• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5877-5897
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• 2018

With the popularity of mobile networks and smartphones, geo-textual publish/subscribe messaging has attracted wide attention. Different from the traditional publish/subscribe format, geo-textual data is published and subscribed in the form of dynamic data flow in the mobile network. The difference creates more requirements for efficiency and flexibility. However, most of the existing Top-k geo-textual publish/subscribe schemes have the following deficiencies: (1) All publications have to be scored for each subscription, which is not efficient enough. (2) A user should take time to set a threshold for each subscription, which is not flexible enough. Therefore, we propose an efficient and flexible Top-k geo-textual publish/subscribe scheme. First, our scheme groups publish and subscribe based on text classification. Thus, only a few parts of related publications should be scored for each subscription, which significantly enhances efficiency. Second, our scheme proposes an adaptive publish/subscribe matching algorithm. The algorithm does not require the user to set a threshold. It can adaptively return Top-k results to the user for each subscription, which significantly enhances flexibility. Finally, theoretical analysis and experimental evaluation verify the efficiency and effectiveness of our scheme.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.45-48
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• 2008

Using these systems, additional information on the paintings and exhibits may be provided in the forms of text, image, speech, and video However, at museums and exhibitions, many tourists are often interested in exhibits of some particular style, authors, or coteries. The proposed Augmented Reality based guidance system may guide the users to exhibits of their interest for selective viewing. Location of the next exhibit of interest may be informed to the users as well as additional multimedia information on the exhibits of interest Such information is shown on the Augmented Reality views of the user's display device. The proposed system is composed an Ultra-Mobile PC (UMPC), an inertia tracker, and a camera. In the beginning, the user may select his/her preference on the exhibits from the menu, and then the system starts guiding by showing the relative orientation, distance, and visual cue to find a next exhibit. When the user finds and locates the matching visual cue within a matching box of the display screen, the system provides multimedia information on the exhibit. According to the preliminary user test, the proposed system is convenient and useful for navigating through large-scale exhibition.

• Journal of Korea Multimedia Society
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• v.10 no.7
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• pp.807-816
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• 2007

Since W3C proposed XML in 1996, XML documents have been widely spreaded in many internet documents. Because of this, needs for research related with XML is increasing. Especially, it is being well performed to study XML management system for storage, retrieval, and management with XML Documents. Among these studies, XRel is a representative study for XML management and has been become a comparative study. In this study, we suggest XML documents management system based on Relational DataBase Management System. This system is stored not all possible path expressions such as XRel, but filtered path expression which has text value or attribute value. And by giving each node Node Expression Identifier, we try to match given Node Expression Identifier. Finally, to prove efficiency of the suggested technique, this paper shows the result of experiment that compares XPath query processing performance between suggested study and existing technique, XRel.

• Korean Journal of Social Welfare
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• v.58 no.2
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• pp.5-31
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• 2006

Since enforcement of the anti-prostitution law, in spite of systematic setting helping escape prostitution of the women who engage in prostitution that they have had the will lasting prostitution. Therefore, this study aimed to devise intervention plan helping their escape prostitution and return to social by examining individual and structural factor obstructing their escape prostitution The data were collected through the in-depth interview and text. And these were analysed according to coding, constitution of concept, matching, construction of explanation on the phenomenon. The nine women who engaging in prostitution were participated in this study. As a result of the data analysis, 46 concepts and 10 categories were generated. By classification of individual and structural factor, the outcomes of an interpretation were as follows: The cause obstructing Prostitutes' escape prostitution were (1) distrust on the policy of the government, (2) life-script was made by reaction-formation, (3) predestined resignation caused by anxiety, (4) body as capital goods, and (5) the commensal model with pimp. Based on this result, we proposed practical and political alternative plans for prostitutes.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.11C
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• pp.929-939
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• 2010

The growth of multimedia technology forces the development of video detection for large database management and illegal copy detection. To meet this demand, this paper proposes a fast video detection method to apply to a large database. The fast video detection algorithm uses spatial features using the gray value distribution from frames and temporal features using the temporal similarity map. We form the video signature using the extracted spatial feature and temporal feature, and carry out a stepwise matching method. The performance was evaluated by accuracy, extraction and matching time, and signature size using the original videos and their modified versions such as brightness change, lossy compression, text/logo overlay. We show empirical parameter selection and the experimental results for the simple matching method using only spatial feature and compare the results with existing algorithms. According to the experimental results, the proposed method has good performance in accuracy, processing time, and signature size. Therefore, the proposed fast detection algorithm is suitable for video detection with the large database.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.6
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• pp.1231-1238
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• 2021

Recently, large online shopping malls are providing image search services as well as text or category searches. However, in the case of an image search service, there is a problem in that the search service cannot be used in the absence of an image. This paper describes the development of a system that allows users to find the clothes they want through hand-drawn images of the style of clothes when they search for clothes in an online clothing shopping mall. The hand-drawing data drawn by the user increases the accuracy of matching through neural network learning, and enables matching of clothes using various object detection algorithms. This is expected to increase customer satisfaction with online shopping by allowing users to quickly search for clothing they are looking for.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.1-23
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• 2013

