• Journal of Korean Philosophical Society
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• v.131
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• pp.199-222
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• 2014

This essay aims at clarifying Dasan Jeong Yagyong's view on Jingfang's yaobian theory. As is well known, Jingfang was a scholar of the Earlier Han period who exerted a profound influence on the theoretical development of Xiangshu School by creating the various techniques for interpreting the Zhouyi. Jingfang is also important in relation with Dasan's commentary of the Zhouyi, because some part of Dasan's interpreting techniques are thought to have their origin in Jinfang. For example, Dasan used the bigua theory of which the origin could be traced back into Jingfang. However, in this article, I did not deal with the bigua theory because I am going to write another article about it. In stead, my focus will be put on analyzing how Dasan evaluated Jingfang's yaobian theory. The main issues of my argument can be summarized as following. Firstly, in terms of yaobian, Jeong Yagyong called attention to Jingfang's annotation on the Zhouyi in which Jingfang utilized the yaobian method in three occasions, i.e., the first nine of the Qian(乾初九), the sixth nine of the Guan(觀上九), and the sixth nine of the Bo(剝上九). It seems that Jeong Yagyong set forth enough evidence about Jingfang's use of yaobian at least in relation to two cases of the first nine of the Qian and the sixth nine of the Bo, while the other evidence of the sixth nine of the Guan was not so persuasive. However, even if it is evident that Jingfang made use of the yaobain in two cases, there is no reason to equate it with that of Dasan. If one takes a close look, it becomes clear that Jingfang's way of yaobain is very different from Dasan's. Secondly, Jeong Yagyong mentioned Jiayi(賈誼) who lived about one hundred years before Jingfang, as the person who utilized the yaobian in his book of Xinshu(新書). If it is certain that Jiayi was aware of the yaobian, we can assume that the yaobian technique had been handed down from Jiayi to Jingfang. The manuscript excavated from the Mawangdui tomb also increases the possibility that Jiayi could have had the knowledge on the yaobian. In the chapter of Muhe(繆和) of the Mawangdui Zhouyi, there appears the phrase, i.e., "the first six of the qian, qian goes to mingyi," which shows exactly the same form of the yaobian in the Mr Zuo's Spring and Autumn Annals(春秋左氏傳). The burial period of Mawangdui tomb is estimated at the same year of B.C.168 in which Jiayi died. Therefore, judging from that fact, it becomes evident that the yaobain method was widely diffused around B.C.168. Subsequently, it is possible to infer that the yaobian method had been handed down from the period of Jiayi to Jingfang. If we could present the persuasive evidence to support that inference, it would also have the effect of consolidating Dasan's argument about the yaobian.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.1-28
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• 2019

This study analyzed the density and mortality rate of Korean fir at 9 sites where individuals of Korean firs were marked into the live and dead trees with coordinates on orthorectified aerial images by digital photogrammetric system. As a result of the analysis, Korean fir in each site showed considerable heterogeneity in density and mortality rate depending on the location within site. This make it possible to assume that death of Korean fir can occur by specific factors that vary depending on the location. Based on the analyzed densities and mortality rates of Korea fir, we investigated the correlation between topographic factors such as altitude, terrain slope, drainage network, solar radiation, aspect and the death of Korean fir. The density of Korean fir increases with altitude, and the mortality rate also increases. A negative correlation is found between the terrain slope and the mortality rate, and the mortality rate is higher in the gentle slope where the drainage network is less developed. In addition, it is recognized that depending on the aspect, the mortality rate varies greatly, and the mean solar radiation is higher in live Korean fir-dominant area than in dead Korean fir-dominant area. Overall, the mortality rate of Korean fir in Mt. Halla area is relatively higher in areas with relatively low terrain slope and low solar radiation. Considering the results of previous studies that the terrain slope has a strong negative correlation with soil moisture and the relationship between solar radiation and evaporation, these results lead us to infer that excess soil moisture is the cause of Korean fir mortality. These inferences are supported by a series of climate change phenomena such as precipitation increase, evaporation decrease, and reduced sunshine duration in the Korean peninsula including Jeju Island, increase in mortality rate along with increased precipitation according to the elevation of Mt. Halla and the vegetation change in the mountain. It is expected that the spatial patterns in the density and mortality rate of Korean fir, which are controlled by topography such as altitude, slope, aspect, solar radiation, drainage network, can be used as spatial variables in future numerical modeling studies on the death or decline of Korean fir. In addition, the method of forest distribution survey using the orthorectified aerial images can be widely used as a numerical monitoring technique in long - term vegetation change research.

