• Journal of the Korea Society of Computer and Information
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• v.10 no.3 s.35
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• pp.201-208
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• 2005

Event notification service is a event-based messaging middleware service needed for various vertical domains, such as, business applications, distributed system management, and web service integration. In this paper, we investigate the architecture of an event notification service that includes a subject-based event dissemination service and a flexible message communication service. The event dissemination service is in charge of transferring events asynchronously but speedy according to the subjects of events and their environmental knowledge. It also includes content-based message filtering. The message communication service provides a common communication infrastructure supporting variety types of messages and variety of protocols. Depending on application domains and situation, we can re-configurate the communication infrastructure in order to optimize the efficiency and usability. This paper shows the performance analysis of our event notification service with various types of message formats and protocols.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.7 no.6
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• pp.1224-1228
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• 2003

The use of digital imaging technique and digital contents based on internet has grown rapidly for last several years, and the needs of digital image protection become more important. For the purpose of copyright protection on digital image, the verification of authentication techniques like content authentication, ownership authentication, illegal copy and etc are needs. Digital watermarking, the invisible encryption technique to insert digital watermarking into image, the sophisticated perceptual information should be used for providing transparency and robustness of images on watermarking process. In this paper, we implement the algorithm for preventing forged attack. ownership protection and authentication by transforming the wavelet algorithms in frequency domain in terms of human visual system.

• The KIPS Transactions:PartC
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• v.13C no.7 s.110
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• pp.859-864
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• 2006

The openness of the Web allows any user to access almost any type of information easily at any time and anywhere. However, with function of easy access for useful information, internet has dysfunctions of providing users with harmful contents indiscriminately. Some information, such as adult content, is not appropriate for all users, notably children. Additionally for adults, some contents included in abnormal porn sites can do ordinary people's mental health harm. In the meantime, since Internet is a worldwide open network it has a limit to regulate users providing harmful contents through each countrie's national laws or systems. Additionally it is not a desirable way of developing a certain system-specific classification technology for harmful contents, because internet users can contact with them in diverse way, for example, porn sites, harmful spams, or peer-to-peer networks, etc. Therefore, it is being emphasized to research and develop context-based core technologies for classifying harmful contents. In this paper, we propose an efficient text filter for blocking harmful texts of web documents using context-based technologies.

• Journal of Information Management
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• v.30 no.1
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• pp.48-67
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• 1999

In an advanced computer and information network technologies, users are rapidly approaching environment in which information will be created, delivered, managed and stored, using vastly different methods to traditional pap r based systems. Information seeking behavior contains not only physical access, but also cognitive process in which reading, viewing, analyzing, reasoning etc. The most Important factor determining the usability of electronic documents is cognitive process. But, currently Navigation tools for using electronic documents depend on physical searching without considering cognitive process. Therefore, this study examined feasibility of using overal cognitive process in developing navigation tools. Six cognitive process style have been analyzed to find common skill of information seeking process. Also this study suggests skills that needs to introduce to functions of navigation tool, such as searching, filtering, visualization, traversal, content structure.

• The Journal of the Korea Contents Association
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• v.21 no.7
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• pp.134-143
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• 2021

Recently, with the development of mobile devices and social media services, contents recommendation schemes have been studied. They are typically applied to the job curation systems. Most existing university education content recommendation schemes only recommend the most frequently taken subjects based on the student's school and major. Therefore, they do not consider the type or field of employment that each student wants. In this paper, we propose a university educational contents recommendation scheme for job curation services. The proposed scheme extracts companies that a user is interested in by analyzing his/her activities in the job curation system. The proposed scheme selects graduates or mentors based on the reliability and similarity of graduates who have been employed at the companies of interest. The proposed scheme recommends customized subjects, comparative subjects, and autonomous activity lists to users through collaborative filtering.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.4
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• pp.107-115
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• 2011

In this paper, we proposed fast and efficient algorithm for detecting near-duplication based on content based retrieval in large scale video database. For handling large amounts of video easily, we split the video into small segment using scene change detection. In case of video services and copyright related business models, it is need to technology that detect near-duplicates, that longer matched video than to search video containing short part or a frame of original. To detect near-duplicate video, we proposed motion distribution and frame descriptor in a video segment. The motion distribution descriptor is constructed by obtaining motion vector from macro blocks during the video decoding process. When matching between descriptors, we use the motion distribution descriptor as filtering to improving matching speed. However, motion distribution has low discriminability. To improve discrimination, we decide to identification using frame descriptor extracted from selected representative frames within a scene segmentation. The proposed algorithm shows high success rate and low false alarm rate. In addition, the matching speed of this descriptor is very fast, we confirm this algorithm can be useful to practical application.

• Journal of KIISE:Software and Applications
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• v.26 no.10
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• pp.1204-1218
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• 1999

