• Proceedings of the Korean Society for Bioinformatics Conference
• /
• 2005.09a
• /
• pp.325-330
• /
• 2005

We are generally interested in the analysis, detection and prediction of structural motifs in proteins, in order to infer compatibility of amino acid sequence to structure in proteins of known three-dimensional structure available in the Protein Data Bank. In this context, we are analyzing some of the well-characterized structural motifs in proteins. We have analyzed simple structural motifs, such as, ${\beta}$-turns and ${\gamma}$-turns by evaluating the statistically significant type-dependent amino acid positional preferences in enlarged representative protein datasets and revised the amino acid preferences. In doing so, we identified a number of ‘unexpected’ isolated ${\beta}$-turns with a proline amino acid residue at the (i+2) position. We extended our study to the identification of multiple turns, continuous turns and to peptides that correspond to the combinations of individual ${\beta}$ and ${\gamma}$-turns in proteins and examined the hydrogen-bond interactions likely to stabilize these peptides. This led us to develop a database of structural motifs in proteins (DSMP) that would primarily allow us to make queries based on the various fields in the database for some well-characterized structural motifs, such as, helices, ${\beta}$-strands, turns, ${\beta}$-hairpins, ${\beta}$-${\alpha}$-${\beta}$, ${\psi}$-loops, ${\beta}$-sheets, disulphide bridges. We have recently implemented this information for all entries in the current PDB in a relational database called ODSMP using Oracle9i that is easy to update and maintain and added few additional structural motifs. We have also developed another relational database corresponding to amino acid sequences and their associated secondary structure for representative proteins in the PDB called PSSARD. This database allows flexible queries to be made on the compatibility of amino acid sequences in the PDB to ‘user-defined’ super-secondary structure conformation and vice-versa. Currently, we have extended this database to include nearly 23,000 protein crystal structures available in the PDB. Further, we have analyzed the ‘structural plasticity’ associated with the ${\beta}$-propeller structural motif We have developed a method to automatically detect ${\beta}$-propellers from the PDB codes. We evaluated the accuracy and consistency of predicting ${\beta}$ and ${\gamma}$-turns in proteins using the residue-coupled model. I will discuss results of our work and describe databases and software applications that have been developed.

• Journal of the Korea Convergence Society
• /
• v.11 no.8
• /
• pp.23-31
• /
• 2020

The need for personalized recommendation is growing due to convenient access and various types of items due to the development of information communication and smartphones. Weather and weather conditions have a great influence on the decision-making of users' places and activities. This weather information can increase users' satisfaction with recommendations. In this paper, we propose a collaborative filtering-based place recommendation system using living index by utilizing living index of users' location information on mobile platform to find users with similar propensity and to recommend places by predicting preferences for places. The proposed system consists of a weather module for analyzing and classifying users' weather, a recommendation module using collaborative filtering for place recommendations, and a management module for user preferences and post-management. Experiments have shown that the proposed system is valid in terms of the convergence of collaborative filtering algorithms and living indices and reflecting individual propensity.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.26 no.2
• /
• pp.301-314
• /
• 2016

Juels and Sudan's fuzzy vault scheme has been applied to various researches due to its error-tolerance property. However, the fuzzy vault scheme does not consider the difference between people's preferences, even though the authors instantiated movie lover' case in their paper. On the other hand, to make secure and high performance face authentication system, Nyang and Lee introduced a face authentication system, so-called fuzzy face vault, that has a specially designed association structure between face features and ordinary fuzzy vault in order to let each face feature have different weight. However, because of optimizing intra/inter class difference of underlying feature extraction methods, we can easily expect that the face authentication system does not successfully decrease the face authentication failure. In this paper, for ensuring the flexible use of the fuzzy vault scheme, we introduce the bucket structure, which differently implements the weighting idea of Nyang and Lee's face authentication system, and three distribution functions, which formalize the relation between user's weight of preferences and system implementation. In addition, we suggest a matchmaker scheme based on them and confirm its computational performance through the movie database.

