• Journal of Information Processing Systems
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• v.14 no.4
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• pp.961-979
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• 2018

For many years, matching in a bipartite graph has been widely used in various assignment problems, such as stable marriage problem (SMP). As an application of bipartite matching, the problem of stable marriage is defined over equally sized sets of men and women to identify a stable matching in which each person is assigned a partner of opposite gender according to their preferences. The classical SMP proposed by Gale and Shapley uses preference lists for each individual (men and women) which are infeasible in real world applications for a large populace of men and women such as matrimonial websites. In this paper, we have proposed an enhancement to the SMP by computing a weighted score for the users registered at matrimonial websites. The proposed enhancement has been formulated into profit maximization of matrimonial websites in terms of their ability to provide a suitable match for the users. The proposed formulation to maximize the profits of matrimonial websites leads to a combinatorial optimization problem. We have proposed greedy and genetic algorithm based approaches to solve the proposed optimization problem. We have shown that the proposed genetic algorithm based approaches outperform the existing Gale-Shapley algorithm on the dataset crawled from matrimonial websites.

• International Journal of Computer Science & Network Security
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• v.22 no.2
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• pp.232-240
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• 2022

To fulfill user expectations, the rapid evolution of software techniques and approaches has necessitated reliable and flawless software operations. Aging prediction in the software under operation is becoming a basic and unavoidable requirement for ensuring the systems' availability, reliability, and operations. In this paper, an improved evolutionary computing-driven extreme learning scheme (ECD-ELM) has been suggested for object-oriented software aging prediction. To perform aging prediction, we employed a variety of metrics, including program size, McCube complexity metrics, Halstead metrics, runtime failure event metrics, and some unique aging-related metrics (ARM). In our suggested paradigm, extracting OOP software metrics is done after pre-processing, which includes outlier detection and normalization. This technique improved our proposed system's ability to deal with instances with unbalanced biases and metrics. Further, different dimensional reduction and feature selection algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), and T-Test analysis have been applied. We have suggested a single hidden layer multi-feed forward neural network (SL-MFNN) based ELM, where an adaptive genetic algorithm (AGA) has been applied to estimate the weight and bias parameters for ELM learning. Unlike the traditional neural networks model, the implementation of GA-based ELM with LDA feature selection has outperformed other aging prediction approaches in terms of prediction accuracy, precision, recall, and F-measure. The results affirm that the implementation of outlier detection, normalization of imbalanced metrics, LDA-based feature selection, and GA-based ELM can be the reliable solution for object-oriented software aging prediction.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.63-73
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• 2023

This study explores modern portfolio theory by integrating the Black-Litterman portfolio with time-series clustering, specificially emphasizing K-shape clustering methodology. K-shape clustering enables grouping time-series data effectively, enhancing the ability to plan and manage investments in stock markets when combined with the Black-Litterman portfolio. Based on the patterns of stock markets, the objective is to understand the relationship between past market data and planning future investment strategies through backtesting. Additionally, by examining diverse learning and investment periods, it is identified optimal strategies to boost portfolio returns while efficiently managing associated risks. For comparative analysis, traditional Markowitz portfolio is also assessed in conjunction with clustering techniques utilizing K-Means and K-Means with Dynamic Time Warping. It is suggested that the combination of K-shape and the Black-Litterman model significantly enhances portfolio optimization in the stock market, providing valuable insights for making stable portfolio investment decisions. The achieved sharpe ratio of 0.722 indicates a significantly higher performance when compared to other benchmarks, underlining the effectiveness of the K-shape and Black-Litterman integration in portfolio optimization.

• The Journal of the Korea Contents Association
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• v.9 no.4
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• pp.55-64
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• 2009

For realizing ubiquitous computing, many research activities are geared towards various areas including embedded computing, RFID, USN, home networking, context-awareness, and etc. By using the ability of RFID technology to recognize a number of objects simultaneously, more convenient ubiquitous application services are effectively provided. In this case, RFID middleware playing a role as a bridge between RFID reader devices and application services is required as well. In this paper, we propose a method of handling a number of types of RFID reader devices in ALE middleware of EPCglobal. For this, the information of connection and data protocol for a reader device is stored in a database as ontology meta-data, and used to interpret the data read by a reader device. By adding ontology data into a database, even though an RFID device newly emerges, ALE middleware can not only handle the device, but also be effectively extended through reusing ontology data, without any changes in the middleware.

