• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.1-33
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• 2019

The small and medium sized enterprises (hereinafter SMEs) are already at a competitive disadvantaged when compared to large companies with more abundant resources. Manufacturing SMEs not only need a lot of information needed for new product development for sustainable growth and survival, but also seek networking to overcome the limitations of resources, but they are faced with limitations due to their size limitations. In a new era in which connectivity increases the complexity and uncertainty of the business environment, SMEs are increasingly urged to find information and solve networking problems. In order to solve these problems, the government funded research institutes plays an important role and duty to solve the information asymmetry problem of SMEs. The purpose of this study is to identify the differentiating characteristics of SMEs that utilize the public information support infrastructure provided by SMEs to enhance the innovation capacity of SMEs, and how they contribute to corporate performance. We argue that we need an infrastructure for providing information support to SMEs as part of this effort to strengthen of the role of government funded institutions; in this study, we specifically identify the target of such a policy and furthermore empirically demonstrate the effects of such policy-based efforts. Our goal is to help establish the strategies for building the information supporting infrastructure. To achieve this purpose, we first classified the characteristics of SMEs that have been found to utilize the information supporting infrastructure provided by government funded institutions. This allows us to verify whether selection bias appears in the analyzed group, which helps us clarify the interpretative limits of our study results. Next, we performed mediator and moderator effect analysis for multiple variables to analyze the process through which the use of information supporting infrastructure led to an improvement in external networking capabilities and resulted in enhancing product competitiveness. This analysis helps identify the key factors we should focus on when offering indirect support to SMEs through the information supporting infrastructure, which in turn helps us more efficiently manage research related to SME supporting policies implemented by government funded institutions. The results of this study showed the following. First, SMEs that used the information supporting infrastructure were found to have a significant difference in size in comparison to domestic R&D SMEs, but on the other hand, there was no significant difference in the cluster analysis that considered various variables. Based on these findings, we confirmed that SMEs that use the information supporting infrastructure are superior in size, and had a relatively higher distribution of companies that transact to a greater degree with large companies, when compared to the SMEs composing the general group of SMEs. Also, we found that companies that already receive support from the information infrastructure have a high concentration of companies that need collaboration with government funded institution. Secondly, among the SMEs that use the information supporting infrastructure, we found that increasing external networking capabilities contributed to enhancing product competitiveness, and while this was no the effect of direct assistance, we also found that indirect contributions were made by increasing the open marketing capabilities: in other words, this was the result of an indirect-only mediator effect. Also, the number of times the company received additional support in this process through mentoring related to information utilization was found to have a mediated moderator effect on improving external networking capabilities and in turn strengthening product competitiveness. The results of this study provide several insights that will help establish policies. KISTI's information support infrastructure may lead to the conclusion that marketing is already well underway, but it intentionally supports groups that enable to achieve good performance. As a result, the government should provide clear priorities whether to support the companies in the underdevelopment or to aid better performance. Through our research, we have identified how public information infrastructure contributes to product competitiveness. Here, we can draw some policy implications. First, the public information support infrastructure should have the capability to enhance the ability to interact with or to find the expert that provides required information. Second, if the utilization of public information support (online) infrastructure is effective, it is not necessary to continuously provide informational mentoring, which is a parallel offline support. Rather, offline support such as mentoring should be used as an appropriate device for abnormal symptom monitoring. Third, it is required that SMEs should improve their ability to utilize, because the effect of enhancing networking capacity through public information support infrastructure and enhancing product competitiveness through such infrastructure appears in most types of companies rather than in specific SMEs.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Proceedings of the Safety Management and Science Conference
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• 2004.11a
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• pp.355-380
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• 2004

I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.109-131
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• 2014

