• The Journal of Art Theory & Practice
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• no.3
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• pp.85-106
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• 2005

As biennale exhibitions has been expanded into all of the world since 1990s, these trends of blockbuster exhibitions have caused several problems. For instance, some major curators monopolized most global size exhibitions despite of a variety of cultural and historical backgrounds. Besides, due to a strong connection between these curators and their own artists, the young emerging artists' opportunities tend to be reduced as a result of the power game. In addition, major curators' power have influence on the exhibition style as well as on the theme itself. Some artists who did not involved that kind of huge scale exhibitions dispute that the direction of the exhibition is concentrating on the curator's interest instead of artists or viewers. Although these dissatisfactions could not portray correctly the process of organizing and managing system of a biennale exhibition, those biennale exhibitions held in recent have shown tautologic discourses without any passion and positive attitude direct to the exploitation of our society as a vanguard. In the process of comparing several kinds of biennale exhibitions, I could find that some artists who participated several biennale exhibitions at the same time did not present their creative vision, although the triumph of an exhibition was typically measured by the amount of visitors. Thus, the aim of this article is to prove that the biennale can show us new cultural discourse as well as progressive method of understanding our times. Is biennale producing the real 'global standard'? If biennale has done it, could this global standard present upto-date paradigm for the unique exhibition system? Is biennale providing an useful opportunity for the understanding and communicating of contemporary art through the recontextualization which is pronounced by the publicity of curator and organizing committee? How can we find the distinctive strategy from each biennale exhibition including Venice Biennale? Biennale, as a blockbuster exhibition, always requires a degree of hype, otherwise it would not be a special event and would not attract a big enough audience. It is the actual reason why major biennale exhibitions seem to be similar artistic events. Unfortunately, it seems that the excess of biennale exhibitions might bring about the lack of contents. In this case, the biennale syndrome would being a kind of the center of poverty, in spite of the visual splendor. After all, following the global standard may not be a matter of great importance now. What really matters is how each biennale exhibition which started under the different conditions can search their own identity.

• Journal of Korean Philosophical Society
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• v.116
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• pp.351-370
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• 2010

Whitehead's metaphysics and philosophy of nature rest on his awareness of the metaphysical problem of the relation between "I", the "creative advance" and forces of the universe. Whitehead attempted to systematize human experience of the world based on logical explorations. In this context it is important to note that his discussion of proposition and language is closely connected with his works on a systematic theory of nature. Whitehead proposes a metaphysical concept of proposition also based on the creative advance of the universe. His theory of proposition is designed to show that human language is constructions of the relations between logical and real events in the creative universe which refer to a particular point of view. In this view, a variety of verbal possibilities in the multiple worlds can be realized in the same proposition. For Whitehead the emergence of novelty in the function of proposition is an essential property of the creative universe. This paper will try to give a discussion to Whitehead's theory of proposition with regard to the emergence of nature. Especially I emphasize in this paper Whitehead's idea and meaning of proposition for the development of the creative advance of nature as one aspect of particular importance.

• Informatization Policy
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• v.26 no.3
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• pp.36-52
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• 2019

The use of social media in government has expanded steadily around the world on the basis of Web 2.0 technology. The government uses social media as a tool for enhancing transparency, participation, collaboration, and saving costs. However, the use of social media in the public sector has not only been positive. It has also been described as a double-edged sword. Most local governments in South Korea use social media for a variety of reasons but there has not been enough practical study of the effectiveness of social media use in the public sector. Local governments generally have positive views of their social media use but the real application of social media is not consistent in each local government. This study tried to determine the reality of social media use in local government and what factors influenced its use. The research analyzed the data from a survey conducted by the Korea Local Information Research & Development Institute (KLID) in 2015 and data from Facebook in each local government. The results show that most local governments were using Facebook for promotional purposes and local government officials similarly recognized that they were using Facebook well. However, local governments showed great differences in their use and practical effect. Meanwhile, the study found that population, financial independence, level of government, the entity operating social media, the median age, and whether social media are used for interaction or to gather opinions were the most influential factors that make a difference in utilization in local government.