문서영상 검색을 위한 디지털도서관의 대부분은 논문제목과/또는 논문요약으로부터 만들어진 색인에 근거한 제한적인 검색기능을 제공하고 있다. 본 논문에서는 영문 문서영상전체에 대한 검색을 위한 단어 영상 형태 특징기반의 단어검색시스템을 제안한다. 본 논문에서는 검색의 효율성과 정확도를 높이기 위해 1) 기존의 단어검색시스템에서 사용된 특징들을 조합하여 사용하며, 2) 특징의 개수 및 위치뿐만 아니라 특징들의 순서를 포함하여 매칭하는 방법을 사용하며, 3) 특징비교에 의해 검색결과를 얻은 후에 여과목적으로 문자인식을 부분적으로 적용하는 2단계의 검색방법을 사용한다. 제안된 시스템의 동작은 다음과 같다. 문서 영상이 주어지면, 문서 영상 구조가 분석되고 단어 영역들의 조합으로 분할된다. 단어 영상의 특징들이 추출되어 저장된다. 사용자의 텍스트 질의가 주어지면 이에 대응되는 단어 영상이 만들어지며 이로부터 영상특징이 추출된다. 이 참조 특징과 저장된 특징들과 비교하여 유사한 단어를 검색하게 된다. 제안된 시스템은 IBM-PC를 이용한 웹 환경에서 구축되었으며, 영문 문서영상을 이용하여 실험이 수행되었다. 실험결과는 본 논문에서 제안하는 방법들의 유효성을 보여주고 있다. Abstract Most existing digital libraries for document image retrieval provide a limited retrieval service due to their indexing from document titles and/or the content of document abstracts. This paper proposes a word spotting system for full English document image retrieval based on word image shape features. In order to improve not only the efficiency but also the precision of a retrieval system, we develop the system by 1) using a combination of the holistic features which have been used in the existing word spotting systems, 2) performing image matching by comparing the order of features in a word in addition to the number of features and their positions, and 3) adopting 2 stage retrieval strategies by obtaining retrieval results by image feature matching and applying OCR(Optical Charater Recognition) partly to the results for filtering purpose. The proposed system operates as follows: given a document image, its structure is analyzed and is segmented into a set of word regions. Then, word shape features are extracted and stored. Given a user's query with text, features are extracted after its corresponding word image is generated. This reference model is compared with the stored features to find out similar words. The proposed system is implemented with IBM-PC in a web environment and its experiments are performed with English document images. Experimental results show the effectiveness of the proposed methods.

• 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.

• Information Systems Review
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• v.18 no.2
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• pp.59-77
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• 2016

Although various recommendation techniques have been applied to the e-commerce market, few studies compare the intent to use these techniques from the customer's perspective. In this paper, we conduct a comparative analysis of customers' intention to use five recommendation techniques widely adapted by online shopping malls and focus on the differences in purchasing electronic goods and apparel products. The recommendation techniques are as follows: best-seller recommendation, merchandiser recommendation, content-based recommendation, collaborative filtering recommendation, and social recommendation. Additionally, we examine which factors influence customer intent to use the recommendation services. Data were collected through a survey administered to 220 e-commerce users with prior experience with recommendation services. Collected data were examined using analysis of variance and regression analysis. Results indicate statistically significant differences in customers' intention to use recommendation services according to the recommendation technique. In particular, the best-seller recommendation technique is preferred when purchasing electronic goods, whereas the content-based recommendation technique is preferred for apparel purchases. Factors such as personal characteristics and personality, purchasing tendency, as well as perception of the product or recommendation service affect a customer's intention to use a recommendation service. However, the influence of these factors varies depending on the recommendation technique. This study provides guidelines for companies to adopt appropriate recommendation techniques according to product categories and personal characteristics of customers.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.97-117
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• 2012

Nowadays, Internet users tend to do a variety of actions at the same time such as web browsing, social networking and multimedia consumption. While watching a video, once a user is interested in any product, the user has to do information searches to get to know more about the product. With a conventional approach, user has to search it separately with search engines like Bing or Google, which might be inconvenient and time-consuming. For this reason, a video annotation platform has been developed in order to provide users more convenient and more interactive ways with video content. In the future of smart TV environment, users can follow annotated information, for example, a link to a vendor to buy the product of interest. It is even better to enable users to search for information by directly discussing with friends. Users can effectively get useful and relevant information about the product from friends who share common interests or might have experienced it before, which is more reliable than the results from search engines. Social networking services provide an appropriate environment for people to share products so that they can show new things to their friends and to share their personal experiences on any specific product. Meanwhile, they can also absorb the most relevant information about the product that they are interested in by either comments or discussion amongst friends. However, within a very huge graph of friends, determining the most appropriate persons to ask for information about a specific product has still a limitation within the existing conventional approach. Once users want to share or discuss a product, they simply share it to all friends as new feeds. This means a newly posted article is blindly spread to all friends without considering their background interests or knowledge. In this way, the number of responses back will be huge. Users cannot easily absorb the relevant and useful responses from friends, since they are from various fields of interest and knowledge. In order to overcome this limitation, we propose a method to filter a user's friends for information search, which leverages semantic video annotation and social networking services. Our method filters and brings out who can give user useful information about a specific product. By examining the existing Facebook information regarding users and their social graph, we construct a user profile of product interest. With user's permission and authentication, user's particular activities are enriched with the domain-specific ontology such as GoodRelations and BestBuy Data sources. Besides, we assume that the object in the video is already annotated using Linked Data. Thus, the detail information of the product that user would like to ask for more information is retrieved via product URI. Our system calculates the similarities among them in order to identify the most suitable friends for seeking information about the mentioned product. The system filters a user's friends according to their score which tells the order of whom can highly likely give the user useful information about a specific product of interest. We have conducted an experiment with a group of respondents in order to verify and evaluate our system. First, the user profile accuracy evaluation is conducted to demonstrate how much our system constructed user profile of product interest represents user's interest correctly. Then, the evaluation on filtering method is made by inspecting the ranked results with human judgment. The results show that our method works effectively and efficiently in filtering. Our system fulfills user needs by supporting user to select appropriate friends for seeking useful information about a specific product that user is curious about. As a result, it helps to influence and convince user in purchase decisions.