As the demand of nuclear power plant equipment is continuously growing worldwide, the importance of handling nuclear strategic materials is also increasing. While the number of cases submitted for the exports of nuclear-power commodity and technology is dramatically increasing, preadjudication (or prescreening to be simple) of strategic materials has been done so far by experts of a long-time experience and extensive field knowledge. However, there is severe shortage of experts in this domain, not to mention that it takes a long time to develop an expert. Because human experts must manually evaluate all the documents submitted for export permission, the current practice of nuclear material export is neither time-efficient nor cost-effective. Toward alleviating the problem of relying on costly human experts only, our research proposes a new system designed to help field experts make their decisions more effectively and efficiently. The proposed system is built upon case-based reasoning, which in essence extracts key features from the existing cases, compares the features with the features of a new case, and derives a solution for the new case by referencing similar cases and their solutions. Our research proposes a framework of case-based reasoning system, designs a case-based reasoning system for the control of nuclear material exports, and evaluates the performance of alternative keyword extraction methods (full automatic, full manual, and semi-automatic). A keyword extraction method is an essential component of the case-based reasoning system as it is used to extract key features of the cases. The full automatic method was conducted using TF-IDF, which is a widely used de facto standard method for representative keyword extraction in text mining. TF (Term Frequency) is based on the frequency count of the term within a document, showing how important the term is within a document while IDF (Inverted Document Frequency) is based on the infrequency of the term within a document set, showing how uniquely the term represents the document. The results show that the semi-automatic approach, which is based on the collaboration of machine and human, is the most effective solution regardless of whether the human is a field expert or a student who majors in nuclear engineering. Moreover, we propose a new approach of computing nuclear document similarity along with a new framework of document analysis. The proposed algorithm of nuclear document similarity considers both document-to-document similarity (${\alpha}$) and document-to-nuclear system similarity (${\beta}$), in order to derive the final score (${\gamma}$) for the decision of whether the presented case is of strategic material or not. The final score (${\gamma}$) represents a document similarity between the past cases and the new case. The score is induced by not only exploiting conventional TF-IDF, but utilizing a nuclear system similarity score, which takes the context of nuclear system domain into account. Finally, the system retrieves top-3 documents stored in the case base that are considered as the most similar cases with regard to the new case, and provides them with the degree of credibility. With this final score and the credibility score, it becomes easier for a user to see which documents in the case base are more worthy of looking up so that the user can make a proper decision with relatively lower cost. The evaluation of the system has been conducted by developing a prototype and testing with field data. The system workflows and outcomes have been verified by the field experts. This research is expected to contribute the growth of knowledge service industry by proposing a new system that can effectively reduce the burden of relying on costly human experts for the export control of nuclear materials and that can be considered as a meaningful example of knowledge service application.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.27-65
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• 2020

Many companies on information and communication technology make public their own developed AI technology, for example, Google's TensorFlow, Facebook's PyTorch, Microsoft's CNTK. By releasing deep learning open source software to the public, the relationship with the developer community and the artificial intelligence (AI) ecosystem can be strengthened, and users can perform experiment, implementation and improvement of it. Accordingly, the field of machine learning is growing rapidly, and developers are using and reproducing various learning algorithms in each field. Although various analysis of open source software has been made, there is a lack of studies to help develop or use deep learning open source software in the industry. This study thus attempts to derive a strategy for adopting the framework through case studies of a deep learning open source framework. Based on the technology-organization-environment (TOE) framework and literature review related to the adoption of open source software, we employed the case study framework that includes technological factors as perceived relative advantage, perceived compatibility, perceived complexity, and perceived trialability, organizational factors as management support and knowledge & expertise, and environmental factors as availability of technology skills and services, and platform long term viability. We conducted a case study analysis of three companies' adoption cases (two cases of success and one case of failure) and revealed that seven out of eight TOE factors and several factors regarding company, team and resource are significant for the adoption of deep learning open source framework. By organizing the case study analysis results, we provided five important success factors for adopting deep learning framework: the knowledge and expertise of developers in the team, hardware (GPU) environment, data enterprise cooperation system, deep learning framework platform, deep learning framework work tool service. In order for an organization to successfully adopt a deep learning open source framework, at the stage of using the framework, first, the hardware (GPU) environment for AI R&D group must support the knowledge and expertise of the developers in the team. Second, it is necessary to support the use of deep learning frameworks by research developers through collecting and managing data inside and outside the company with a data enterprise cooperation system. Third, deep learning research expertise must be supplemented through cooperation with researchers from academic institutions such as universities and research institutes. Satisfying three procedures in the stage of using the deep learning framework, companies will increase the number of deep learning research developers, the ability to use the deep learning framework, and the support of GPU resource. In the proliferation stage of the deep learning framework, fourth, a company makes the deep learning framework platform that improves the research efficiency and effectiveness of the developers, for example, the optimization of the hardware (GPU) environment automatically. Fifth, the deep learning framework tool service team complements the developers' expertise through sharing the information of the external deep learning open source framework community to the in-house community and activating developer retraining and seminars. To implement the identified five success factors, a step-by-step enterprise procedure for adoption of the deep learning framework was proposed: defining the project problem, confirming whether the deep learning methodology is the right method, confirming whether the deep learning framework is the right tool, using the deep learning framework by the enterprise, spreading the framework of the enterprise. The first three steps (i.e. defining the project problem, confirming whether the deep learning methodology is the right method, and confirming whether the deep learning framework is the right tool) are pre-considerations to adopt a deep learning open source framework. After the three pre-considerations steps are clear, next two steps (i.e. using the deep learning framework by the enterprise and spreading the framework of the enterprise) can be processed. In the fourth step, the knowledge and expertise of developers in the team are important in addition to hardware (GPU) environment and data enterprise cooperation system. In final step, five important factors are realized for a successful adoption of the deep learning open source framework. This study provides strategic implications for companies adopting or using deep learning framework according to the needs of each industry and business.