• Journal of Digital Contents Society
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• v.19 no.3
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• pp.453-460
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• 2018

Virtualization technology is characterized by the ability to isolate the user's system environment and to support the computing resources flexibly and extensively on demand. However, virtualization technology of cloud computing, which is already well known, must overload the guest OS and the hypervisor to manage it. Container technology is emerging to solve such OS-based virtualization problems. This technology can isolate the processes under which the application is running, thus creating a virtualization-like environment with minimal overhead. In this work, we construct a container-based education practice system using Docker instead of the existing cloud-based environment. To do this, we analyze the requirements for the establishment of the training practice environment. We also analyze the functions of the container and study the method to meet the requirements. This can take advantage of the existing flexible and scalable cloud computing. Also, it maximizes the availability of limited resources by minimizing the performance load.

• Journal of The Korean Association of Information Education
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• v.20 no.4
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• pp.357-366
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• 2016

The ability of computational thinking is a key competence that person of talent in the future should keep. Computational thinking is a serial process in which a problem is defined in context of computing, stages of abstraction are processed in order to find the efficient solution, the most appropriate process and resources for a solution are selected and combined through algorithms which use various concepts, principles and methods for automatic implementation of abstract concepts. It needs appropriate learning content in stage of elementary school. This study has verified the effect it made on improvement of learner's creative personality by developing and applying the educational content for software education based on the integrative production. The result of study confirmed that learning through the educational content for software education based on the integrative production affects improvement on learner's creativity positively and suggested a method of applying it to computing education in elementary school.

• The Journal of Korean Association of Computer Education
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• v.20 no.4
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• pp.59-66
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• 2017

In the fourth industrial revolution, the government has established a policy to cultivate the creative convergent talent with software literacy in order to cultivate human resources leading the economy. Therefore, software education based on computational thinking is being adopted in liberal arts courses in universities. However it would be damaged the fundamental objectives for increasing computational thinking by a weighted stress of students to learn programming of software education. In this paper, we proposed a teaching method that can improve computing thinking through information literacy education which can be directly applied in practice as a way to reduce the burden on the programming learning of software education. Through this paper, it was found that information literacy education can improve the computing thinking as well as the computing ability by making it possible to construct enough knowledge, analysis and efficient procedures for problems.

• Journal of Internet Computing and Services
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• v.14 no.4
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• pp.85-90
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• 2013

Currently, the electric fire prediction system uses PIC(Peripheral Interface Controller) for controller microprocessor. PIC has a slower computing speed than DSP does, so its real-time computing ability is inadequate. So with the basic characteristics waveform during arc generation as the standard reference, the comparison to this reference is used to predict and alarm electric fire from arc. While such alarm can be detected and taken care of from a remote central server, that prediction error rate is high and remote control in mobile environment is not available. In this article, the arc detection of time domain and frequency domain and wavelet-based adaptation algorithm executing the adaptation algorithm in conversion domain were applied to develop an electric fire prediction system loaded with new real-time arc detection algorithm using DSP. Also, remote control was made available through iPhone environment-based app development which enabled remote monitoring for arc's electric signal and power quality, and its utility was verified.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.2
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• pp.353-360
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• 2017

The Internet of Things(IoT) security has more need than a technical problem as it needs series of regulations and faultless security system for common purposes. So, this study proposes an efficient key management in order that can be trusted IoT data in cloud computing. In contrast with a key distribution center of existing sensor networks, the proposed a federation key management of cloud proxy key server is not central point of administration and enables an active key recovery and update. The proposed key management is not a method of predetermined secret keys but sharing key information of a cloud proxy key server in autonomous cloud, which can reduce key generation and space complexity. In addition, In contrast with previous IoT key researches, a federation key of cloud proxy key server provides an extraction ability from meaningful information while moving data.

• Journal of Internet Computing and Services
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• v.12 no.6
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• pp.83-94
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• 2011

Main researching issue in affective computing is to give a machine the ability to recognize the emotion of a person and to react it properly. Efforts in that direction have mainly focused on facial and oral cues to get emotions. Postures have been recently considered as well. This paper aims to discriminate emotions posture by identifying and measuring the saliency of posture features that play a role in affective expression. To do so, affective postures from human subjects are first collected using a motion capture system, then emotional features in posture are described with spatial ones. Through standard statistical techniques, we verified that there is a statistically significant correlation between the emotion intended by the acting subjects, and the emotion perceived by the observers. Discriminant Analysis are used to build affective posture predictive models and to measure the saliency of the proposed set of posture features in discriminating between 6 basic emotional states. The evaluation of proposed features and models are performed using a correlation between actor-observer's postures set. Quantitative experimental results show that proposed set of features discriminates well between emotions, and also that built predictive models perform well.