• Management & Information Systems Review
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• v.38 no.3
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• pp.187-210
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• 2019

Companies shall hold social responsibility as a member of the social community. Corporate social responsibility uses corporate resources, yet it plays important roles in reducing social imbalance. Their responsibilities are highly associated with the corporate sustainability. Many earlier studies on the association between corporate social responsibility and corporate sustainability have been attempted. Yet it should be mentioned that they do not show a variety of realities as linearity between dependent variables and independent variables were assumed. Thus, this study aims to analyze Markov blanket, a node of minimum descriptive variables that relieve a rigid assumption among variables and affect corporate sustainability by using Bayesian network. Sensitivity analysis was used to elicit how other variables affect by reflecting the complex reality when real factors are changed. As an important result of this study, the firm's future earnings sustainability is naturally related to operating earnings, and as the corporate governance structure is sound, the firm is able to steadily fulfill its social responsibility. However, the fact that the size of a company is large does not mean that it is in good compliance with corporate laws. This would not be unrelated to the fact that many of today's companies are not complying with the law and are suffering social condemnation. Results from this study will serve as a useful analytic tool when investors and creditors showing interests in corporate sustainability for assessing the value of companies and making investment decisions. Moreover, they can be used as references for relevant agency supervising capital markets to establish or improve appropriate institutions aimed at improving corporate sustainability.

• Membrane Journal
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• v.34 no.2
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• pp.87-95
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• 2024

Recent studies explore a wide array of desalination and water treatment methods, encompassing membrane processes such as reverse osmosis (RO), nanofiltration (NF), and electrodialysis (ED) to advanced capacitive deionization (CDI) and its membrane variant (MCDI). Comparative analyses reveal ED's cost-effectiveness in low-salinity scenarios, while hybrid systems (NF-MCDI, RO-NF-MCDI) show improved salt removal and energy efficiency. Novel ion separation methods (NF-CDI, NF-FCDI) offer enhanced efficacy and energy savings. These studies also highlight the efficiency of these methods in treating complex wastewater specific to various industries. Environmental impact assessments emphasize the need for sustainability in system selection. Additionally, the integration of microfabricated sensors into membranes allows real-time monitoring, advancing technology development. These studies underscore the variety and promise of emerging desalination and water treatment technologies. They provide valuable insights for enhancing efficiency, minimizing energy usage, tackling industry-specific issues, and innovating to surpass conventional method limitations. The future of sustainable water treatment appears bright, with continual advancements focused on improving efficiency, minimizing environmental impact, and ensuring adaptability across diverse applications.

• The Journal of Art Theory & Practice
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• no.8
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• pp.59-87
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• 2009

This thesis started from the attempt to make it clear that 80's American artist Keith Haring(1958-1990) had conducted social intervention of criticism, resistance, and participation through his works, and so pursued public value. Haring of graffiti fame left popular and familiar cartoon style pictures on the street wall, the billboards, the posters and so on. Popular and playful works was explained as his unique characteristics, but Haring's creative way at the field has more value than just being grasped as artist's personal characteristics. Haring's work pieces became everyday art by joining with people's life, and are working as a social speaking place. So I think that these Haring's art works possess characteristics of 'the public sphere'. 'The Public Sphere' means that is independent and free from the government or partisan economic forces, so that is not connected with the interested relations, and that is the sphere of rational argumentation without 'disguise' or 'fabrication', and that is the sphere where general public can participate in and is inspected by them. The public sphere between the sphere of public authority such a nation and a market and the private sphere of free individual, it is mutually connected with them and works as the space forming public opinion. Private individuals communicate with this public sphere and perform a role of direct and indirect check, balance, and social criticism way off from power. Openness that should include the voice of not only leading power but also the socially weak such as citizens, women, homosexuals, minority races, and so on, and alienated class, is an index of the public characteristics. The public sphere is not working just with speech and mass media. Many artists as well as Haring open their mouth and act through an art at the center of society, and create another public sphere by an art. I understood that the real participatory and practical characteristics on the Haring's work is a phenomenon and current of a part of the art world including Haring. Such current started from 1960s is the in-depth effort to be connected with the life more closely, to communicate with people, and to improve problems of life. And it has pursued public value on the different way from the nation or public power. Artists have intervened in the society with strategic and positive ways in order to raise pushed-out value and sinked rights as the public agenda, and labored to accept the value of variety and difference at the society. The aspect of such social intervention is the notable features, findable on the Haring's works and process. Haring's works include art historical meanings and are expressed with familiar and plastic language, so they were able to communicate with various classes. And he secured various customers at the field and the street. This communicative and public approach factor raised the possibility much for his works to work as the public sphere. Haring presented critical and resistant speech toward society with his works based on this factor. He asserted his position and justice of gender identity as a sexual minority. And his such work continued to movement for alienated class and social week over his own rights. His speech and message on the wall painting, poster, T-shirts, billboard of the subway, and so on worked as a spectacle and pressed concern with social issues and consciousness shift. And he's been trying to protect and care people who is injured by HIV and drug and to realize social justice through social week protection. Haring's works planned to meet many people as much as possible performed its role of intervening in society through criticism, resistance, speech, and participation, and controlling and checking social issues. These things considered, Haring's works show his consciousness about public attributes of art, and obviously include public value seeking. And also we can find the meaning of such his work as that an art is working as the public sphere and shows the possibility to discuss and practice public issues.

