• Journal of the Korea Institute of Information Security & Cryptology
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• v.17 no.3
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• pp.109-116
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• 2007

In this paper, we analyse the security of the variant of Self-Shrinking generator proposed by Chang et al. against a guess-and-determine attack. This variant, which we call SSG-XOR is claimed to have better cryptographic properties than the Self-Shrinking generator in a practical setting. But we show that SSG-XOR is weaker than the Self-Shrinking generator from the viewpoint of guess-and-determine attack.

• Journal of the Korea Society of Computer and Information
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• v.20 no.9
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• pp.129-135
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• 2015

This study was conducted in order to identify factors that influence the will of self-reliance among workers participating in self-support programs. For this purpose, data were collected from self-support program participants in 2012 (1,301 participants in self-supporting labor programs and 128 in self-support community programs). Input variables analyzed as factors influencing the will of self-reliance were socio-demographic variables, self-esteem, and social capital. According to the results, first, self-supporting labor program participants' self-esteem was 2.328 out of 4 (S.M= .402) and it was somewhat lower than 2.406 (S.M=.404) in self-support community participants, but the difference was not significant. Self-supporting labor program participants' social capital was 2.980 out of 5 (S.M=.844), significantly lower than 3.356 (S.M=.815) in self-support community program participants. Self-supporting labor program participants' will of self-support was 4.150 out of 5 (S.M=.602), lower than 4.314 (S.M=.521) in self-support community program participants. Second, according to the results of regression analysis on self-support program participants' will of self-reliance, age (B=-.198, p<.001), self-esteem (B=.236, p<.001), and social capital (B=.166, p<.001) were found to have a significant effect. That is, the will of self-support was higher when age was young, when self-esteem was high, and when social capital was high. Based on these findings, this study suggested self-support policies, education systems, differentiated programs, etc. for enhancing self-support program participants' will of self-reliance.

• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.127-133
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• 2005

With the increasing use of the Internet improved Internet technologies as well as web-based applications, the uses of e-Learning have also increased the effectiveness of e-Learning has become one of the most practically and theoretically important issues in both Educational Engineering and Information Systems. This study suggests a research model, based on an e-Learning success model, the relationship of the e-leaner's self-regulated learning strategy and the quality perception of the e-Learning environment. This research model focuses on the learning environment and on self regulated learning strategy. The former consists of LMS, learning contents and interaction that are provided by e-Learning and the latter refers to the learners' self-regulated learning strategy. We will show the validity of the model empirically. As result, most of the hypotheses except for H6 suggested in this model were accepted.

• Science of Emotion and Sensibility
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• v.20 no.3
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• pp.71-88
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• 2017

This paper compared self-validation and regulatory focus between consumers who felt happy vs. sad prior to decision and explored the effects of self-validation on regulatory focus and information distortion. The results of empirical analysis are as follows. First, consumers who felt happy beforehand revealed larger self-validation and stronger promotion focus than those who felt sad in advance. Second, compared to sadness, just-felt happiness was found to have partially positive impact on promotion focus by means of self-validation and exercise entirely positive impact on information distortion through mediation of self-validation. This study has made theoretic contributions by identifying the differences in the extent of self-validation and promotion focus between happiness and sadness as ambient emotion felt prior to the impending decision making as well as by investigating the effects of self-validation upon information distortion.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.11
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• pp.1946-1958
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• 2011

Wireless Sensor Network (WSN) is made up of a number of sensor nodes and base stations. Traffic flow in WSN appears self-similar due to its data delivery process, and this impacts queue length greatly and makes queuing delay worse. Active queue management can be designed to improve QoS performance for WSN. In this paper, we propose self-similar traffic rate estimating algorithm named Power-Law Moving Averaging (PLMA) to regulate packet marking probability. This algorithm improves the availability of the rate estimation algorithm under the self-similar traffic condition. Then, we propose an adaptive Proportional Integral algorithm (SSPI) based on the estimation of the Self-Similar traffic rate by PLMA. Simulation results show that SSPI can achieve lower queue length jitter and smaller setting time than PI.