• Korean Journal of Culture and Social Issue
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• v.26 no.1
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• pp.25-45
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• 2020

Intentionality judgement in criminal cases is a core area of fact finding that is root of guilty and sentencing judgment on the defendant. However, the third party is not sure the intentionality because it reflects subjective aspect of agent. Thus, mechanism behind intentionality judgment is an important factor to be properly understood by the academia and the criminal justice system. However, previous studies regarding intentionality judgment models have shown inconsistent results. Mental-state models proposed foreseeability(belief) and desire of agent at the time of the offence as key factors in intentionality judgment. These factors consistent with central things on intentionality judgment in criminal law. However, key factors in moral-evaluation models are blameworthiness of agent and badness of outcome reflected on the consequent aspect of act. Recently, deep-self concordance model emerged suggesting important factors on intentionality judgment are not mental states and moral evaluations but individual's deep-self. However, these models are limited in that they do not consider the important features of criminal cases, that the consequence of the case is inevitably negative, and therefore the actor who is a party to legal punishment rarely expresses his or her mental state at the time of the act. Therefore, this study suggests that, based on the existing intentionality judgment studies and the characteristics of the criminal case, the inference about who the agent was originally will play a key role in judging the intentionality in the criminal case. This is the moral-character model. Futhermore, In this regard, this study discussed what the media and criminal justice institutions should keep in mind and the directions for future research.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.127-146
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• 2022

Recently, as word embedding has shown excellent performance in various tasks of deep learning-based natural language processing, researches on the advancement and application of word, sentence, and document embedding are being actively conducted. Among them, cross-language transfer, which enables semantic exchange between different languages, is growing simultaneously with the development of embedding models. Academia's interests in vector alignment are growing with the expectation that it can be applied to various embedding-based analysis. In particular, vector alignment is expected to be applied to mapping between specialized domains and generalized domains. In other words, it is expected that it will be possible to map the vocabulary of specialized fields such as R&D, medicine, and law into the space of the pre-trained language model learned with huge volume of general-purpose documents, or provide a clue for mapping vocabulary between mutually different specialized fields. However, since linear-based vector alignment which has been mainly studied in academia basically assumes statistical linearity, it tends to simplify the vector space. This essentially assumes that different types of vector spaces are geometrically similar, which yields a limitation that it causes inevitable distortion in the alignment process. To overcome this limitation, we propose a deep learning-based vector alignment methodology that effectively learns the nonlinearity of data. The proposed methodology consists of sequential learning of a skip-connected autoencoder and a regression model to align the specialized word embedding expressed in each space to the general embedding space. Finally, through the inference of the two trained models, the specialized vocabulary can be aligned in the general space. To verify the performance of the proposed methodology, an experiment was performed on a total of 77,578 documents in the field of 'health care' among national R&D tasks performed from 2011 to 2020. As a result, it was confirmed that the proposed methodology showed superior performance in terms of cosine similarity compared to the existing linear vector alignment.

• Journal of Korean Elementary Science Education
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• v.41 no.3
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• pp.521-537
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• 2022

State curiosity and state anxiety in the science learning have a great influence on academic performance and achievement. Since the levels of state curiosity and anxiety can change at any moment, it is essential to identify the levels of student's state curiosity and state anxiety throughout the course of science learning. Accordingly, we assessed the changes in state curiosity and anxiety levels sensed by 5th- and 6th-grade elementary school students depending on their exposure to the violation of expectation. To this end, we classified science learning into three situations: confronting a scientific task, checking the result, and learning science concepts. As a result, there was no significant difference in state curiosity level of the nVOE group who confronting the result consistent with their expectations in checking the result after confronting a scientific task, but the state curiosity level of the VOE group who facing violation of their expectation increased. In the VOE groups, there was no significant change in the state curiosity level of the VOE-R group who correctly inferred the reason for the result, but that of the VOE-FR group who could not correctly inferred increased. The state anxiety levels of the VOE and nVOE groups decreased after checking the result of the task. The state anxiety level also declined in the VOE-R group. In contrast, there was no significant change in state anxiety level of the VOE-FR group. In learning science concepts of the result after checking the result, the state curiosity of the VOE, nVOE, and VOE-FR group all faded. No significant change was observed in the state anxiety level of the nVOE group, whereas the VOE, VOE-R, and VOE-FR group presented a decreased state anxiety. This study discusses the educational implication of these findings and its outcomes are expected to broaden the understanding of emotional states of students in science learning.

