• Journal of Science and Technology Studies
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• v.19 no.2
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• pp.169-206
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• 2019

This article seeks to explore new relationships and ethics of human and technology by analyzing a cultural imaginary produced by artificial intelligence. Drawing on theoretical reflections of the Feminist Scientific and Technological Studies which understand science and technology as the matter of care(Puig de la Bellacas, 2011), this paper focuses on the fact that artificial intelligence and robots materialize cultural imaginary such as autonomy. This autonomy, defined as the capacity to adapt to a new environment through self-learning, is accepted as a way to conceptualize an authentic human or an ideal subject. However, this article argues that artificial intelligence is mediated by and dependent on invisible human labor and complex material devices, suggesting that such autonomy is close to fiction. The recent growth of the so-called 'assistant technology' shows that it is differentially visualizing the care work of both machines and humans. Technology and its cultural imaginary hide the care work of human workers and actively visualize the one of the machine. And they make autonomy and agency ideal humanness, leaving disabled bodies and dependency as unworthy. Artificial intelligence and its cultural imaginary negate the value of disabled bodies while idealizing abled-bodies, and result in eliminating the real relationship between man and technology as mutually dependent beings. In conclusion, the author argues that the technology we need is not the one to exclude the non-typical bodies and care work of others, but the one to include them as they are. This technology responsibly empathizes marginalized beings and encourages solidarity between fragile beings. Inspired by an art performance of artist Sue Austin, the author finally comes up with and suggests 'artificial intelligence in wheelchair' as an alternative figuration for the currently dominant 'autonomous artificial intelligence'.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.1-17
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• 2019

Because stock price forecasting is an important issue both academically and practically, research in stock price prediction has been actively conducted. The stock price forecasting research is classified into using structured data and using unstructured data. With structured data such as historical stock price and financial statements, past studies usually used technical analysis approach and fundamental analysis. In the big data era, the amount of information has rapidly increased, and the artificial intelligence methodology that can find meaning by quantifying string information, which is an unstructured data that takes up a large amount of information, has developed rapidly. With these developments, many attempts with unstructured data are being made to predict stock prices through online news by applying text mining to stock price forecasts. The stock price prediction methodology adopted in many papers is to forecast stock prices with the news of the target companies to be forecasted. However, according to previous research, not only news of a target company affects its stock price, but news of companies that are related to the company can also affect the stock price. However, finding a highly relevant company is not easy because of the market-wide impact and random signs. Thus, existing studies have found highly relevant companies based primarily on pre-determined international industry classification standards. However, according to recent research, global industry classification standard has different homogeneity within the sectors, and it leads to a limitation that forecasting stock prices by taking them all together without considering only relevant companies can adversely affect predictive performance. To overcome the limitation, we first used random matrix theory with text mining for stock prediction. Wherever the dimension of data is large, the classical limit theorems are no longer suitable, because the statistical efficiency will be reduced. Therefore, a simple correlation analysis in the financial market does not mean the true correlation. To solve the issue, we adopt random matrix theory, which is mainly used in econophysics, to remove market-wide effects and random signals and find a true correlation between companies. With the true correlation, we perform cluster analysis to find relevant companies. Also, based on the clustering analysis, we used multiple kernel learning algorithm, which is an ensemble of support vector machine to incorporate the effects of the target firm and its relevant firms simultaneously. Each kernel was assigned to predict stock prices with features of financial news of the target firm and its relevant firms. The results of this study are as follows. The results of this paper are as follows. (1) Following the existing research flow, we confirmed that it is an effective way to forecast stock prices using news from relevant companies. (2) When looking for a relevant company, looking for it in the wrong way can lower AI prediction performance. (3) The proposed approach with random matrix theory shows better performance than previous studies if cluster analysis is performed based on the true correlation by removing market-wide effects and random signals. The contribution of this study is as follows. First, this study shows that random matrix theory, which is used mainly in economic physics, can be combined with artificial intelligence to produce good methodologies. This suggests that it is important not only to develop AI algorithms but also to adopt physics theory. This extends the existing research that presented the methodology by integrating artificial intelligence with complex system theory through transfer entropy. Second, this study stressed that finding the right companies in the stock market is an important issue. This suggests that it is not only important to study artificial intelligence algorithms, but how to theoretically adjust the input values. Third, we confirmed that firms classified as Global Industrial Classification Standard (GICS) might have low relevance and suggested it is necessary to theoretically define the relevance rather than simply finding it in the GICS.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.139-161
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• 2019