To discover significant social issues such as unemployment, economy crisis, social welfare etc. that are urgent issues to be solved in a modern society, in the existing approach, researchers usually collect opinions from professional experts and scholars through either online or offline surveys. However, such a method does not seem to be effective from time to time. As usual, due to the problem of expense, a large number of survey replies are seldom gathered. In some cases, it is also hard to find out professional persons dealing with specific social issues. Thus, the sample set is often small and may have some bias. Furthermore, regarding a social issue, several experts may make totally different conclusions because each expert has his subjective point of view and different background. In this case, it is considerably hard to figure out what current social issues are and which social issues are really important. To surmount the shortcomings of the current approach, in this paper, we develop a prototype system that semi-automatically detects social issue keywords representing social issues and problems from about 1.3 million news articles issued by about 10 major domestic presses in Korea from June 2009 until July 2012. Our proposed system consists of (1) collecting and extracting texts from the collected news articles, (2) identifying only news articles related to social issues, (3) analyzing the lexical items of Korean sentences, (4) finding a set of topics regarding social keywords over time based on probabilistic topic modeling, (5) matching relevant paragraphs to a given topic, and (6) visualizing social keywords for easy understanding. In particular, we propose a novel matching algorithm relying on generative models. The goal of our proposed matching algorithm is to best match paragraphs to each topic. Technically, using a topic model such as Latent Dirichlet Allocation (LDA), we can obtain a set of topics, each of which has relevant terms and their probability values. In our problem, given a set of text documents (e.g., news articles), LDA shows a set of topic clusters, and then each topic cluster is labeled by human annotators, where each topic label stands for a social keyword. For example, suppose there is a topic (e.g., Topic1 = {(unemployment, 0.4), (layoff, 0.3), (business, 0.3)}) and then a human annotator labels "Unemployment Problem" on Topic1. In this example, it is non-trivial to understand what happened to the unemployment problem in our society. In other words, taking a look at only social keywords, we have no idea of the detailed events occurring in our society. To tackle this matter, we develop the matching algorithm that computes the probability value of a paragraph given a topic, relying on (i) topic terms and (ii) their probability values. For instance, given a set of text documents, we segment each text document to paragraphs. In the meantime, using LDA, we can extract a set of topics from the text documents. Based on our matching process, each paragraph is assigned to a topic, indicating that the paragraph best matches the topic. Finally, each topic has several best matched paragraphs. Furthermore, assuming there are a topic (e.g., Unemployment Problem) and the best matched paragraph (e.g., Up to 300 workers lost their jobs in XXX company at Seoul). In this case, we can grasp the detailed information of the social keyword such as "300 workers", "unemployment", "XXX company", and "Seoul". In addition, our system visualizes social keywords over time. Therefore, through our matching process and keyword visualization, most researchers will be able to detect social issues easily and quickly. Through this prototype system, we have detected various social issues appearing in our society and also showed effectiveness of our proposed methods according to our experimental results. Note that you can also use our proof-of-concept system in http://dslab.snu.ac.kr/demo.html.

• KIISE Transactions on Computing Practices
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• v.22 no.6
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• pp.290-295
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• 2016

Recently, folksonomy-based image-sharing sites where users cooperatively make and utilize tags of image annotation have been gaining popularity. Typically, these sites retrieve images for a user request using simple text-based matching and display retrieved images in the form of photo stream. However, these tags are personal and subjective and images are not categorized, which results in poor retrieval accuracy and low user satisfaction. In this paper, we propose a categorization scheme for folksonomy images which can improve the retrieval accuracy in the tag-based image retrieval systems. Consequently, images are classified by the semantic similarity using text-information and image-information generated on the folksonomy. To evaluate the performance of our proposed scheme, we collect folksonomy images and categorize them using text features and image features. And then, we compare its retrieval accuracy with that of existing systems.

• Journal of KIISE:Software and Applications
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• v.28 no.11
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• pp.795-813
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• 2001

Sophisticated geometric structure analysis must be preceded to create electronic document from logical components extracted from document image. this paper presents a knowledge-based method for sophisticated geometric structure analysis of technical journal pages. The proposed knowledge base encodes geometric characteristics that are not only common in technical journals but also publication-specific in the form rules. The method takes the hybrid of top-down and bottom-up techniques and consists of two phases: region segmentation and identification. Generally, the result of segmentation process does not have a one-to-one matching with composite layout components. Therefore, the proposed method identifies non-text objects such as image, drawing and table, as well as text objects such as text line and equation by splitting or grouping segmented regions into composite layout components. Experimental results with 372 images scanned from the IEEE Transactions on Pattern Analysis and Machine Intelligence show that the proposed method has performed geometrical structure analysis successfully on more than 99% of the test images, resulting in sophisticated performance compared with previous works.

• 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.