• Journal of Intelligence and Information Systems
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• v.29 no.1
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• pp.249-263
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• 2023

Collaborative Filtering, a representative recommendation system methodology, consists of two approaches: neighbor methods and latent factor models. Among these, the latent factor model using matrix factorization decomposes the user-item interaction matrix into two lower-dimensional rectangular matrices, predicting the item's rating through the product of these matrices. Due to the factor vectors inferred from rating patterns capturing user and item characteristics, this method is superior in scalability, accuracy, and flexibility compared to neighbor-based methods. However, it has a fundamental drawback: the need to reflect the diversity of preferences of different individuals for items with no ratings. This limitation leads to repetitive and inaccurate recommendations. The Adaptive Deep Latent Factor Model (ADLFM) was developed to address this issue. This model adaptively learns the preferences for each item by using the item description, which provides a detailed summary and explanation of the item. ADLFM takes in item description as input, calculates latent vectors of the user and item, and presents a method that can reflect personal diversity using an attention score. However, due to the requirement of a dataset that includes item descriptions, the domain that can apply ADLFM is limited, resulting in generalization limitations. This study proposes a Generalized Adaptive Deep Latent Factor Recommendation Model, G-ADLFRM, to improve the limitations of ADLFM. Firstly, we use item ID, commonly used in recommendation systems, as input instead of the item description. Additionally, we apply improved deep learning model structures such as Self-Attention, Multi-head Attention, and Multi-Conv1D. We conducted experiments on various datasets with input and model structure changes. The results showed that when only the input was changed, MAE increased slightly compared to ADLFM due to accompanying information loss, resulting in decreased recommendation performance. However, the average learning speed per epoch significantly improved as the amount of information to be processed decreased. When both the input and the model structure were changed, the best-performing Multi-Conv1d structure showed similar performance to ADLFM, sufficiently counteracting the information loss caused by the input change. We conclude that G-ADLFRM is a new, lightweight, and generalizable model that maintains the performance of the existing ADLFM while enabling fast learning and inference.

• Asia Marketing Journal
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• v.13 no.4
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• pp.159-189
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• 2012

What attracts consumers to bloggers' reviews? Consumers would be attracted both by the Bloggers' expertise (i.e., knowledge and experience) and by his/her unbiased manner of delivering information. Expertise and trustworthiness are both virtues of information sources, particularly when there is uncertainty in decision-making. Noting this point, we postulate that consumers' motives determine the relative weights they place on expertise and trustworthiness. In addition, our hypotheses assume that tie strength moderates consumers' expectation on bloggers' expertise and trustworthiness: with expectation on expertise enhanced for power-blog user-group (weak-ties), and an expectation on trustworthiness elevated for personal-blog user-group (strong-ties). Finally, we theorize that the effect of credibility on willingness to accept a review is moderated by tie strength; the predictive power of credibility is more prominent for the personal-blog user-groups than for the power-blog user groups. To support these assumptions, we conducted a field survey with blog users, collecting retrospective self-report data. The "gourmet shop" was chosen as a target product category, and obtained data analyzed by structural equations modeling. Findings from these data provide empirical support for our theoretical predictions. First, we found that the purposive motive aimed at satisfying instrumental information needs increases reliance on bloggers' expertise, but interpersonal connectivity value for alleviating loneliness elevates reliance on bloggers' trustworthiness. Second, expertise-based credibility is more prominent for power-blog user-groups than for personal-blog user-groups. While strong ties attract consumers with trustworthiness based on close emotional bonds, weak ties gain consumers' attention with new, non-redundant information (Levin & Cross, 2004). Thus, when the existing knowledge system, used in strong ties, does not work as smoothly for addressing an impending problem, the weak-tie source can be utilized as a handy reference. Thus, we can anticipate that power bloggers secure credibility by virtue of their expertise while personal bloggers trade off on their trustworthiness. Our analysis demonstrates that power bloggers appeal more strongly to consumers than do personal bloggers in the area of expertise-based credibility. Finally, the effect of review credibility on willingness to accept a review is higher for the personal-blog user-group than for the power-blog user-group. Actually, the inference that review credibility is a potent predictor of assessing willingness to accept a review is grounded on the analogy that attitude is an effective indicator of purchase intention. However, if memory about established attitudes is blocked, the predictive power of attitude on purchase intention is considerably diminished. Likewise, the effect of credibility on willingness to accept a review can be affected by certain moderators. Inspired by this analogy, we introduced tie strength as a possible moderator and demonstrated that tie strength moderated the effect of credibility on willingness to accept a review. Previously, Levin and Cross (2004) showed that credibility mediates strong-ties through receipt of knowledge, but this credibility mediation is not observed for weak-ties, where a direct path to it is activated. Thus, the predictive power of credibility on behavioral intention - that is, willingness to accept a review - is expected to be higher for strong-ties.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.35-52
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• 2023