• Journal of Internet Computing and Services
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• v.16 no.2
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• pp.77-83
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• 2015

Frequent pattern mining, which is one of the major areas actively studied in data mining, is a method for extracting useful pattern information hidden from large data sets or databases. Moreover, frequent pattern mining approaches have been actively employed in a variety of application fields because the results obtained from them can allow us to analyze various, important characteristics within databases more easily and automatically. However, traditional frequent pattern mining methods, which simply extract all of the possible frequent patterns such that each of their support values is not smaller than a user-given minimum support threshold, have the following problems. First, traditional approaches have to generate a numerous number of patterns according to the features of a given database and the degree of threshold settings, and the number can also increase in geometrical progression. In addition, such works also cause waste of runtime and memory resources. Furthermore, the pattern results excessively generated from the methods also lead to troubles of pattern analysis for the mining results. In order to solve such issues of previous traditional frequent pattern mining approaches, the concept of representative pattern mining and its various related works have been proposed. In contrast to the traditional ones that find all the possible frequent patterns from databases, representative pattern mining approaches selectively extract a smaller number of patterns that represent general frequent patterns. In this paper, we describe details and characteristics of pattern condensing techniques that consider the maximality or closure property of generated frequent patterns, and conduct comparison and analysis for the techniques. Given a frequent pattern, satisfying the maximality for the pattern signifies that all of the possible super sets of the pattern must have smaller support values than a user-specific minimum support threshold; meanwhile, satisfying the closure property for the pattern means that there is no superset of which the support is equal to that of the pattern with respect to all the possible super sets. By mining maximal frequent patterns or closed frequent ones, we can achieve effective pattern compression and also perform mining operations with much smaller time and space resources. In addition, compressed patterns can be converted into the original frequent pattern forms again if necessary; especially, the closed frequent pattern notation has the ability to convert representative patterns into the original ones again without any information loss. That is, we can obtain a complete set of original frequent patterns from closed frequent ones. Although the maximal frequent pattern notation does not guarantee a complete recovery rate in the process of pattern conversion, it has an advantage that can extract a smaller number of representative patterns more quickly compared to the closed frequent pattern notation. In this paper, we show the performance results and characteristics of the aforementioned techniques in terms of pattern generation, runtime, and memory usage by conducting performance evaluation with respect to various real data sets collected from the real world. For more exact comparison, we also employ the algorithms implementing these techniques on the same platform and Implementation level.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.147-168
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• 2017