• Journal of the Korean Society for information Management
• /
• v.27 no.3
• /
• pp.15-34
• /
• 2010

In this study we determine the level of awareness among academic librarians of ten technological tools as outlined in American Libraries. Towards this end, we conducted a survey targeting 156 academic librarians in 25 Korean university libraries. Questionnaires were designed to determine both the viability and level of acceptance of the ten technological proposals in question. Conclusions drawn after analyzing the responses to the survey were as follows: 1) Customer service can be improved by first drawing up a list of technological skills required for staff members. Methods to develop the cataloging service to more closely match individual user preferences and the use of SMS to send alerts proved to be the proposals, of the ten that were proposed, that not only bore the greatest necessity but also proved to be the most effective once they were implemented. 2) Proposals that proved to be the most difficult to implement were: Using technology to improve the cataloging service to make it more capable of evolving according to the individual preferences of users; the special event wiki for users; and improvements in customer service arising from identifying and drawing up a list of technological skills required for staff members.

• Information Systems Review
• /
• v.8 no.2
• /
• pp.139-154
• /
• 2006

Recommender systems help users find their interesting items more easily or provide users with meaningful items based on their preferences. Collaborative filtering(CF) recommender systems, the most successful recommender system, use opinions of users to recommend for an active user who needs recommendation. That is, ratings which users have voted on items to indicate preference on them are the source for making recommendation. Although CF systems are designed only to use users' preferences as the source of recommendation, use of some available information is believed to increase both the performance and the accuracy of CF systems. In this paper, we propose a CF recommender system which utilizes both degree of match and demographic information(e.g., occupation, gender, age) to increase the performance and the accuracy. Since more and more information is accumulated in CF systems, it is important to reduce the data volume while maintaining the same or the higher level of accuracy. We used both degree of match and demographic information as criteria for reducing the data volume, thereby naturally enhancing the performance. It is shown that using degree of match improves the prediction accuracy too in CF systems and also that using some demographic information also results in better accuracy.

• Journal of the Korean Society for Library and Information Science
• /
• v.52 no.1
• /
• pp.259-283
• /
• 2018

This research aims at understanding the differences between expert and general users' perceptions regarding publication preferences for Korean classics collations and translations in order to formulate future directions for these materials. For this purpose, an overview of changes in publishing in general as well as current status of collation/translation of Korean classics in particular are being identified. An online questionnaire was carried out in order to collect data regarding perceptions and preferences of expert users and general users of Korean classics. The results are based on the analyses of more than 1,000 responses. The analyses show that electronic books will not completely replace printed books and publishing both electronic and printed books in tandem for the time being is most preferable in order to satisfy varying user needs. Statistical analysis shows differences in terms of use value, value from possession, and readability of electronic and printed books in the two groups of users. However, as for the value of preservation by relevant institutions, there was a statistical difference between two groups towards printed books unlike their electronic equivalents. The research shows strong preference towards printed forms of classics collations and translations for the purpose of scholarly research and translation. Actual usage statistics reveal much heavy use of online database of classics translations compared to the use of available electronic books. For future publishing decisions for classics collations and translations will need to take into consideration of their special characteristics and symbolic nature. Proper representation of these materials into electronic format would require a standardized platform that enable various uses in different environments.

• Journal of Intelligence and Information Systems
• /
• v.27 no.2
• /
• pp.1-15
• /
• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Korean Institute of Interior Design Journal
• /
• v.21 no.1
• /
• pp.280-288
• /
• 2012

The purpose of this study is to deduct influence factors by analyzing preferences for public design that can increase user satisfaction through differentiated identity of regions. The study methods are as follows. First, consider a concept and semantic change of public design through the relevant literatures and precedent studies and then understand application status of domestic and overseas public design. Second, extract design elements for regional differentiation through precedent studies and reclassify regional resources that have an effect. Third, understand the necessity level of public design for regional differentiation by targeting experts and research the extracted design element and the degree of reflection about regionality. Lastly, understand a correlation between design elements and influence factors about territorial regionality of public design based on the researched contents, and analyze the degree of reflection between each other. As a result of this study, a plan that used 'material' and 'color' had high preference on the aspect of design elements and this is an important element that can show regional differentiation of regions. public design of showing regional differentiation. Thus, it is considered that various methods of using 'material' and 'color' must be planned. 'Public space' among design territories was most effective, and the following was 'public facility'. In particular, 'public space' has high preference of using 'natural resources' and 'industrial resources', and therefore an effect of a plan that uses these is judged to be positive. This study can use these results as basic data that suggested the standard for utilization of regional factors for regional differentiation of public design.