The utilization of the e-commerce market has become a common life style in today. It has become important part to know where and how to make reasonable purchases of good quality products for customers. This change in purchase psychology tends to make it difficult for customers to make purchasing decisions in vast amounts of information. In this case, the recommendation system has the effect of reducing the cost of information retrieval and improving the satisfaction by analyzing the purchasing behavior of the customer. Amazon and Netflix are considered to be the well-known examples of sales marketing using the recommendation system. In the case of Amazon, 60% of the recommendation is made by purchasing goods, and 35% of the sales increase was achieved. Netflix, on the other hand, found that 75% of movie recommendations were made using services. This personalization technique is considered to be one of the key strategies for one-to-one marketing that can be useful in online markets where salespeople do not exist. Recommendation techniques that are mainly used in recommendation systems today include collaborative filtering and content-based filtering. Furthermore, hybrid techniques and association rules that use these techniques in combination are also being used in various fields. Of these, collaborative filtering recommendation techniques are the most popular today. Collaborative filtering is a method of recommending products preferred by neighbors who have similar preferences or purchasing behavior, based on the assumption that users who have exhibited similar tendencies in purchasing or evaluating products in the past will have a similar tendency to other products. However, most of the existed systems are recommended only within the same category of products such as books and movies. This is because the recommendation system estimates the purchase satisfaction about new item which have never been bought yet using customer's purchase rating points of a similar commodity based on the transaction data. In addition, there is a problem about the reliability of purchase ratings used in the recommendation system. Reliability of customer purchase ratings is causing serious problems. In particular, 'Compensatory Review' refers to the intentional manipulation of a customer purchase rating by a company intervention. In fact, Amazon has been hard-pressed for these "compassionate reviews" since 2016 and has worked hard to reduce false information and increase credibility. The survey showed that the average rating for products with 'Compensated Review' was higher than those without 'Compensation Review'. And it turns out that 'Compensatory Review' is about 12 times less likely to give the lowest rating, and about 4 times less likely to leave a critical opinion. As such, customer purchase ratings are full of various noises. This problem is directly related to the performance of recommendation systems aimed at maximizing profits by attracting highly satisfied customers in most e-commerce transactions. In this study, we propose the possibility of using new indicators that can objectively substitute existing customer 's purchase ratings by using RFM multi-dimensional analysis technique to solve a series of problems. RFM multi-dimensional analysis technique is the most widely used analytical method in customer relationship management marketing(CRM), and is a data analysis method for selecting customers who are likely to purchase goods. As a result of verifying the actual purchase history data using the relevant index, the accuracy was as high as about 55%. This is a result of recommending a total of 4,386 different types of products that have never been bought before, thus the verification result means relatively high accuracy and utilization value. And this study suggests the possibility of general recommendation system that can be applied to various offline product data. If additional data is acquired in the future, the accuracy of the proposed recommendation system can be improved.

• Journal of The Korean Association For Science Education
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• v.39 no.2
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• pp.161-171
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• 2019

The purposes of this study are: 1) to verify the systems thinking factor structure of elementary school students and 2) to compare systems thinking according to their preferred subjects in order to get implications for following research. For the study, pre-tests analyze data from 732 elementary school students using the STMI (Systems Thinking Measuring Instrument) developed by Lee et al. (2013). And exploratory factor analysis was conducted to identify the factor structure of the students. Based on the results of the pre-test, the expert group council revised the STMI so that elementary school students could respond to the 5-factor structure that STMI intended. In the post-test, 503 data were analyzed by modified STMI and exploratory factor analysis was performed. The results of the study are as follows: First, in the pre-test, elementary school students responded to the STMI with a test paper consisting of two factors (personal internal factors and personal external factors). The total reliability of the instrument was .932 and the reliability of each factor was analyzed as .857 and .894. Second, for modified STMI, elementary school students responded a 4-factor instrument. Team learning, Shared Vision, and Personal Mastery were derived independent factors, and mental model and systems analysis were derived 1-factor. The total reliability of the instrument was .886 and the reliability of each factor was analyzed as .686 to .864. Finally, a comparison of systems thinking according to preferred subjects showed a significant difference between students who selected science (engineering) group and art (music and physical education). In conclusion, it was confirmed that statistically meaningful results could be obtained using STMI modified by term and sentence structure appropriate for elementary school students, and it is a necessary to study the relation of systems thinking with various student variables such as the preferred subjects.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• Journal of Korean Home Economics Education Association
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• v.33 no.3
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• pp.85-105
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• 2021

