• International Journal of Fuzzy Logic and Intelligent Systems
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• v.11 no.4
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• pp.238-246
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• 2011

This paper proposes a new semantic representation and its associated similarity measure. The representation expresses textual context observed in a context of a certain term as a network where nodes are terms and edges are the number of cooccurrences between connected terms. To compare terms represented in networks, a graph kernel is adopted as a similarity measure. The proposed representation has two notable merits compared with previous semantic representations. First, it can process polysemous words in a better way than a vector representation. A network of a polysemous term is regarded as a combination of sub-networks that represent senses and the appropriate sub-network is identified by context before compared by the kernel. Second, the representation permits not only words but also senses or contexts to be represented directly from corresponding set of terms. The validity of the representation and its similarity measure is evaluated with two tasks: synonym test and unsupervised word sense disambiguation. The method performed well and could compete with the state-of-the-art unsupervised methods.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.73-85
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• 2011

Lots of valuable textual information is used to extract relations between named entities from literature. Composite kernel approach is proposed in this paper. The composite kernel approach calculates similarities based on the following information:(1) Phrase structure in convolution parse tree kernel that has shown encouraging results. (2) Predicate-argument structure patterns. In other words, the approach deals with syntactic structure as well as semantic structure using a reciprocal method. The proposed approach was evaluated using various types of test collections and it showed the better performance compared with those of previous approach using only information from syntactic structures. In addition, it showed the better performance than those of the state of the art approach.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.329-334
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• 2009

The ontology alignment has two kinds of major problems. First, the features used for ontology alignment are usually defined by experts, but it is highly possible for some critical features to be excluded from the feature set. Second, the semantic and the structural similarities are usually computed independently, and then they are combined in an ad-hoc way where the weights are determined heuristically. This paper proposes the modified parse tree kernel (MPTK) for ontology alignment. In order to compute the similarity between entities in the ontologies, a tree is adopted as a representation of an ontology. After transforming an ontology into a set of trees, their similarity is computed using MPTK without explicit enumeration of features. In computing the similarity between trees, the approximate string matching is adopted to naturally reflect not only the structural information but also the semantic information. According to a series of experiments with a standard data set, the kernel method outperforms other structural similarities such as GMO. In addition, the proposed method shows the state-of-the-art performance in the ontology alignment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2094-2112
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• 2019

Focusing on the issue that the conventional Kernel Correlation Filter (KCF) algorithm has poor performance in handling scale change and obscured objects, this paper proposes an anti-occlusion and scale adaptive tracking algorithm in the basis of KCF. The average Peak-to Correlation Energy and the peak value of correlation filtering response are used as the confidence indexes to determine whether the target is obscured. In the case of non-occlusion, we modify the searching scheme of the KCF. Instead of searching for a target with a fixed sample size, we search for the target area with multiple scales and then resize it into the sample size to compare with the learnt model. The scale factor with the maximum filter response is the best target scaling and is updated as the optimal scale for the following tracking. Once occlusion is detected, the model updating and scale updating are stopped. Experiments have been conducted on the OTB benchmark video sequences for compassion with other state-of-the-art tracking methods. The results demonstrate the proposed method can effectively improve the tracking success rate and the accuracy in the cases of scale change and occlusion, and meanwhile ensure a real-time performance.

• Journal of the Korean Institute of Telematics and Electronics
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• v.22 no.5
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• pp.39-44
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• 1985

For the application of (actory automation, a UNIX system with satellite processor, which can be categorized into master / slave hierarchical structure, is studied and implemented. The kernel part of UHIX is modified for the master/slavc structure, but it is designed fully compatible with the existing UNIX systems. 4 special user process is created to make easy to down load developed program from host to satellite processor with the concept of co-process. Also satellite processor can send messages and request retrain seNices from host computer. The design principles considered here art reliability, expamibility, and con-currency.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.921-929
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• 2011

In Information age, the academic liberal art Computer education course set up goals for promoting computer literacy and for developing the ability to cope actively with in Information Society and for improving productivity and competition among nations. In this paper, we analyze on discovering of decisive property and satisfaction index to have a influence on computer education on university students. As a preprocessing method, the proposed method select optimum property using correlation feature selection of machine learning tool based on Java and then we use multiclass least square support vector machine based on statistical learning theory. After applying that compare with multiclass support vector machine and multiclass least square support vector machine, we can see the fact that the proposed method have a excellent result like multiclass support vector machine in analysis of the academic liberal art computer education satisfaction index data.