Recently, as the non-face-to-face environment has developed due to COVID-19 and environmental pollution, the importance of online digital healthcare is increasing, and venture start-ups and activities such as health care, telemedicine, and digital treatments are also actively underway. This study conducted the impact on the acceptability of digital healthcare smartwatches with an integrated approach of the expanded integrated technology acceptance model (UTAUT2) and the behavioral inference model (BRT). The most advanced integrated technology acceptance model for innovative technology acceptance research was used to identify major factors such as utility expectations, social effects, convenience, price barriers, lack of alternatives, and behavioral intentions. For the study, about 410 responses from ordinary people in their teens to 60s across the country were collected, and based on this, the hypothesis was verified using structural equations after testing reliability and validity of the data. SPSS 23 and AMOS 23 were used for research analysis. Studies have shown that personal innovation has a significant impact on the reasons for acceptance (use value, social impact, convenience of use), attitude, and non-use (price barriers, lack of alternatives, and barriers to use). These results are the same as the results of previous studies that confirmed the influence of the main value of innovative ICT on user acceptance intention. In addition, the reason for acceptance had a significant effect on attitude, but the effect of the reason for non-acceptance was not significant. It can be analyzed that consumers are interested in new ICT products and new services, but purchase them more carefully and selectively. This study has evolved from the acceptance analysis of general-purpose consumer innovation technology to the acceptance analysis of consumer value in smartwatch digital healthcare, which is a new and important area in the future. Industrially, it can contribute to the product's purchase and marketing. It is hoped that this study will contribute to increasing research in the digital healthcare sector, which will play an important role in our lives in the future, and that it will develop into in-depth factors that are more suitable for consumer value through integrated approach models and integrated analysis of consumer acceptance and non-acceptance.

• Health Policy and Management
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• v.34 no.3
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• pp.293-308
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• 2024

Background: Assessing the change in mental health status of quarantined patients in community treatment centers at the time of admission and discharge, and inferring the influencing factors. Methods: The study was conducted on a sample of 1,941 quarantined patients from three community treatment centers. Changes in anxiety, psychological distress, post-traumatic stress, depression, and self-harm ideation between admission and discharge were categorized as either "improved" or "worsened." Inference was performed to determine the probability of worsening in mental health status. Results: The mental health status of quarantined patients, such as anxiety and depression, was relatively higher than that of the general population. Anxiety (84.3% improved) and psychological distress (79.0% improved) were reduced during quarantine treatment. However, some patients continued to experience moderate to severe levels of anxiety (11.2%) and psychological distress (11.0%) at discharge. As for depression, the depression of moderate or higher level was increased at the time of discharge (28.7%→36.7%) compared to admission. The deterioration of anxiety and psychological distress was found to be the most significant factor influencing the worsening of depression at discharge (odds ratio [OR] for anxiety deterioration, 2.04; OR for psychological distress deterioration, 3.56). These effects were also observed similarly in post-traumatic stress and self-injury ideation. Conclusion: Improving anxiety and psychological distress among quarantined patients in community treatment centers can reduce the worsening of post-traumatic stress, depression, and self-injury ideation at the time of discharge. These findings provide evidence for the need for active mental health management from the initial stages of quarantine treatment.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.