There have been many studies on accurate stock market forecasting in academia for a long time, and now there are also various forecasting models using various techniques. Recently, many attempts have been made to predict the stock index using various machine learning methods including Deep Learning. Although the fundamental analysis and the technical analysis method are used for the analysis of the traditional stock investment transaction, the technical analysis method is more useful for the application of the short-term transaction prediction or statistical and mathematical techniques. Most of the studies that have been conducted using these technical indicators have studied the model of predicting stock prices by binary classification - rising or falling - of stock market fluctuations in the future market (usually next trading day). However, it is also true that this binary classification has many unfavorable aspects in predicting trends, identifying trading signals, or signaling portfolio rebalancing. In this study, we try to predict the stock index by expanding the stock index trend (upward trend, boxed, downward trend) to the multiple classification system in the existing binary index method. In order to solve this multi-classification problem, a technique such as Multinomial Logistic Regression Analysis (MLOGIT), Multiple Discriminant Analysis (MDA) or Artificial Neural Networks (ANN) we propose an optimization model using Genetic Algorithm as a wrapper for improving the performance of this model using Multi-classification Support Vector Machines (MSVM), which has proved to be superior in prediction performance. In particular, the proposed model named GA-MSVM is designed to maximize model performance by optimizing not only the kernel function parameters of MSVM, but also the optimal selection of input variables (feature selection) as well as instance selection. In order to verify the performance of the proposed model, we applied the proposed method to the real data. The results show that the proposed method is more effective than the conventional multivariate SVM, which has been known to show the best prediction performance up to now, as well as existing artificial intelligence / data mining techniques such as MDA, MLOGIT, CBR, and it is confirmed that the prediction performance is better than this. Especially, it has been confirmed that the 'instance selection' plays a very important role in predicting the stock index trend, and it is confirmed that the improvement effect of the model is more important than other factors. To verify the usefulness of GA-MSVM, we applied it to Korea's real KOSPI200 stock index trend forecast. Our research is primarily aimed at predicting trend segments to capture signal acquisition or short-term trend transition points. The experimental data set includes technical indicators such as the price and volatility index (2004 ~ 2017) and macroeconomic data (interest rate, exchange rate, S&P 500, etc.) of KOSPI200 stock index in Korea. Using a variety of statistical methods including one-way ANOVA and stepwise MDA, 15 indicators were selected as candidate independent variables. The dependent variable, trend classification, was classified into three states: 1 (upward trend), 0 (boxed), and -1 (downward trend). 70% of the total data for each class was used for training and the remaining 30% was used for verifying. To verify the performance of the proposed model, several comparative model experiments such as MDA, MLOGIT, CBR, ANN and MSVM were conducted. MSVM has adopted the One-Against-One (OAO) approach, which is known as the most accurate approach among the various MSVM approaches. Although there are some limitations, the final experimental results demonstrate that the proposed model, GA-MSVM, performs at a significantly higher level than all comparative models.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.93-110
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• 2015

The recommender system is a system which recommends products to the customers who are likely to be interested in. Based on automated information filtering technology, various recommender systems have been developed. Collaborative filtering (CF), one of the most successful recommendation algorithms, has been applied in a number of different domains such as recommending Web pages, books, movies, music and products. But, it has been known that CF has a critical shortcoming. CF finds neighbors whose preferences are like those of the target customer and recommends products those customers have most liked. Thus, CF works properly only when there's a sufficient number of ratings on common product from customers. When there's a shortage of customer ratings, CF makes the formation of a neighborhood inaccurate, thereby resulting in poor recommendations. To improve the performance of CF based recommender systems, most of the related studies have been focused on the development of novel algorithms under the assumption of using a single profile, which is created from user's rating information for items, purchase transactions, or Web access logs. With the advent of big data, companies got to collect more data and to use a variety of information with big size. So, many companies recognize it very importantly to utilize big data because it makes companies to improve their competitiveness and to create new value. In particular, on the rise is the issue of utilizing personal big data in the recommender system. It is why personal big data facilitate more accurate identification of the preferences or behaviors of users. The proposed recommendation methodology is as follows: First, multimodal user profiles are created from personal big data in order to grasp the preferences and behavior of users from various viewpoints. We derive five user profiles based on the personal information such as rating, site preference, demographic, Internet usage, and topic in text. Next, the similarity between users is calculated based on the profiles and then neighbors of users are found from the results. One of three ensemble approaches is applied to calculate the similarity. Each ensemble approach uses the similarity of combined profile, the average similarity of each profile, and the weighted average similarity of each profile, respectively. Finally, the products that people among the neighborhood prefer most to are recommended to the target users. For the experiments, we used the demographic data and a very large volume of Web log transaction for 5,000 panel users of a company that is specialized to analyzing ranks of Web sites. R and SAS E-miner was used to implement the proposed recommender system and to conduct the topic analysis using the keyword search, respectively. To evaluate the recommendation performance, we used 60% of data for training and 40% of data for test. The 5-fold cross validation was also conducted to enhance the reliability of our experiments. A widely used combination metric called F1 metric that gives equal weight to both recall and precision was employed for our evaluation. As the results of evaluation, the proposed methodology achieved the significant improvement over the single profile based CF algorithm. In particular, the ensemble approach using weighted average similarity shows the highest performance. That is, the rate of improvement in F1 is 16.9 percent for the ensemble approach using weighted average similarity and 8.1 percent for the ensemble approach using average similarity of each profile. From these results, we conclude that the multimodal profile ensemble approach is a viable solution to the problems encountered when there's a shortage of customer ratings. This study has significance in suggesting what kind of information could we use to create profile in the environment of big data and how could we combine and utilize them effectively. However, our methodology should be further studied to consider for its real-world application. We need to compare the differences in recommendation accuracy by applying the proposed method to different recommendation algorithms and then to identify which combination of them would show the best performance.