The purpose of this study is to develop and implement cooperative consumption education programs using design thinking in middle school home economics education classes to understand the impact on students' cooperative problem solving competency. Accordingly, a cooperative consumption education program based on design thinking was developed according to the ADDIE model, and the evaluation was conducted on a total of 25 students. The results of the study were as follows. First, based on prior research, we developed a consumption education program based on D. school's design thinking process under the theme of 'Creating a Shared School' for the practice of cooperative consumption. As a result of expert validity verification of the teaching/learning course plan and workbook for the eight sessions, the average question was 4.72 (out of 5 points) and the average CVI was 0.93, indicating that the content validity and field suitability were excellent. Second, to summarize the results achieved from the implementation of the cooperative consumption education program, the pre-/post-test using the revised and supplemented cooperative problem-solving competency tool, and the open-ended survey, It was confirmed that the developed program had a significant effect on improving not only the students' knowledge and perceived necessity for cooperative consumption along with the awareness of practice, but also the cooperative problem-solving competency. As a follow-up study, we propose to expand the research to a wider audience, and to further conduct research and develop programs applied with design thinking in home economics curriculum and in consumer competency development. This study confirmed that cooperative consumption education programs using design thinking are effective in improving youth's cooperative problem-solving competency and is meaningful in that they developed consumption education programs under the theme of 'cooperative consumption' in response to changing consumer education needs.

• Journal of Korean Home Economics Education Association
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• v.33 no.3
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• pp.107-127
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• 2021

The purpose of this study is to examine the relationship between the content areas and competencies of the Family & Consumer Sciences National Standards(NSFCS 3.0) of the U. S. and UNESCO Global Citizenship Education(GCED). For this purpose, the global perspective, content areas and competencies in NSFCS 3.0 and the keywords related to the three areas of content areas of UNESCO GCED were analyzed. Specifically, the content standards and competencies related to the words 'world' or 'global' were extracted and their relationship to the GCED topics and keywords were analyzed. The results of the study are as follows. First, NSFCS 3.0 described the direct correlation between individuals and the world by recognizing individuals as global citizens in 14 areas except for 'interpersonal relations' and 'parenting', specifically using the keyword of 'world' in content standards and competencies. Second, in the content standards and competencies of NSFCS 3.0, the keywords related to the topics of GCED areas were presented evenly in the three areas of FCS, dietary habits, family life, and human development. The social and emotional areas were not presented in clothing, housing, and consumer life. On the other hand, the behavioral area, which is emphasized most in the GCED, is presented in all the FCS content areas. From this, it is apparent that the learning field for GCED may be considered as the area of life pursued by the home economics curriculum. The results of this study provide foundational bases for understanding the relationship between NSFCS 3.0 and the GCED, and implications as to how to implement the content of the GCED in the next revision of the national home economics curriculum of Korea.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.66 no.2
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• pp.138-145
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• 2021

Perilla contains more than 60% of fatty acids. Linolenic acid is effective in preventing heart disease, improving learning ability, treating allergies, and preventing cancer. This study was carried out to improve the cultivation method to aid the stable production of perilla by developing a suitable inter-cropping system with sesame in the central region as well as to report a suitable planting time. The test results are summarized as follows. As the planting time of perilla in the inter-cropping system with sesame was delayed, the number of clusters and capsules decreased. The perilla yields in this system showed significant differences compared to that with the previous crops (sesame varieties) and planting period. The yield of perilla was significantly lower in the characteristic-Type B variety than in the characteristic-Type A variety and decreased significantly as the planting time was delayed. With regards to the quality characteristics of perilla, such as crude protein, crude fat, etc., there were no differences between previous perilla crops and those inter-cropped with sesame. The perilla composition did not show any difference during the planting period; however, with delay in the planting time, crude protein content increased but crude fat content decreased. Yield of perilla was 38% higher in a two-row (40 x 40 cm) system, compared to a single-row cultivation (110 x 20 cm) of perilla inter-cropped with sesame. These results suggest that the suitable method for inter-cropping perilla with sesame in the central region is to sow the characteristic-Type A variety in early May, and cultivate the perilla in two lines (40 x 40 cm) in mid-June. This was judged to be the best cultivation method in the central region.

