• Journal of Science and Technology Studies
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• v.17 no.1
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• pp.71-115
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• 2017

In 1953, theoretical physicist George Gamow attempted to explain the process of protein synthesis by hypothesizing that the base sequence of DNA encodes a protein's amino acid sequence and, in response, proposed the nucleic acid-protein information transfer model, which he dubbed the "diamond code." After expressing interest in discussing the daring hypothesis, contemporary biologists, including James Watson, Francis Crick, Sydney Brenner, and Gunther Stent, were soon invited to join the RNA Tie Club, an informal research group that would also count biologists and various researchers in physics, mathematics, and computer engineering among its members. In examining the club's formation, growth, and decline in multidisciplinary research on deciphering the genetic code in the 1950s, this paper first investigates whether Gamow's idiosyncratic approach could be adopted as a collaborative research forum among contemporary biologists. Second, it explores how the RNA Tie Club's research agenda could have been expanded to other relevant research topics needing multidisciplinary approach? Third, it asks why and how the RNA Tie Club dissolved in the late 1950s. In answering those questions, this paper shows that analyses on the intersymbol correlation of the overlapping code functioned to integrate diverse approaches, including sequence decoding and statistical analysis, in research on the genetic code. As those analyses reveal, the peculiar approaches of the RNA Tie Club could be regarded as a useful method for biological research. The paper also concludes that the RNA Tie Club dissolved in the late 1950s due to the disappearance of the collaborative research agenda when the overlapping code hypothesis was abandoned.

• Journal of Information Technology Applications and Management
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• v.15 no.4
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• pp.159-188
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• 2008

This study suggests strategies which can enable to creation of new opportunities of competitive advantages while operating a long lasting and consistent business with major trading partners, based on interorganizational information systems (IOISs) specially established and installed for interorganizational transactions. Nowadays, IOISs based mechanism having been widely expanded as a conventional business infrastructure for the interorganizational transactions and/or exchanges, it is customary difficult to obtain any strongly sound advantage over the competitors who have adopted even the simplest deployment of the IOIS mechanisms. In this connection, this study intends to investigate the interorganizational collaborative activities conducted by under the auspicious of IOISs, focused on the prospect of the exploitation of IOISs rather than the implementation of the IOISs. In this study, we, firstly, suggest the concept of Electronic Collaboration which can be defined by the collaborative activities conducted by IOISs, compared to the ones conducted on off-line. In addition, we suggest the Electronic Collaboration as a multi-dimensional concept, constituted by three sub-constructs, the Electronic Information Sharing (EIS), the Electronic Joint Activity (EJA), and the construction of the Electronic Relational Knowledge Store (ERKS). Secondly, we empirically verify the effects of relational and environmental determinants on the Electronic Collaboration. In this study, the relational determinants relate to the variables created in interorganizational relationship like Trust, Influence, Relational Specific Asset-asset invested for the transaction-, and Continuity of the relationship. On the other hand, the environmental determinants relate to the variables surrounding the relationship which are difficult to control. We consider Product Complexity, Technological Uncertainty, and Market Variability as the domain of the environmental determinants. To test our hypotheses, we conducted both paper-based survey and online-based survey. After refining the data with missing responses, a total of 150 data was used for analysis. The results were as follows : Firstly, it is statistically significant that the Electronic Collaboration is composed of EIS, EJA, and ERKS. In particular, the results imply that the firms are able to accumulate relational knowledge base as well as to exchange information or knowledge, and to conduct joint activities through effort to further expand the Electronic Collaboration. Secondly, we have verified the individual effects of the relational and the environmental determinants on the Electronic Collaboration. Product Complexity has been revealed as the most influential variable affecting the Electronic Collaboration. Next, Interorganizational Trust and Technological Uncertainty, in that order, have been seen to have significant effects on the Electronic Collaboration. In other words, when products or services seem to be difficult to standardize, and the core technologies seem to rapidly change, the need for the Electronic Collaboration increase. In addition, the observation dictates that the interorganizational trust turns out to be a critical variable in building a relationship and in seeking further collaboration. The results, further, illustrate that the environmental determinants are relatively more effective than the relational determinants, which is not consistent with a few prior researches relational determinants emphasized. It is because this study doesn't consider the size of the firm. A few researchers have given an emphasis on the relational determinants like trust and influence, especially from the perspective of small firms in interorganizational relationship. However, in our study, where all the sizes of the firms are contained, electronic collaboration is considerably affected by the environmental determinants.

