODs, which linked distributed data across e-commerce web. We incorporated faceted search to allow consumer to easily make a information derivation query defined by GoodRelations ontology. The derived information is displayed as a faceted graph facilitating information choice.

A method for automatically creating an interactive semantic video based on AR system

Abstract: This paper proposes an interactive semantic video creation based on an augmented reality environment. This approach relies on Intelligent Augmented Reality (IAR) [5], which was developed for handling complex tasks in aircraft maintenance. The approach consists of two main modules, Augmented Reality (AR) Module and Knowledge Base System (KBS). The system was extended using an interactive semantic video ontology to express a moving object and semantic information. This system is called an iSEE (Interactive SEmantic vidEo) Player. In addition, it showed better cost performance with Cubic Spline Interpolation [15], which generates an interactive semantic video system from an IAR context manager.

A WordNet-based Open Market Category Search System for Efficient Goods Registration

Abstract: Open Market is one of the key factors to accelerate the profit. Usually retailers sell items in several Open Market. One of the challenges for retailers is to assign categories of items with different classification systems. In this research, we propose an item category recommendation method to support appropriate products category registration. Our recommendations are based on semantic relation between existing and any other Open Market categorization. In order to analyze correlations of categories, we use Morpheme analysis, Korean Wiki Dictionary, WordNet and

Grid Computing System based on Web Worker for Smart TV Environments

Abstract: In recent years, there has been a popularity rose up on Smart TV (Smart Television) usage at home. Therefore, it is also have increase the demand on grid computing system. Smart TV has a variety of platform and usage compare to PC (Personal computer). Base on this, it is difficult to apply a traditional grid system on Smart TV. One major reason are concerning the small idle time compare to PC. To overcome this problem, this paper will propose a Javascript grid system and introducing a new scheduling policy that best suit for a smart TV. We have conduct an experiment on the proposed method. The result provides an average of 1.78 percent, which is improved compare to the traditional method which is only provides an average of 0.09 percent.

Collaborative Filtering Method based on User’s Behavior in Social Network

Abstract: Social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Relationship of each user in social network known as social relationship has been used for modeling to help improving effectiveness of recommendation in existing collaborative filtering (CF) method. In this paper, we propose a model that integrates social relationship discovered from user’s behavior with traditional CF in order to increase performance and prediction accuracy of recommender system. We identify user’s relationship by using user’s behavior with their friends such as posts and comments in Facebook. The experimental results have shown that our proposed model improves the accuracy of prediction in terms of MAE and performance in terms of Precision and Recall compared with traditional CF.

A genetic algorithm with the concept of viral infections to solve hard constraints in workflow scheduling

Abstract: Cloud computing is an emerging technology that allows users to utilize on-demand computation, storage, data and services from around the world. The main contribution of this work is to propose a new multi-objective genetic algorithm coupled with the viral infection capable of handling hard constraints, such as restrictions on task scheduling, which are not handled by current algorithms. Furthermore, our algorithm can optimize any number of parameters such as execution time, cost, reliability, and availability; In addition, it can handle restrictions such as deadlines and requirements on the different variables. Using data of the Amazon EC2 cloud resources and workflows from London e-Science Center, we have been investigating the problem of scheduling workflow applications and have compared our algorithm with other scheduling algorithms. Our experimentations have shown the efficiency of our algorithm and have confirmed that the viral infection operator is a powerful tool when solving hard constraints.

An Effective Learning Method in Art Using Virtual Reality and Ontology

Abstract: In this paper, we propose an effective learning method using virtual reality and ontology technologies for appreciation activity in the art education. Appreciation activity, one of the learning methods, indicates sensory, aesthetic understanding through watching and enjoying art. However, current art education methods cannot support a variety of learners’ needs and interests. For watching the art, the learner should visit a museum or an art gallery. And some famous art works may not be exhibited publicly. To solve these spatial and temporal issues, therefore, we propose a new learning method in the art using virtual reality and ontology. In virtual Reality, learners can enjoy the art in their computer. And ontology provides useful information to learners. In this paper, we model the ontology and use Google Art Project for virtual reality.