• The Korean Journal of Nuclear Medicine Technology
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• v.20 no.1
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• pp.32-36
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• 2016

Purpose In nuclear medicine examination, $^{131}I$ is widely used in nuclear medicine examination such as diagnosis, treatment, and others of thyroid cancer and other diseases. $^{131}I$ conducts examination and treatment through emission of ${\gamma}$ ray and ${\beta}^-$ ray. Since $^{131}I$ (364 keV) contains more energy compared to $^{99m}Tc$ (140 keV) although it displays high integrated rate and enables quick discharge through kidney, the objective of this study lies in comparing the difference in exposure dose of $^{131}I$ before and after wearing apron when handling $^{131}I$ with focus on 3 elements of external exposure protection that are distance, time, and shield in order to reduce the exposure to technicians in comparison with $^{99m}Tc$ during the handling and administration process. When wearing apron (in general, Pb 0.5 mm), $^{99m}Tc$ presents shield of over 90% but shielding effect of $^{131}I$ is relatively low as it is of high energy and there may be even more exposure due to influence of scattered ray (secondary) and bremsstrahlung in case of high dose. However, there is no special report or guideline for low dose (74 MBq) high energy thus quantitative analysis on exposure dose of technicians will be conducted based on apron wearing during the handling of $^{131}I$. Materials and Methods With patients who visited Department of Nuclear Medicine of our hospital for low dose $^{131}I$ administration for thyroid cancer and diagnosis for 7 months from Jun 2014 to Dec 2014 as its subject, total 6 pieces of TLD was attached to interior and exterior of apron placed on thyroid, chest, and testicle from preparation to administration. Then, radiation exposure dose from $^{131}I$ examination to administration was measured. Total procedure time was set as within 5 min per person including 3 min of explanation, 1 min of distribution, and 1 min of administration. In regards to TLD location selection, chest at which exposure dose is generally measured and thyroid and testicle with high sensitivity were selected. For preparation, 74 MBq of $^{131}I$ shall be distributed with the use of $2m{\ell}$ syringe and then it shall be distributed after making it into dose of $2m{\ell}$ though dilution with normal saline. When distributing $^{131}I$ and administering it to the patient, $100m{\ell}$ of water shall be put into a cup, distributed $^{131}I$ shall be diluted, and then oral administration to patients shall be conducted with the distance of 1m from the patient. The process of withdrawing $2m{\ell}$ syringe and cup used for oral administration was conducted while wearing apron and TLD. Apron and TLD were stored at storage room without influence of radiation exposure and the exposure dose was measured with request to Seoul Radiology Services. Results With the result of monthly accumulated exposure dose of TLD worn inside and outside of apron placed on thyroid, chest, and testicle during low dose $^{131}I$ examination during the research period divided by number of people, statistics processing was conducted with Wilcoxon Signed Rank Test using SPSS Version. 12.0K. As a result, it was revealed that there was no significant difference since all of thyroid (p = 0.345), chest (p = 0.686), and testicle (p = 0.715) were presented to be p > 0.05. Also, when converting the change in total exposure dose during research period into percentage, it was revealed to be -23.5%, -8.3%, and 19.0% for thyroid, chest, and testicle respectively. Conclusion As a result of conducting Wilcoxon Signed Rank Test, it was revealed that there is no statistically significant difference (p > 0.05). Also, in case of calculating shielding rate with accumulate exposure dose during 7 months, it was revealed that there is irregular change in exposure dose for inside and outside of apron. Although the degree of change seems to be high when it is expressed in percentage, it cannot be considered a big change since the unit of accumulated exposure dose is in decimal points. Therefore, regardless of wearing apron during high energy low dose $^{131}I$ administration, placing certain distance and terminating the administration as soon as possible would be of great assistance in reducing the exposure dose. Although this study restricted $^{131}I$ administration time to be within 5 min per person and distance for oral administration to be 1m, there was a shortcoming to acquire accurate result as there was insufficient number of N for statistics and it could be processed only through non-parametric method. Also, exposure dose per person during lose dose $^{131}I$ administration was measured with accumulated exposure dose using TLD rather than through direct-reading exposure dose thus more accurate result could be acquired when measurement is conducted using electronic dosimeter and pocket dosimeter.