• Journal of Broadcast Engineering
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• v.17 no.2
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• pp.270-283
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• 2012

With the advent of multi-channel TV, IPTV and smart TV services, excessive amounts of TV program contents become available at users' sides, which makes it very difficult for TV viewers to easily find and consume their preferred TV programs. Therefore, the service of automatic TV recommendation is an important issue for TV users for future intelligent TV services, which allows to improve access to their preferred TV contents. In this paper, we present a recommendation model based on statistical machine learning using a collaborative filtering concept by taking in account both public and personal preferences on TV program contents. For this, users' preference on TV programs is modeled as a latent topic variable using LDA (Latent Dirichlet Allocation) which is recently applied in various application domains. To apply LDA for TV recommendation appropriately, TV viewers's interested topics is regarded as latent topics in LDA, and asymmetric Dirichlet distribution is applied on the LDA which can reveal the diversity of the TV viewers' interests on topics based on the analysis of the real TV usage history data. The experimental results show that the proposed LDA based TV recommendation method yields average 66.5% with top 5 ranked TV programs in weekly recommendation, average 77.9% precision in bimonthly recommendation with top 5 ranked TV programs for the TV usage history data of similar taste user groups.

• Journal of the Korea Society for Simulation
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• v.15 no.3
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• pp.23-30
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• 2006

A recommender system for E-commerce site receives information from customers about which products they are interested in, and recommends products that are likely to fit their needs. In this paper, we investigate several methods for large-scale product purchase data for the purpose of producing useful recommendations to customers. We apply the traditional data mining techniques of cluster analysis and collaborative filtering(CF), and CF with reduction of product-dimensionality by use of latent semantic indexing(LSI). If reduced product-dimensionality obtained from LSI shows a similar latent trend of customers for buying products to that based on original customer-product purchase data, we expect less computational effort for obtaining the nearest-neighbor for target customer may improve the efficiency of recommendation performance. From simulation experiments on synthetic customer-product purchase data, CF-based method with reduction of product-dimensionality presents a better performance than the traditional CF methods with respect to the recall, precision and F1 measure. In general, the recommendation quality increases as the size of the neighborhood increases. However, our simulation results shows that, after a certain point, the improvement gain diminish. Also we find, as a number of products of recommendation increases, the precision becomes worse, but the improvement gain of recall is relatively small after a certain point. We consider these informations may be useful in applying recommender system.

• Journal of Technology Innovation
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• v.17 no.1
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• pp.149-177
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• 2009

Today, the phase of modem war is very different from past war. That is, the winning of war depends on the ability to obtain information and high technology. The purposes of this research are to propose an effective R&D governance model in national defense sector and to present R&D strategy for obtaining core national defense technology. As a part of collaborative innovation, the strategy to exchange R&D results actively between the defense sector and the private sector will be discussed. The main contribution of this research is dearly defining the concept of R&D governance in national defense sector and applying it to an actual case. The national defense R&D governance model proposed in this paper is based on the characteristics of national defense R&D which are different from other industries. The analysis of business success factors for national defense R&D through the T-50 case study is presented in detail. The T-50 case study reveals the importance of strategic intent, core technology knowledge base, organizational structure, and project management.

• Journal of The Korean Association For Science Education
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• v.37 no.6
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• pp.1037-1049
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• 2017

The purpose of this study is to explore how the flow of discourse move and their reasoning process in dialogic argumentation during group modeling on excretion. Five groups of three to four students in the second grade of a middle school participated in the modeling practice of a Gifted Center. Analysis was conducted on argumentation during the modeling activity in which students should explain how the waste product (ammonia) leaves the body. It was found that there was a sequential argumentative process-tentative consensus, solving the uncertainty, and consensus. There were several discourse moves - 'claim' and 'counterclaim' in the stage of tentative consensus, 'query' and 'clarification of meaning' in the stage of solving the uncertainty, and 'change of claim' in the stage of consensus. Students participated in the dialogic argumentation by constructing argument collaboratively for reaching a consensus. Critical questioning in the stage of solving the uncertainty and reasoning in the stage of consensus were the impact factors of dialogic argumentation. By answering the critical questions, students changed their claims or suggested new claims by defending or rebutting previous claims. Students justified group claims with diverse argumentation scheme and scientific reasoning to reach a group consensus. These findings have implication for science educators who want to adopt dialogic argumentation in science classes.