Collaborative Filtering based on Clustering method using Genre and Interest in SNS

Abstract: Recommender systems play an important role in online business ecosystem, especially to recommend users’ new items. The most critical problem in the recommender systems is providing high accuracy of recommendation to new users which lack of preference to compute similarity between users. In this paper, we propose a recommender system to solve the cold start problem by combining traditional collaborative filtering of users’ rating preference and the users’ genres interest that derived from SNS. First we compute users’ similarity according to their rating on movies. Second we also compute the users’ similarity from genre interest extracted from SNS. We combine these both similarities information in order to produce new user’s similarity. Our experiment results show that our approach is outperform in cold start problem compared to traditional collaborative filtering.

A User Profile-based Filtering Method for Information Search in Smart TV Environment

Abstract: 인터넷 사용자는 비디오를 보면서 소셜 네트워크 서비스를 이용하고 웹 검색을 하고, 비디오에 나타난 상품에 관심이 있을 경우 검색엔진을 통해 정보를 찾는다. 비디오와 사용자의 직접적인 상호작용을 위해 비디오 어노테이션에 대한 연구가 진행되었고, 스마트 TV 환경에서 어노테이션 된 비디오가 활용될 경우 사용자는 객체에 대한 링크를 통해 원하는 상품의 정보를 쉽게 확인할 수 있게 된다. 사용자가 상품에 대한 구매를 원할 경우 상품에 대한 정보검색 이외에 상품평이나 소셜 네트워크 친구의 의견을 통해 구매 결정을 한다. 소셜 네트워크로부터 발생되는 정보는 다른 정보에 비해 신뢰도가 높아 구매 결정에 큰 영향을 미친다. 하지만 현재 소셜 네트워크 서비스는 의견을 얻고자 할 경우 모든 소셜 네트워크 친구들에게 전달되고 많은 의견을 얻게 되어 이들로부터 유용한 정보를 파악하는 것은 쉽지 않다. 본 논문에서는 소셜 네트워크 사용자의 프로파일을 기반으로 상품에 대해 유용한 정보를 제공할 수 있는 친구를 규명하기 위한 필터링 방법을 제안한다. 사용자 프로파일은 페이스북의 사용자 정보와 페이스북 페이지의 “Like” 정보를 이용하여 구성된다. 프로파일의 상품 정보는 GoodRelations 온톨로지와 BestBuy 데이터를 이용하여 의미적으로 표현된다. 사용자가 비디오를 보면서 상품 정보를 얻고자 할 경우 어노테이션된 URI를 이용하여 정보가 전달된다. 시스템은 소셜 네트워크 친구들에 대한 사용자 프로파일과 BestBuy를 기반으로 어노테이션된 상품에 대한 의미적 유사도를 계산하고 유사도 값에 따라 순위가 결정한다. 결정된 순위는 유용한 정보를 제공할 수 있는 소셜 네트워크 상의 친구를 규명하는데 사용된다. 참가자의 동의하에 페이스북 정보를 활용하였고, 시스템에 의해 도출된 결과와 참가자 인터뷰를 통해 평가된 결과를 이용하여 타당성을 검증하였다. 비교 실험의 결과는 제안하는 시스템이 상품 구매결정을 하기 위해 유용한 정보를 획득할 수 있는 방법임을 증명한다.

Ontology-based Machine Translation Mashup System for Public Information

Abstract: We have proposed an ontology-based translation mashup system for foreigner to enjoy Korean cultural information without any language barrier(linguistic problem). In order to utilize public information, we use a mobile public information open API of Seoul metropolitan city. Google AJAX language API is used for translations of public information. We apply an ontology to minimize errors caused by the translations. For ontology modeling, we analyze the public information domain and define classes, relations and properties of cultural vocabulary ontology. We generate ontology instances for titles, places and sponsors which are the most frequently occurring translation errors. We compare the accuracy of translations through our experiment. Through the experimental results using the proposed ontology-based translation mashup system, we verify the validity of the system.

