• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2069-2085
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• 2021

A Social Networking Service (SNS) platform provides digital footprints to discover users' interests and track the social diffusion of product adoptions. How to identify a small set of seed users in a SNS who is potential to adopt a new promoting product with high probability, is a key question in social networks. Existing works approached this as a social influence maximization problem. However, these approaches relied heavily on text information for topic modeling and neglected the impact of seed users' relation in the model. To this end, in this paper, we first develop a general product adoption function integrating both users' interest and social influence, where the user interest model relies on historical user behavior and the seed users' evaluations without any text information. Accordingly, we formulate a product adoption maximization problem and prove NP-hardness of this problem. We then design an efficient algorithm to solve this problem. We further devise a method to automatically learn the parameter in the proposed adoption function from users' past behaviors. Finally, experimental results show the soundness of our proposed adoption decision function and the effectiveness of the proposed seed selection method for product adoption maximization.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.10
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• pp.809-814
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• 2012

We propose an adaptive threshold detector algorithm for multi-level holographic data storage based on the expectation-maximization (EM) method. In this paper, the signal intensities that are passed through the four-level holographic channel are modeled as a four Gaussian mixture with unknown DC offsets and the threshold levels are estimated based on the maximum likelihood criterion. We compare the bit error rate (BER) performance of the proposed algorithm with the non-adaptive threshold detection algorithm for various levels of DC offset and misalignments. Our proposed algorithm shows consistently acceptable performance when the DC offset variance is fixed or the misalignments are lower than 20%. When the DC offset varies with each page, the BER of the proposed method is acceptable when the misalignments are lower than 10% and DC offset variance is 0.001.

• Journal of Communications and Networks
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• v.12 no.4
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• pp.317-329
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• 2010

In recent years, many sparse estimation methods, also known as compressed sensing, have been developed. However, most of these methods presume that the measurement matrix is completely known. We develop a new blind maximum likelihood method-the expectation-sparse-maximization (ESpaM) algorithm-for models where the measurement matrix is the product of one unknown and one known matrix. This method is a variant of the expectation-maximization algorithm to deal with the resulting problem that the maximization step is no longer unique. The ESpaM algorithm is justified theoretically. We present as well numerical results for two concrete examples of blind channel identification in digital communications, a doubly-selective channel model and linear time invariant sparse channel model.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.5
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• pp.102-109
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• 1985

A new yield maximization procedure by investigating method of the multidimensional Monte Carlo integration is presented. And then maximum yield is obtained by the new modified weight selection algorithm applied to objective function of MOSFET NAND GATE Also this yield maximization procedure can be applied to nonconvex objective function.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2299-2318
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• 2019

Location-aware networks are of great importance for both civil lives and military applications. Methods based on line-of-sight (LOS) measurements suffer sever performance loss in harsh environments such as indoor scenarios, where sensors can receive both LOS and non-line-of-sight (NLOS) measurements. In this paper, we propose a data association (DA) process based on the expectation maximization (EM) algorithm, which enables us to exploit multipath components (MPCs). By setting the mapping relationship between the measurements and scatters as a latent variable, coefficients of the Gaussian mixture model are estimated. Moreover, considering the misalignment of sensor position, we propose a space-alternating generalized expectation maximization (SAGE)-based algorithms to jointly update the target localization and sensor position information. A two dimensional (2-D) circularly symmetric Gaussian distribution is employed to approximate the probability density function of the sensor's position uncertainty via the minimization of the Kullback-Leibler divergence (KLD), which enables us to calculate the expectation step with low computational complexity. Moreover, a distributed implementation is derived based on the average consensus method to improve the scalability of the proposed algorithm. Simulation results demonstrate that the proposed centralized and distributed algorithms can perform close to the Monte Carlo-based method with much lower communication overhead and computational complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1263-1274
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• 2021

In online competitive social networks, each user can be influenced by different competing influencers and consequently chooses different products. But their interest may change over time and may have swings between different products. The existing influence spreading models seldom take into account the time-related shifts. This paper proposes a minimum cost influence maximization algorithm based on the competitive transition probability. In the model, we set a one-dimensional vector for each node to record the probability that the node chooses each different competing influencer. In the process of propagation, the influence maximization on Competitive Linear Threshold (IMCLT) spreading model is proposed. This model does not determine by which competing influencer the node is activated, but sets different weights for all competing influencers. In the process of spreading, we select the seed nodes according to the cost function of each node, and evaluate the final influence based on the competitive transition probability. Experiments on different datasets show that the proposed minimum cost competitive influence maximization algorithm based on IMCLT spreading model has excellent performance compared with other methods, and the computational performance of the method is also reasonable.

• Bulletin of the Korean Chemical Society
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• v.33 no.5
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• pp.1527-1535
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• 2012

Selection of the most informative molecular descriptors from the original data set is a key step for development of quantitative structure activity/property relationship models. Recently, mutual information (MI) has gained increasing attention in feature selection problems. This paper presents an effective mutual information-based feature selection approach, named mutual information maximization by replacing collinear variables (MIMRCV), for nonlinear quantitative structure-property relationship models. The proposed variable selection method was applied to three different QSPR datasets, soil degradation half-life of 47 organophosphorus pesticides, GC-MS retention times of 85 volatile organic compounds, and water-to-micellar cetyltrimethylammonium bromide partition coefficients of 62 organic compounds.The obtained results revealed that using MIMRCV as feature selection method improves the predictive quality of the developed models compared to conventional MI based variable selection algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.10
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• pp.1244-1246
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• 2016

Influence maximization problem is to select seed node set, which maximizes information spread in social networks. Greedy algorithm shows an optimum solution, but has a high computational cost. A few heuristic algorithms were proposed to reduce the complexity, but their performance in influence maximization is limited. In this paper, we propose general degree discount algorithm, and show that it has better performance while keeping complexity low.

• Journal of KIISE
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• v.44 no.10
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• pp.1105-1111
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• 2017

Influence maximization (IM) is the problem of finding a seed set composed of k nodes that maximizes the influence spread in social networks. However, one of the biggest problems of existing solutions for IM is that it takes too much time to select a k-seed set. This performance issue occurs at the micro and macro levels. In this paper, we propose a fast hybrid method that addresses two issues at micro and macro levels. Furthermore, we propose a path-based community detection method that helps to select a good seed set. The results of our experiment with four real-world datasets show that the proposed method resolves the two issues at the micro and macro levels and selects a good k-seed set.

• ETRI Journal
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• v.29 no.3
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• pp.300-304
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• 2007

This paper proposes a new data-driven method for high-pass approaches, which suppresses slow-varying noise components. Conventional high-pass approaches are based on the idea of decorrelating the feature vector sequence, and are trying for adaptability to various conditions. The proposed method is based on temporal local decorrelation using the information-maximization theory for each utterance. This is performed on an utterance-by-utterance basis, which provides an adaptive channel normalization filter for each condition. The performance of the proposed method is evaluated by isolated-word recognition experiments with channel distortion. Experimental results show that the proposed method yields outstanding improvement for channel-distorted speech recognition.