• The Journal of the Korea Contents Association
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• v.13 no.4
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• pp.1-7
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• 2013

A personalized recommendation system is a recommendation system that recommends goods to users' taste by using an automated information filtering technology. A collaborative filtering method in this technology is a method that discriminates certain types, which represent similar patterns. Thus, it is possible to estimate the pain strength based on the data of the patients who have the past similar types and extract related conditions according to the similarity in classified patients. A representative method using the Pearson correlation coefficient for extracting the similarity weight may represent inexact results as the sample data is small according to the amount of data. Also, it has a disadvantage that it is not possible to fast draw results due to the increase in calculations as a square scale as the sample data is large. In this paper, the excellency of the intelligence pain nursing intervention u-health system implemented by comparing the scale and similarity group of the sample data for extracting significant data is verified through the evaluation of MAE and Raking scoring. Based on the results of this verification, it is possible to present basic data and guidelines of the pain of patients recognized by nurses and that leads to improve the welfare of patients.

• Journal of the Korea Society of Computer and Information
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• v.17 no.6
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• pp.163-172
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• 2012

Collaborative filtering which is used explicit method in a existing recommedation system, can not only reflect exact attributes of item but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. This paper proposes the personalized recommendation system using RFM method and k-means clustering in u-commerce which is required by real time accessablity and agility. In this paper, using a implicit method which is is not used complicated query processing of the request and the response for rating, it is necessary for us to keep the analysis of RFM method and k-means clustering to be able to reflect attributes of the item in order to find the items with high purchasablity. The proposed makes the task of clustering to apply the variable of featured vector for the customer's information and calculating of the preference by each item category based on purchase history data, is able to recommend the items with efficiency. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• Journal of The Korean Association For Science Education
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• v.39 no.1
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• pp.115-128
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• 2019

The purpose of this study is to analyze the relationship between mentor teachers' mentoring characteristics and mentee teachers' reflective practices and to investigate mentor teachers' mentoring methods to enhance mentee teachers' reflective practices based on the analysis. The participants were four beginning science teachers and four mentors who have more than seven years of teaching experience. This study compiled mentor and mentee teachers' journals, records and transcripts from mentee teachers' five periods of classes, lesson plans, evaluation forms of lessons, one-on-one mentoring records and transcripts, questionnaires conducted before, during, and after the mentoring program, and a questionnaire about the effects of one-on-one mentoring. The mentoring characteristics of mentor teachers were analyzed based on mentor's interaction methods and the contents and frequency of the support based on teaching feedback. Mentee teachers' reflective thinking was analyzed by being categorized as voluntary self-reflections of their classes and reflections on the support provided by mentor teachers. Mentee teachers' reflective practices were analyzed by utilizing RTOP. The conclusions of this study are as follows: Mentor teachers could promote mentee teachers' reflective practices by eliciting conversation that helped mentees perceive problems in their teaching practices. Mentors' questions evoking mentees' reflective thinking could elicit mentees' spontaneous self-reflection, and it led to the enhancement of self-reflection on mentors' support and reflective practices. When mentors offered the support based on teaching practices while playing a role as a facilitator to help mentees identify and solve problems by themselves, mentees' reflective practices could be promoted.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.318-331
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• 2022

With the recent development of hardware computing devices and software based frameworks, machine tasks using deep learning networks are expected to be utilized in various industrial fields and personal IoT devices. However, in order to overcome the limitations of high cost device for utilizing the deep learning network and that the user may not receive the results requested when only the machine task results are transmitted from the server, Collaborative Intelligence (CI) proposed the transmission of feature maps as a solution. In this paper, an efficient compression method for feature maps with vast data sizes to support the CI paradigm was analyzed and presented through experiments. This method increases redundancy by applying feature map reordering to improve compression efficiency in traditional video codecs, and proposes a feature map method that improves compression efficiency and maintains the performance of machine tasks by simultaneously utilizing image compression format and video compression format. As a result of the experiment, the proposed method shows 14.29% gain in BD-rate of BPP and mAP compared to the feature compression anchor of MPEG-VCM.