• Journal of KIISE
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• v.43 no.1
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• pp.96-105
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• 2016

Recently, social network services have changed by moving towards an open platform where, as well as simply allowing the building of relationships among users, various types of information can be generated and shared. Since existing user reputation management methods evaluate user reliability based on user profiles, explicit relations, and evaluation, they are not suitable for determining user reliability on social media due to few explicit evaluation. In this paper, we analyze social activities on social media and propose a new user reputation management method that considers implicit evaluation as well as explicit evaluation. The proposed method derives positive and negative implicit evaluation from social activities, and generates user reputation information by field in order to consider user expertise. It also considers the number of users that participate in evaluation in order to measure user influence. As a result, it generates the reputation information of users who have no explicit evaluation and creates user reputation information that is more suitable for social media.

• Knowledge Management Research
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• v.25 no.1
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• pp.111-134
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• 2024

Cosmetics brands are increasingly offering "multi-purpose" products that serve multiple functions at once, such as an eye cream that could also be used as a lip moisturizer. Yet, there is little research on consumers' perceptions of multi-purpose products and the brands that offer these products. This research examines the impact of consumers' own theory of beauty. We find that endorsing a strong incremental (vs. entity) theory of beauty (i.e., believing that one's beauty is changeable through one's effort rather than innate and unchangeable) reduces consumers' preferences for multi-purpose products, particularly among those who are highly appearance oriented. These consumers' reduced evaluation of multi-purpose products also spills over to their overall brand evaluation. The findings suggest that brand managers should consider consumers' implicit theory of beauty and product preferences when managing product knowledge.

• Journal of applied mathematics & informatics
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• v.6 no.2
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• pp.631-638
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• 1999

Sensitive testing has been widely employed for many years in connection with the development and evaluation explosives detonation devices and propellants. Perhaps its earliest and possibly most important implementation was in biological studies of dosage mortality and response to drugs. Recently sensitivity experiments has been employed in the evaluation of new materials subject to stress in various environments and in delineanation of unstable combustion regions in chemical propulsion systems. This paper discussed a sta-tistical development of sensitivity testing.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.197-206
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• 2006

One of the most significant characteristics of JPEG2000. the emerging still image standards. is the ROI (Region of Interest) coding. JPEG2000 provides a number of ROI coding mechanisms and ROI parameters. To apply them to an application, it must select the applicable values. In this paper, we evaluate how the ROI coding mechanisms and the ROI parameters influencing JPEG2000 qualify affect the ROI quality and the whole image quality. The ROI coding mechanisms are Maxshift and Implicit. and the parameters are tile size and ROI size, codeblock size, number of DWT decomposition levels and ROI importance. The bigger the tile size, the better the quality. The bigger the ROI size, the ROI importance and the number of DWT decomposition levels, the worse the qualify. In code block $32{\times}32$ of Maxshift and Implicit, it has the best qualify.

• Journal of computational fluids engineering
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• v.2 no.1
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• pp.8-12
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• 1997

The three-dimensional Euler equations are solved numerically for the analysis of contraction flows in wind or water tunnels. A second-order finite difference method is used for the spatial discretization on the nonstaggered grid system and the 4-stage Runge-Kutta scheme for the numerical integration in time. In order to speed up the convergence, the local time stepping and the implicit residual-averaging schemes are introduced. The pressure field is obtained by solving the pressure-Poisson equation with the Neumann boundary condition. For the evaluation of the present Euler solver, numerical computations are carried out for three contraction geometries, one of which was adopted in the Large Cavitation Channel for the U.S. Navy. The comparison of the computational results with the available experimental data shows good agreement.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10a
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• pp.163-164
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• 2007

• Journal for History of Mathematics
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• v.25 no.1
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• pp.57-70
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• 2012

We will research the elementary formal logic and mathematical concept represented in Mojing, and focus on the ancient Chinese's mathematical principles implicit in Mojing. Moreover, the mathematical significance and values of Mojing are examined.

• Journal of the Korean Geotechnical Society
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• v.15 no.6
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• pp.131-142
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• 1999

The objective of this study is to perform finite element analyses using the anisotropic hardening constitutive model on the basis of the total stress concept. An anisotropic hardening constitutive model had been developed in a companion paper, and was then formulated by implicit stress integration and consistent tangent moduli. A nonlinear finite element analysis program was coded including the algorithm, and as a result, the nonlinear solution was accurately calculated and converged to be asymptotically quadratic. In the analysis of a test embankment it was found that the proposed model could predict the displacement of soils more reasonably than the analysis with von Mises type model. In addition the proposed model could predict accurately the actual behavior through the reanalysis of the problem by a reasonable evaluation of the strength parameter.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.23 no.4
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• pp.337-344
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• 2014

While implicit integration methods such as Newton's method have excellent stability for the analysis of stiff and constrained mechanical systems, they have the drawback that the evaluation and LU-factorization of the system Jacobian matrix required at every time step are time-consuming. This paper proposes a Jacobian update-free Newton's method in order to overcome these defects. Because the motions of all bodies in a vehicle model are limited with respect to the chassis body, the equations are formulated with respect to the moving chassis-body reference frame instead of the fixed inertial reference frame. This makes the system Jacobian remain nearly constant, and thus allows the Newton's method to be free from the Jacobian update. Consequently, the proposed method significantly decreases the computational cost of the vehicle dynamic simulation. This paper provides detailed generalized formulation procedures for the equations of motion, constraint equations, and generalized forces of the proposed method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.1
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• pp.454-475
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• 2018

This paper presents a novel Android malware classification model planned to classify and categorize Android malicious code at Drebin dataset. The amount of malicious mobile application targeting Android based smartphones has increased rapidly. In this paper, Restricted Boltzmann Machine and Deep Belief Network are used to classify malware into families of Android application. A texture-fingerprint based approach is proposed to extract or detect the feature of malware content. A malware has a unique "image texture" in feature spatial relations. The method uses information on texture image extracted from malicious or benign code, which are mapped to uncompressed gray-scale according to the texture image-based approach. By studying and extracting the implicit features of the API call from a large number of training samples, we get the original dynamic activity features sets. In order to improve the accuracy of classification algorithm on the features selection, on the basis of which, it combines the implicit features of the texture image and API call in malicious code, to train Restricted Boltzmann Machine and Back Propagation. In an evaluation with different malware and benign samples, the experimental results suggest that the usability of this method---using Deep Belief Network to classify Android malware by their texture images and API calls, it detects more than 94% of the malware with few false alarms. Which is higher than shallow machine learning algorithm clearly.