• Journal of Global Scholars of Marketing Science
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• v.20 no.2
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• pp.149-163
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• 2010

Previous studies have shown that retail price promotion change consumers' purchase behavior and that retailers use price promotion more frequently. Keeping constant the benefits received by consumers, there are several ways for retailers to communicate a price promotion. For example, retailers can present a price reduction in absolute terms ($, ${\euro}$), percentage terms (%), or some combinations of these two methods (Della Bitta et al. 1981). Communicating a price promotion in different ways is similar to the framing of purchase decisions (Monroe 1990). Framing effects refers to the finding that subjects respond differently to different descriptions of the same decision question (Frisch 1993). Thus, the presentation of the promotion has an impact on consumer deal evaluation and hence retail sales. In fact, much research in marketing attests to the effects of price presentation on deal perception (Lichtenstein and Bearden 1989; Urbany et al. 1988; Yadav and Monroe 1993). In this sense, a number of marketing researches have argued that deal perceptions are also determined by the degree to which consumers are able to calculate the discounts and final purchase prices accurately (Estelami 2003a; Morwitz et al. 1998), which suggests that marketers may be able to enhance responses to discounts by improving calculation accuracy. Consequently, since calculation inaccuracies in the aggregate lead to the underestimation of discounts (Kim and Kramer 2006), consumers are more likely to appreciate a discounted offer following deeper processing of price information that enables them to evaluate a price discount more accurately. The purpose of this research is to examine the effect of different presentations of discount prices on consumer price perceptions. To be more precise, the purpose of this study is to investigate how different implementations of the same price promotion (semantic and visual salience) affect consumers' perceptions of the promotion and their purchase decisions. Specifically, the analysis will focus on the effect of price presentation on evaluation, purchase intentions and perception of savings. In order to verify the hypotheses proposed in the research, this paper will present an experimental analysis dealing with several discount presentations. In this sense, a2 (Numerical salience presentation: absolute and relative) x2 (Worded salience presentation: novel and traditional) x2 (Visual salience: red and blue) design was employed to investigate the effects of discount presentation on three dependent variables: evaluation, purchase intentions and perception of savings. Respondents were exposed to a hypothetical advertisement that they had to evaluate and were informed of the offer conditions. Once the sample finished evaluating the advertisement, they answered a questionnaire related to price salience and dependent dimensions. Then, manipulation checks were conducted to ensure that respondents remembered their treatment conditions. Next, a $2{\times}2{\times}2$ MANOVA and follow-up univariate tests were conducted to verify the research hypotheses suggested and to examine the effects of the individual factors (price salience) on evaluation, purchase intentions and perceived savings. The results of this research show that semantic and visual salience presentations have significant main effects and interactions on evaluation, purchase intentions and perception of savings. Significant numerical salience interactions affected evaluation and purchase intentions. Additionally, a significant worded salience main effect on perception of savings and interactions on evaluation and purchase intentions were found. Finally, visual salience interactions have significant effects on evaluation. The main findings of this research suggest practical implications that firms should consider when planning promotion-based discounts to attract consumer attention. Consequently, because price presentation has important effects on consumer perception, retailers should consider which effect is wanted in order to design an effective discount presentaion. Specifically, retailers should present discounts with a traditional style that facilitates final price calculation. It is thus important to investigate ways in which marketers can enhance the accuracy of consumers' mental arithmetic to improve responses to price discounts. This preliminary study on the effect of price presentation on consumer perception and purchase intentions opens the line of research for further research. The results obtained in this research may have been determined by a number of limiting conceptual and methodological factors. In this sense, the research deals with a variety of discount presentations as well as with their effects; however, the analysis could include additional salience dimensions and effects on consumers. Furthermore, a similar study could be carried out including a larger, more inclusive and heterogeneous sample of consumers. In addition, the experiment did not require sample individuals to actually buy the product, so it is advisable to compare the effects obtained in the research with real consumer behavior and perception.