• Journal of Science of Art and Design
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• v.8
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• pp.241-266
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• 2005

Current information-oriented society is expanding rapidly with the huge change to our society from the information revolution emerged in mid '50s. Information technique such as computer, communication satellite and cable technology play their in this information-oriented society, and in 1980s, digital technique was adopted to media, inviting new phenomenon in communication of overall society. Expectations and hope for new trends and changes from digital era flourished when we entered into new Millenium, the 21st century. Since then we have gone through changes in years from analogue to digital. Now the word 'digital' is conceived in unified manner in almost every fields and influences overall society, changing our life pattern rapidly. Modern society had demanded some speedy and bidirectional communication tool in shifting digital era, and resulted from that was a new culture, Now the society changes more and more quickly under the speedy information sharing. From that perspective we need to establish the aspects of sensibility expression, which is one feature of the communication. The sensibility-oriented digital was originated from the human nature, which counts emotion and composure much. To satisfy the human nature a new cultural value which transcend a traditional one is needed. And the sensitivity expression to meet the need is one communication tendency in digital era. Digital era has totally different social qualities from that of analogue era. In digital era, prompt respond to rapid change and creativity combined with sensitivity are needed. Therefore a man with sensitivity would be more outstanding than a man of intelligence. The picture of digital and analogue in our society consists of two axis one is analogue axis like retrospective trend, admiration for nature , oriental thought etc. , and the other is the digital axis of internet and information technology. Sensibility can be widely interpreted as analogue thinking. It is surely different from the plain retrospective trend or nostalgia for past. It is a tendency that digital technology is used and cooperated with the emotion in human nature, In this paper we suggest the sensitivity expressions in detail through the study cases including both sides of digital and sensitivity expressions such as 'Bravo your life' advertising campaign of Samsung Insurance, MP3 advertising campaign, and 'Mini-homepage' of Cyworld. Under the circumstances the main direction of communication is oriented to human. This study's purpose is to think the true meaning of this digital era, to overcoming its weaknesses, such as over-dependency on technique and selfish individualism and to harmonize digital and sensitivity expression. The sensitivity-oriented expression than the reason-oriented one is expected to be put forth in whole media, and this is a more efficient tendency under the shifting digital era.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.6
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• pp.366-373
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• 2020

This study explored the factors associated with dependence among smartphone-dependent adults in their 20s. The data was derived from the 2017 survey on smartphone over-dependence conducted by the Ministry of Science and ICT and the National Information Society Agency. The participants were 879 adults in their 20s. The data was analyzed by frequencies, percentages, means, standard deviations, independent t-tests, Pearson's correlation coefficients, and multiple regression analysis. The results revealed instant messengers as the most used application by participants. Participants in the high risk category of dependence also used SNS (Social Networking Services), music, and games more than those in the potential risk category. The more serious the dependence, the greater the frequency of smartphone use (β=.16, p=.000), and use of games (β=.10, p=.028), webtoons (β=.14, p=.004), SNS (β=.09, p=.047), and financial transactions (β=.17, p=.000). They did not recognize their smartphone dependence when it was relatively low. However, when this became serious, they then realized that they depended on the smartphone more than others. That means that it is not easy for adults to recognize their smartphone dependence on their own. However, recognition of the problem is the first step for adults to solve their problems. A program that evaluates their problematic smartphone use should be installed and used on all smartphones.

• Journal of KIISE:Information Networking
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• v.36 no.5
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• pp.425-436
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• 2009

In recent years, the user demands have increased for multimedia service of high quality over the broadband convergence network. These rising demands for high quality multimedia service led the popularization of various user terminals and large scale display equipments, which needs a variety type of QoS (Quality of Service). In order to support demands for QoS, numerous research projects are in progress both from the perspective of network as well as end system; For example, at the network perspective, QoS guaranteeing by improving of internet performance such as Active Queue Management, while at the end system perspective, SVC (Scalable Video Coding) encoding scheme to guarantee media quality. However, existing AQM algorithms have problems which do not guarantee QoS, because they did not consider the essential characteristics of video encoding schemes. In this paper, it is proposed to solve this problem by deploying the TS- AQM (Temporal Scalability Active Queue Management) which employs the differentiated packet dropping for dependency of the temporal level among the frames, based on SVC encoding characteristics by exploiting the TID (Temporal ID) field of the SVC NAL unit header. The proposed TS-AQM guarantees multimedia service quality through video decoding reliability for SVC streaming service, by differentiated packet dropping when congestion exists.

• Journal of Intelligence and Information Systems
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• v.21 no.1
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• pp.119-142
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• 2015

With the explosive growth in the volume of information, Internet users are experiencing considerable difficulties in obtaining necessary information online. Against this backdrop, ever-greater importance is being placed on a recommender system that provides information catered to user preferences and tastes in an attempt to address issues associated with information overload. To this end, a number of techniques have been proposed, including content-based filtering (CBF), demographic filtering (DF) and collaborative filtering (CF). Among them, CBF and DF require external information and thus cannot be applied to a variety of domains. CF, on the other hand, is widely used since it is relatively free from the domain constraint. The CF technique is broadly classified into memory-based CF, model-based CF and hybrid CF. Model-based CF addresses the drawbacks of CF by considering the Bayesian model, clustering model or dependency network model. This filtering technique not only improves the sparsity and scalability issues but also boosts predictive performance. However, it involves expensive model-building and results in a tradeoff between performance and scalability. Such tradeoff is attributed to reduced coverage, which is a type of sparsity issues. In addition, expensive model-building may lead to performance instability since changes in the domain environment cannot be immediately incorporated into the model due to high costs involved. Cumulative changes in the domain environment that have failed to be reflected eventually undermine system performance. This study incorporates the Markov model of transition probabilities and the concept of fuzzy clustering with CBCF to propose predictive clustering-based CF (PCCF) that solves the issues of reduced coverage and of unstable performance. The method improves performance instability by tracking the changes in user preferences and bridging the gap between the static model and dynamic users. Furthermore, the issue of reduced coverage also improves by expanding the coverage based on transition probabilities and clustering probabilities. The proposed method consists of four processes. First, user preferences are normalized in preference clustering. Second, changes in user preferences are detected from review score entries during preference transition detection. Third, user propensities are normalized using patterns of changes (propensities) in user preferences in propensity clustering. Lastly, the preference prediction model is developed to predict user preferences for items during preference prediction. The proposed method has been validated by testing the robustness of performance instability and scalability-performance tradeoff. The initial test compared and analyzed the performance of individual recommender systems each enabled by IBCF, CBCF, ICFEC and PCCF under an environment where data sparsity had been minimized. The following test adjusted the optimal number of clusters in CBCF, ICFEC and PCCF for a comparative analysis of subsequent changes in the system performance. The test results revealed that the suggested method produced insignificant improvement in performance in comparison with the existing techniques. In addition, it failed to achieve significant improvement in the standard deviation that indicates the degree of data fluctuation. Notwithstanding, it resulted in marked improvement over the existing techniques in terms of range that indicates the level of performance fluctuation. The level of performance fluctuation before and after the model generation improved by 51.31% in the initial test. Then in the following test, there has been 36.05% improvement in the level of performance fluctuation driven by the changes in the number of clusters. This signifies that the proposed method, despite the slight performance improvement, clearly offers better performance stability compared to the existing techniques. Further research on this study will be directed toward enhancing the recommendation performance that failed to demonstrate significant improvement over the existing techniques. The future research will consider the introduction of a high-dimensional parameter-free clustering algorithm or deep learning-based model in order to improve performance in recommendations.