Jae Wook Lee

Cucurbit[n]uril Derivatives Soluble in Water and Organic Solvents

Reminiscent of the ringed planet Saturn, new cucurbit[n]uril (CB[n]) derivatives CB*[5] and CB*[6] have rings decorating the equator. The rings in this case are five- and six-fused cyclohexane rings, respectively (the structure of CB*[6] is shown). The remarkable solubility of the new CB[n] derivatives in water and organic solvents allows not only their unusual binding properties toward metal and organic ions in neutral water but also their applications in ion-selective electrodes to be studied.

Rotaxane-based molecular switch with fluorescence signaling

Abstract A pseudorotaxane containing cucurbituril (as a molecular ‘bead’) threaded on fluorenyltriamine (as a ‘string’) behaves as a fluorescent, reversible molecular switch. The switching of the molecular bead from one site to the other site on the ‘string’ induced by pH change is easily detected by change in color and fluorescence.

Growth of poly(pseudorotaxane) on gold using host-stabilized charge-transfer interaction

A novel supramolecular polymer (poly(pseudorotaxane)) in which the repeating units are linked by host-stabilized charge-transfer interaction between the guest molecules is grown on gold and characterized.

A novel method for measuring semantic similarity for XML schema matching

Enterprises integration has recently gained great attentions, as never before. The paper deals with an essential activity enabling seamless enterprises integration, that is, a similarity-based schema matching. To this end, we present a supervised approach to measure semantic similarity between XML schema documents, and, more importantly, address a novel approach to augment reliably labeled training data from a given few labeled samples in a semi-supervised manner. Experimental results reveal the proposed method is very cost-efficient and reliably predicts semantic similarity.

Designing of Variables Repetitive Group Sampling Plan Involving Minimum Average Sample Number

This article proposes the variables repetitive group sampling plan where the quality characteristic follows normal distribution or lognormal distribution and has upper or lower specification limit. The problem is formulated as a nonlinear programming problem where the objective function to be minimized is the average sample number and the constraints are related to lot acceptance probabilities at acceptable quality level (AQL) and limiting quality level (LQL) under the operating characteristic curve. Sampling plan tables are constructed for the selection of parameters indexed by AQL and LQL in the cases of known standard deviation and unknown standard deviation. It is shown that the proposed sampling plan significantly reduces the average sample number as compared with the single and double sampling plans.

Classification-based collaborative filtering using market basket data

Collaborative filtering based on voting scores has been known to be the most successful recommendation technique and has been used in a number of different applications. However, since voting scores are not easily available, similar techniques should be needed for the market basket data in the form of binary user-item matrix. We viewed this problem as a two-class classification problem and proposed a new recommendation scheme using binary logistic regression models applied to binary user-item data. We also suggested using principal components as predictor variables in these models. The proposed scheme was illustrated with a numerical experiment, where it was shown to outperform the existing one in terms of recommendation precision in a blind test.

An efficient and practical method for the synthesis of mono-N-protected α,ω-diaminoalkanes

Abstract The large-scale synthesis of mono- N -protected (Cbz, Boc, Ts, and Ns) α,ω-diaminoalkanes (the number of carbon atoms=3, 4, 5 and 6) are accomplished in 81–94% yield by the protection of amine and subsequent reduction of an azido group from α,ω-azido alkyl amines. α,ω-Azido alkyl amines are prepared efficiently by the partial reduction of α,ω-diazidoalkanes which are obtained from the corresponding dibromoalkanes.

An iterative semi-explicit rating method for building collaborative recommender systems

Collaborative filtering plays the key role in recent recommender systems. It uses a user-item preference matrix rated either explicitly (i.e., explicit rating) or implicitly (i.e., implicit feedback). Despite the explicit rating captures the preferences better, it often results in a severely sparse matrix. The paper presents a novel iterative semi-explicit rating method that extrapolates unrated elements in a semi-supervised manner. Extrapolation is simply an aggregation of neighbor ratings, and iterative extrapolations result in a dense preference matrix. Preliminary simulation results show that the recommendation using the semi-explicit rating data outperforms that of using the pure explicit data only.

Improved churn prediction in telecommunication industry by analyzing a large network

Customer retention in telecommunication companies is one of the most important issues in customer relationship management, and customer churn prediction is a major instrument in customer retention. Churn prediction aims at identifying potential churning customers. Traditional approaches for determining potential churning customers are based only on customer personal information without considering the relationship among customers. However, the subscribers of telecommunication companies are connected with other customers, and network properties among people may affect the churn. For this reason, we proposed a new procedure of the churn prediction by examining the communication patterns among subscribers and considering a propagation process in a network based on call detail records which transfers churning information from churners to non-churners. A fast and effective propagation process is possible through community detection and through setting the initial energy of churners (the amount of information transferred) differently in churn date or centrality. The proposed procedure was evaluated based on the performance of the prediction model trained with a social network feature and traditional personal features.

User credit-based collaborative filtering

Memory-based collaborative filtering is the state-of-the-art method in recommender systems and has proven to be successful in various applications. In this paper we develop novel memory-based methods that incorporate the level of a user credit instead of using similarity between users. The user credit is the degree of ones rating reliability that measures how adherently the user rates items as others do. Preliminary simulation results show that the proposed methods outperform the conventional memory-based ones. The methods are effective in a cold-starting problem.