• Journal of Information Science Theory and Practice
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• v.1 no.1
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• pp.10-26
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• 2013

Domain transfer is a widely recognized problem for machine learning algorithms because models built upon one data domain generally do not perform well in another data domain. This is especially a challenge for tasks such as opinion classification, which often has to deal with insufficient quantities of labeled data. This study investigates the feasibility of self-training in dealing with the domain transfer problem in opinion classification via leveraging labeled data in non-target data domain(s) and unlabeled data in the target-domain. Specifically, self-training is evaluated for effectiveness in sparse data situations and feasibility for domain adaptation in opinion classification. Three types of Web content are tested: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. Findings of this study suggest that, when there are limited labeled data, self-training is a promising approach for opinion classification, although the contributions vary across data domains. Significant improvement was demonstrated for the most challenging data domain-the blogosphere-when a domain transfer-based self-training strategy was implemented.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.2
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• pp.244-259
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• 2021

The economic value of personal information has its importance as an objective measure of valuation in commercial, legal, and policy areas. Until recently, however, personal information subjects have not properly recognized the economic value of personal information, which has led to the inability to exercise the right to self-determination of personal information by unconsciously agreeing to the terms and conditions of personal information service without recognizing the value of personal information provided to the service provider when subscribing to a specific service. Therefore, we will examine the methodologies for calculating the economic value of personal information and the practical guarantee of the right to self-determination of personal information and analyze the economic value of personal information through a survey. Also, we would like to propose various ways for the subject of personal information with limited cognitive resources to visually accept the economic value of personal information required by the terms and conditions and suggest the optimal visualization of personal information economic value to exercise the right to self-determination of personal information. To do so, in this paper, we have conducted two survey experiments to estimate the economic value of personal information. Based on the price of personal information by category retrieved from surveys, we have visualized the price of personal information in various forms and asked respondents to choose the optimal infographic that best represents the value of personal information visually. As a result, we have proposed an optimal usage of the infographic to 'nudge' information subjects about their right to self-determination of personal information, therefore opening the possibility of diminishing privacy paradox.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1505-1509
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• 2005

This paper introduces a new architecture of genetically optimized self-organizing fuzzy polynomial neural networks by means of information granulation. The conventional SOFPNNs developed so far are based on mechanisms of self-organization and evolutionary optimization. The augmented genetically optimized SOFPNN using Information Granulation (namely IG_gSOFPNN) results in a structurally and parametrically optimized model and comes with a higher level of flexibility in comparison to the one we encounter in the conventional FPNN. With the aid of the information granulation, we determine the initial location (apexes) of membership functions and initial values of polynomial function being used in the premised and consequence part of the fuzzy rules respectively. The GA-based design procedure being applied at each layer of genetically optimized self-organizing fuzzy polynomial neural networks leads to the selection of preferred nodes with specific local characteristics (such as the number of input variables, the order of the polynomial, a collection of the specific subset of input variables, and the number of membership function) available within the network. To evaluate the performance of the IG_gSOFPNN, the model is experimented with using gas furnace process data. A comparative analysis shows that the proposed IG_gSOFPNN is model with higher accuracy as well as more superb predictive capability than intelligent models presented previously.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1743-1758
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• 2023

Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and open-set signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.

• Journal of Service Research and Studies
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• v.13 no.3
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• pp.20-38
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• 2023

Recently, car sharing has shown the most remarkable growth among sharing economy services. In the process of analyzing the intention to reuse the car sharing service, this study tried to reflect the unique characteristics of the service, which consists of non-face-to-face self-service, such as reservation, approval, handover, inspection, and return of the vehicle. Specifically, in addition to the perceived benefits and the perceived risks, we considered 'information quality' as a platform characteristic and 'self-efficacy' as a personal characteristic. To collect data, an online survey was conducted on adults with experience in car sharing, and a total of 320 responses were used for analysis. As a result of analyzing the structural equation model, it was found that information quality and self-efficacy increased the perceived benefits of services, and the higher the information quality, the higher the self-efficacy. On the other hand, the role of information quality and self-efficacy in lowering perceived risks was insignificant, and the intention to reuse services was more affected by perceived benefits than perceived risks. As a result of further analysis using Process Macro, it was found that the effect of self-efficacy on reuse intention was mediated by perceived benefits. It was analyzed that the indirect effects of information quality on reuse intention through perceived benefits or self-efficacy were all significant. These results suggest that providing timely, sufficient, and easy-to-understand information required by users on the platform improves self-efficacy and increases service reuse intention. In order to increase the number of service users, it is important for service providers not only to provide promotional activities such as offering attractive prices, but also to provide high-quality information so that users can use it more easily.