A Mobile Landmarks Guide

Abstract: In recent years, mobile phone has experienced an extremely fast evolution. It is equipped with high-quality color displays, high resolution cameras, and real-time accelerated 3D graphics. In addition, some other features are includes GPS sensor and Digital Compass, etc. This evolution advent significantly helps the application developers to use the power of smart-phones, to create a rich environment that offers a wide range of services and exciting possibilities. To date mobile AR in outdoor research there are many popular location-based AR services, such Layar and Wikitude. These systems have big limitation the AR contents hardly overlaid on the real target. Another research is context-based AR services using image recognition and tracking. The AR contents are precisely overlaid on the real target. But the real-time performance is restricted by the retrieval time and hardly implement in large scale area. In our work, we exploit to combine advantages of location-based AR with context-based AR. The system can easily find out surrounding landmarks first and then do the recognition and tracking with them. The proposed system mainly consists of two major parts-landmark browsing module and annotation module. In landmark browsing module, user can view an augmented virtual information (information media), such as text, picture and video on their smart-phone viewfinder, when they pointing out their smart-phone to a certain building or landmark. For this, landmark recognition technique is applied in this work. SURF point-based features are used in the matching process due to their robustness. To ensure the image retrieval and matching processes is fast enough for real time tracking, we exploit the contextual device (GPS and digital compass) information. This is necessary to select the nearest and pointed orientation landmarks from the database. The queried image is only matched with this selected data. Therefore, the speed for matching will be significantly increased. Secondly is the annotation module. Instead of viewing only the augmented information media, user can create virtual annotation based on linked data. Having to know a full knowledge about the landmark, are not necessary required. They can simply look for the appropriate topic by searching it with a keyword in linked data. With this, it helps the system to find out target URI in order to generate correct AR contents. On the other hand, in order to recognize target landmarks, images of selected building or landmark are captured from different angle and distance. This procedure looks like a similar processing of building a connection between the real building and the virtual information existed in the Linked Open Data. In our experiments, search range in the database is reduced by clustering images into groups according to their coordinates. A Grid-base clustering method and user location information are used to restrict the retrieval range. Comparing the existed research using cluster and GPS information the retrieval time is around 70~80ms. Experiment results show our approach the retrieval time reduces to around 18~20ms in average. Therefore the totally processing time is reduced from 490~540ms to 43 8~480ms. The performance improvement will be more obvious when the database growing. It demonstrates the proposed system is efficient and robust in many cases.

A Filtering Method based on User Profile for Seeking Opinions from Social Network

Abstract: Many online shoppers tend to seek for the opinions of early adopters before making a purchase decision to reduce the risk of buying a new product. Social network provides a very suitable environment for user to share as well as to absorb the most relevant information among friends. However, within a very huge graph of friends, a task of determine who are the most appropriate persons for user to seek opinions on a specific product is yet a limitation within the current approach. We propose a method of defining friends who are the most appropriate persons for user to discuss with. Through Facebook data, user profile is generated in order to determine user’s interest and knowledge about a specific product. Our method has been empirically tested by a case study in order to evaluate the results generated by our system. From the results of user case study, our system can effectively filter the most appropriate friends that user can discuss with on a specific product.

A taxonomy based similarity measure using inference process to reduce cold start issue in collaborative filtering

Abstract: Collaborative filtering (CF) is one of the most widespread methods for recommendation approach. It has been used efficiently to help users to find items that they should appreciate, by identifying users that can be characterized as “similar”, using a similarity measure based on items rating data. However, traditional CF approaches have shown limitation occasioned by problems such as sparsity and cold start. This situation can cause a user to stop using a system due to lack of accuracy in produced recommendations. This paper suggests another source of information, item taxonomies, which have become extremely common among recommender systems for products classification in diverse domains. We combine such knowledge with an inference process on the taxonomy hierarchy to propose a new similarity measure, which focuses on improving recommendations under cold-start conditions. Experiments made on the MovieLens dataset indicate that our method improves results when applied to cold start situations.