• The Korean Journal of Air & Space Law and Policy
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• v.28 no.2
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• pp.3-35
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• 2013

Air transport today is a means of transport that is optimized for exchanges between nations. Around the world, has experienced an increase in operating and the number of airline route expansion that has entered into the international aviation agreements in order to take advantage of the air transport efficient, but the possibility of the occurrence of air transport accidents increased. When compared to the accident of other means of transport, development of air transport accidents, not high, but it leads to catastrophe aviation accident occurs. Air Transport accident many international transportation accident than domestic transportation accident, in the event of an accident, the analysis of the legal responsibility of the shipper or the like is necessary or passenger air carrier. Judgment of the legal order of discipline of air transport accident is a classification of the type of air transport agreement. Depending on the object, air transport agreements are classified into the contract of carriage of aviation of the air passenger transportation contract. For casualties occurs, air passenger transportation accident is a need more discussion of legal discipline for this particular. Korean Commercial Code, it is possible to reflect in accordance with the actual situation of South Korea the contents of the treaty, which is utilized worldwide in international air transport, even on the system, to control land, sea, air transport and welcoming to international standards. However, Korean Commercial Code, the problem of the Montreal Convention has occurred as it is primarily reflecting the Montreal Convention. As a cause of liability for damages, under the Commercial Code of Korea and the contents of the treaty precedent is reflected, the concept of accident is necessary definition of the exact concept for damages of passengers in particular. Cause of personal injury or death of passengers, in the event of an accident to the "working for the elevation" or "aircraft" on, the Montreal Convention is the mother method of Korea Commercial Code, liability for damages of air carrier defines. The Montreal Convention such, continue to be a matter of debate so far in connection with the scope of "working for the lifting of" the concepts defined in the same way from Warsaw Convention "accident". In addition, it is discussed and put to see if you can be included mental damage passenger suffered in air transport in the "personal injury" in the damage of the passenger is in the range of damages. If the operation of aircraft, injury accident, in certain circumstances, compensation for mental damage is possible, in the same way as serious injury, mental damage caused by aviation accidents not be able to live a normal life for the victim it is damage to make. So it is necessary to interpret and what is included in the injury to the body in Korea Commercial Code and related conventions, non-economic damage of passengers, clearly demonstrated from the point of view of prevention of abuse of litigation and reasonable protection of air carrier it must compensate only psychological damage that can be. Since the compensation of delay damages, Warsaw Convention, the Montreal Convention, Korea Commercial Code, there are provisions of the liability of the air carrier due to the delayed arrival of passenger and baggage, but you do not have a reference to delayed arrival, the concept of delay arrangement is necessary. The strict interpretation of the concept of delayed arrival, because it may interfere with safe operation of the air carrier, within the time agreed to the airport of arrival that is described in the aviation contract of carriage of passenger baggage, or, these agreements I think the absence is to be defined as when it is possible to consider this situation, requests the carrier in good faith is not Indian or arrive within a reasonable time is correct. The loss of passenger, according to the international passenger Conditions of Carriage of Korean Air, in addition to the cases prescribed by law and other treaties, loss of airline contracts, resulting in passengers from a service that Korean Air and air transport in question do damage was is, that the fact that Korean Air does not bear the responsibility as a general rule, that was caused by the negligence or intentional negligence of Korean Air is proof, negligence of passengers of the damage has not been interposed bear responsibility only when it is found. It is a clause in the case of damage that is not mandated by law or treaty, and responsible only if the negligence of the airline side has been demonstrated, but of the term negligence "for" intentional or negligent "Korean Air's Terms" I considered judgment of compatibility is required, and that gross negligence is appropriate. The "Korean Air international passenger Conditions of Carriage", airlines about the damage such as electronic equipment that is included in the checked baggage of passengers does not bear the responsibility, but the loss of baggage, international to arrive or depart the U.S. it is not the case of transportation. Therefore, it is intended to discriminate unfairly passengers of international flights arriving or departure to another country passengers of international flights arriving or departure, the United States, airlines will bear the responsibility for the goods in the same way as the contents of the treaty it should be revised in the direction.