• Journal of KIISE:Computer Systems and Theory
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• v.34 no.9
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• pp.407-415
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• 2007

In parallel cluster computing systems, the efficiency of communication between computing nodes is one of important factors that decide overall system performance. Accordingly, many researchers have studied on high-performance inter-node communication. The recently launched multi-core processor, however. increases the importance of intra-node communication as well because the more the number of cores in a node, the more the number of parallel processes running in the same node. Though there have been studies on intra-node communications, these have limited considerations on the state-of-the-art systems. In this paper, we propose a Linux kernel module that minimizes the number of data copy by exploiting the memory mapping mechanism for high-performance intra-node communication. The proposed kernel module supports the Linux kernel version 2.6. The performance measurements over a multi-core system present that the proposed kernel module can achieve lower latency up to 62% and higher throughput up to 144% than an existing kernel module approach. In addition, the measurements reveal that the performance of intra-node communication can vary significantly based on whether the cores that run the communication processes are belong to the same processor package (i.e., sharing the L2 cache).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.8
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• pp.1435-1442
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• 2006

The ideal method of safety evaluation is to verify results of execution against all possible operations within operating system, but it is impossible. However, the formal method can theoretically prove the safety on actual logic of operating system. Therefore we explain the contents of the art of the safety verification of security kernel, and make a comparative study of various standardized formal verification tools. And then we assigned PVS(Prototype Verification system) of SRI(Stanford Research Institute) to verify the safety of a modified BLP(Bell & LaPadula) model, the core access control model of multi-lavel based security kernel. Finally, we describe formal specification of the revised BLP model using the PVS, and evaluate the safety of the model by inspecting the specification of the PVS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2129-2147
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• 2019

This paper introduces an adaptive face recognition method based on a Novel Incremental Kernel Nonparametric Discriminant Analysis (IKNDA) that is able to learn through time. More precisely, the IKNDA has the advantage of incrementally reducing data dimension, in a discriminative manner, as new samples are added asynchronously. Thus, it handles dynamic and large data in a better way. In order to perform face recognition effectively, we combine the Gabor features and the ordinal measures to extract the facial features that are coded across local parts, as visual primitives. The variegated ordinal measures are extraught from Gabor filtering responses. Then, the histogram of these primitives, across a variety of facial zones, is intermingled to procure a feature vector. This latter's dimension is slimmed down using PCA. Finally, the latter is treated as a facial vector input for the advanced IKNDA. A comparative evaluation of the IKNDA is performed for face recognition, besides, for other classification endeavors, in a decontextualized evaluation schemes. In such a scheme, we compare the IKNDA model to some relevant state-of-the-art incremental and batch discriminant models. Experimental results show that the IKNDA outperforms these discriminant models and is better tool to improve face recognition performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.855-870
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• 2019

In this paper, an adaptive iterative algorithm is proposed for motion deblurring by using the salient intensity prior. Based on the observation that the salient intensity of the clear image is sparse, and the salient intensity of the blurred image is less sparse during the image blurring process. The salient intensity prior is proposed to enforce the sparsity of the distribution of the saliency in the latent image, which guides the blind deblurring in various scenarios. Furthermore, an adaptive iteration strategy is proposed to adjust the number of iterations by evaluating the performance of the latent image and the similarity of the estimated blur kernel. The negative influence of overabundant iterations in each scale is effectively restrained in this way. Experiments on publicly available image deblurring datasets demonstrate that the proposed algorithm achieves state-of-the-art deblurring results with small computational costs.