• Kyungpook Mathematical Journal
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• v.47 no.1
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• pp.57-68
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• 2007

In this article, we investigate the problem of uniqueness of meromorphic functions sharing one set and having deficient values, and obtain a result which improves some earlier results.

• Communications of the Korean Mathematical Society
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• v.35 no.3
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• pp.773-787
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• 2020

In this paper we consider the uniqueness of meromorphic functions when two linear differential polynomials share a set of values. Our result improves and rectifies a result of An and Khoai.

• Journal of the Korean Mathematical Society
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• v.58 no.3
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• pp.741-760
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• 2021

In this paper, we exhibit the equivalence between different notions of unique range sets, namely, unique range sets, weighted unique range sets and weak-weighted unique range sets under certain conditions. Also, we present some uniqueness theorems which show how two meromorphic functions are uniquely determined by their two finite shared sets. Moreover, in the last section, we make some observations that help us to construct other new classes of unique range sets.

• Kyungpook Mathematical Journal
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• v.49 no.1
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• pp.15-29
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• 2009

Dealing with a question of gross, we prove some uniqueness theorems concerning meromorphic functions with the notion of weighted sharing of sets. Our results will not only improve and supplement respectively two results of Lahiri-Banerjee [9] and Qiu and Fang [13] but also improve a very recent result of the present author [1].

• Journal of KIISE:Computer Systems and Theory
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• v.29 no.4
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• pp.213-219
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• 2002

A secret sharing scheme is a kind of cryptographic protocol to maintain secret information by splitting it to many small pieces of shares and sharing between shareholders. In case of shareholders having different authorization to reconstruct the original secret, it is required a new secret sharing scheme to reflect any hierarchical structure between shareholders. In this paper, we propose a new weighted secret sharing scheme, that is, each shareholder has a weight according to the authorization of reconstructing the secret and an access set which is a subset of shareholders can reconstruct the secret if the sum of weights is equal or greater than a predefined threshold.

• Proceedings of the IEEK Conference
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• 2000.11a
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• pp.287-290
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• 2000

This paper analyze link-sharing mechanisms in packet networks based on the hierarchical class based queueing. The CBQ outlines a set of flexible, efficiently implemented gateway mechanisms that can meet a range of service and link-sharing requirements. We have analyzed the Class level(B, C, D) using the EWMA (Exponential Weighted Moving Average) weight value and EWMA average limit value.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used