• Journal of the Korean Institute of Landscape Architecture
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• v.47 no.5
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• pp.14-27
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• 2019

This study presents a plan for a memorial park that respects the characteristics based on the historical facts for the concept of space of the Gwangju Hospital of Korea Army and the location of the 505 Security Forces, which were designated as historic sites after the 5-18 Democratization Movement. The Gwangju Metropolitan City as it is the location of the 5-18 historic sites, is taking part in the 5-18 Memorial Project, and plans to establish a city park recognizing the historic site of the 5-18 Democratization Movement, which has been preserved only as a memory space to this point. The park is promoting a phased development plan. This study suggests that the 5-18 historic sites can be modernized and that social consensus can establish the framework of the step-by-step planning and composition process to ensure the plans for the space heals wounds while preserving the history. In this paper, we propose a solution to a problem. We solve the approach for space utilization through an analysis of precedent research and planning cases related to park planning at historical sites. In addition to exploring the value of the site, we also describe the space utilization strategy that covers the historical characteristics and facts while maintaining the concept of park planning. As a result of the research, the historic site of the Gwangju Hospital of Korea Army is planned as a park of historical memory and healing in order to solve the problems left behind by the 5-18 Democratization Movement. The historic site of the 505 Security Forces was selected as an area for historical experiences and a place for learning that can be sympathized with by future generations of children and adolescents in terms of expanding and sustaining the memory of the 5-18 Democratization Movement. In the planning stage, the historical sites suggested the direction of space utilization for representation as did the social consensus of citizens, related groups, and specialists. Through this study, we will contribute to construction of a memorial park containing historical values in from 5-18 historic sites. It is meaningful to suggest a direction that can revitalize the life of the city as well as its citizen and can share with the history with future generations beyond being a place to heal wounds and keep alive the memory of the past.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.117-133
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• 2021

The art of the Confucian literati' was mainly centered on literati' painting. Literary paintings combined with poem-writing-painting are based on the literary characters and studies. This is usually based on the technique of Shensi(神似) caused by a hobby accomplishment, enjoying the chinese painting and cherishing with chinese painting. The Confucian literati' cultivated their own character and sublimated humanism to art based on studying. They sought the life of supremacy and supreme gentleness, and enjoyed life on the boundary of pleasure through art. The aim of the Confucian literati' arts lies in the pursuit of expressing the artist's inner world, spirit, and the combination of the Confucian and the Taoism, Because of literati's spirit based on learning, the Confucian literati' arts still exist. The aesthetic of Sunbi Spirit is mainly in the customs of Sunbi, the loyalty of Sunbi, the Silhak(實學) of Sunbi, and loving of the people of Sunbi. We can find honor and loyalty in the Sunbi spirit of the Confucian literati' of the Joseon Dynasty. In addition, it is also possible to observe the loyal troops, the hard work for the country, and the Pung-ryu with nature. In other words, the Sunbi honor, loyalty, loyal troops, pursuit of study and the Pung-ryu show the spirit of the Confucian literati' of Joseon Dynasty. The aesthetic of the Sunbi spirit is in Pung-ryu, loyalty, Silhak, loving of the people etc. The aesthetic of experience of art is mainly based on the aesthetic experience by emotional intelligence and the aesthetic experience according to the individual's inclination. The aesthetic sense actually shows Pung-ryu, loyalty, Silhak and love etc. We can see it in many of our literary paintings. Therefore, the Confucian literati' painting in Chosun Dynasty were the intentionality of the mind and the intentionality of the spare. Furthermore, it has directivity of expressing the artist's inner world, directivity of substance, so it is possible to see that the characteristics of the Sunbi spirit are diverse.