• Review of KIISC
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• v.28 no.1
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• pp.43-47
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• 2018

Textual passwords are widely used for accessing online accounts. Despite the problems of current textual passwords, research has shown that there is no other strong alternatives for a textual password due to its simplicity. There has been significant research to make passwords more secure and usable through password composition policies, password managers, password meters, and multi-factor authentications. In this paper, we focus on several key research that investigates and analyzes widely used password composition policies, and summarize the latest research which aims to improve current password composition policies.

• Proceedings of the Korean Society for Language and Information Conference
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• 2007.11a
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• pp.192-201
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• 2007

This paper is a study on constructing a natural language interface to database, concentrating on generating textual answers. TGEN, a system that generates textual answer from query result tables is presented. The TGEN architecture guarantees its portability across domains. A combination of a frame-based approach and natural language generation techniques in the TGEN provides text fluency and text flexibility. The implementation result shows that this approach is feasible while a deep NLG approach is still far to be reached.

• Fashion & Textile Research Journal
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• v.23 no.1
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• pp.13-30
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• 2021

The research expects that textual sources such as reading texts with additional information in the form of texts can be effective inspiration sources for fashion design ideation and development process. This research analyzes how efficiently textual sources work along with individual internal sources, such as sociocultural influence, design fixation, and during the design process. Six fashion design graduate students shared 2 inspirational experiences under 2 different studies (4 experiences in total); in addition, in-depth interviews were conducted based on individual design sketches. The result shows that textual sources provided a positive effect on all 6 participants with different intensities based on various backgrounds and individual tastes. This result demonstrates individual 'influence' (their sociocultural capital such as personal preferences, likings, habits, and past experiences) and 'inspiration' mutually work together to make an effect on fashion designers' ideation and development process for the design, sometimes one working more than the other (or vice versa), respectively. This paper makes important practical contributions by identifying and discussing the design behavior performed (especially in fashion design) by fashion design students during the design process with new sources of inspiration provided such as textual sources. The research revealed how textual sources can be an effective inspiration for fashion design students and provide insight to fashion design educators and professional fashion designers.

• Journal of the Korean Society for information Management
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• v.13 no.1
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• pp.19-43
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• 1996

The indexing languages and techniques were ~ u ~ e y e d for Korean textual databases, and retrieval effectivenesses of two indexing languages were evaluated in an online searching experiment. It was found that most of the Korean textual databases surveyed employ natural language indexing by either an automatic or a manual method, and that natural language indexing may outperform controlled language indexing if appropriate search strategies are employed.

• Proceedings of the Korean Society for Information Management Conference
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• 2001.11a
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• pp.7-24
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• 2001

• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.482-484
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• 1998

Abstract : Multimedia data now one of the widely used information in all the fields as the fast developments of computer techniques have been made. Traditional database systems based on textual information have limitations when applied to multimedia information. This is because simple textual descriptions are ambiguous and inadequate for searching multimedia information for multimedia databases and digital libraries. Thus, especially for image data, which is one of the important multimedia information types, which can retrieve and browse image data on the basis of pictorial queries. Therefore, this paper presents an efficient method for describing texture information in image data.

• Journal of KIISE:Software and Applications
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• v.35 no.10
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• pp.608-615
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• 2008

In Knowledge Q&A services where information is created by unspecified users, document quality is an important factor of user satisfaction with search results. Previous work on quality prediction of Knowledge Q&A documents evaluate the quality of documents by using non-textual information, such as click counts and recommendation counts, and focus on enhancing retrieval performance by incorporating the quality measure into retrieval model. Although the non-textual information used in previous work was proven to be useful by experiments, data sparseness problem may occur when predicting the quality of newly created documents with such information. To solve data sparseness problem of non-textual features, this paper proposes new features for document quality prediction, namely text-confidence features, which indicate how trustworthy the content of a document is. The proposed features, extracted directly from the document content, are stable against data sparseness problem, compared to non-textual features that indirectly require participation of service users in order to be collected. Experiments conducted on real world Knowledge Q&A documents suggests that text-confidence features show performance comparable to the non-textual features. We believe the proposed features can be utilized as effective features for document quality prediction and improve the performance of Knowledge Q&A services in the future.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3578-3593
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• 2017

Given two collections of objects that carry both spatial and textual information in the form of tags, a $\text\underline{S}patio$-$\text\underline{T}extual$-based object $\text\underline{S}imilarity$ $\text\underline{JOIN}$ (ST-SJOIN) retrieves the pairs of objects that are textually similar and spatially close. In this paper, we have proposed a block index-based approach called BIST-JOIN to facilitate the efficient ST-SJOIN processing. In this approach, a dual-feature distance plane (DFDP) is first partitioned into some blocks based on four segmentation schemes, and the ST-SJOIN is then transformed into searching the object pairs falling in some affected blocks in the DFDP. Extensive experiments on real and synthetic datasets demonstrate that our proposed join method outperforms the state-of-the-art solutions.

• The KIPS Transactions:PartD
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• v.13D no.2 s.105
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• pp.223-234
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• 2006

Branching and merging have been being the outstanding methods for SCM in terms of supporting parallel developments. Since well-known commercial merging tools based on textual merging have not detecting semantics conflicts, they can cause semantic errors in the result of merging. Although a lot of researches for detecting semantic conflict and merging up to recently, these researches have been doing individually. Therefore, it is necessary for a research detecting semantic conflict on textual merging and solving it. In this paper, we propose a new method for merging which preserve semantics on textual merging. The method merging two revisions from a source program is as follows : 1) defining changing operations, which include Update, Delete, and Insert operation, per line on two revisions corresponding to the line in source program, 2) detecting textual conflicts and semantic conflict in terms of executional behaviors, 3) solving these conflicts before merging. So, the proposed method can be regarded as a hybrid method that combines a method of textual merging and a behavioral semantic merging.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2222-2240
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• 2023

Brand fandom refers to a collection of consumers with strong emotions toward a brand. Studying the dynamics of brand fandom can help brands understand which services or strategies influence their consumers to become a part of brand fandom. However, existing literature on fandom in the last three decades has mainly used qualitative methods, and there is still a lack of research on fandom using quantitative methods. Specifically, previous studies lack a framework for locating fandoms from online textual data and analyzing their dynamics. This study proposes a framework for exploring brand fandom dynamics based on online textual data. This framework consists of four phases based on the design thinking model: Preparing Data, Defining Fandom Categories, Generating Fandom Dynamics, and Analyzing Fandom Dynamics. This framework uses techniques such as social network analysis and process mining, combined with brand personality theory. We demonstrate the applicability of this framework using case studies of two Korean home appliance brands. The dataset contains 14,593 posts by consumers in 374 online communities. The results show that the proposed framework can analyze brand fandom dynamics using textual customer data. Our study contributes to the interdisciplinary research at the intersection of data-driven service design and consumer culture quantification.