• Information Systems Review
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• v.26 no.2
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• pp.155-167
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• 2024

This study recognized the importance of joint research in the field of artificial intelligence and analyzed the characteristics of the industry-academic-research technological cooperation ecosystem focusing on patents from the perspective of the Techno-Economic Segment (TES). To this end, economic entities such as companies, universities, and research institutes within the ecosystem were identified for 7,062 joint research projects out of 113,289 artificial intelligence patents over the past 10 years filed in IP5 countries since 2012. Next, this study identified the topics of technological cooperation and the characteristics of cooperation. As a result of the analysis, technological cooperation is increasing, and the frequency of all types of cooperation was high in industry-to-industry (40%) and industry-to-university (25.2%) relationships. Here, this study confirmed that the role of universities is being strengthened, with an increase in the ratio of companies with strengths in funding and analytical data, industry and universities with excellent research personnel (9.8%), and cooperation between universities (1.9%). In addition, as a result of identifying collaborative patent research areas of interest and collaborative relationships through topic modeling and network analysis, overall similar research interests were derived regardless of the type of cooperation, and applications such as autonomous driving, edge computing, cloud, marketing, and consumer behavior analysis were derived. It was confirmed that the scope of research was expanding, collaborating entities were becoming more diverse, and a large-scale network including Chinese-centered universities was emerging.

• Journal of Korea Society of Digital Industry and Information Management
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• v.11 no.1
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• pp.47-57
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• 2015

Recently, Internet of Things (IoT) have become popular for research and development in many areas. IoT makes a new intelligent network between things, between things and persons, and between persons themselves. Social network service technology is in its infancy, but, it has many benefits. Adjacent users in a social network tend to trust each other more than random pairs of users in the network. In this paper, we propose recommendation technique using social network in Internet of Things environment. We study previous researches about information recommendation, IoT, and social IoT. We proposed SIoT_P(Social IoT Prediction) using social relationships and item-based collaborative filtering. Also, we proposed SR(Social Relationship) using four social relationships (Ownership Object Relationship, Co-Location Object Relationship, Social Object Relationship, Parental Object Relationship). We describe a recommendation scenario using our proposed method.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.382-387
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• 2008

Current global construction market has grown at a dramatic expansion rate every year in connection with more market accessibility by foreign contractors. The market openness is largely due to globalization of world construction markets, rapid development of world-wide telecommunication technologies, the formation of collaborative acquisitions and joint ventures among contractors, development of regional Free Trade Blocks, and just name a few. This paper focuses on the formation of collaborative networks when expanding into new foreign markets. The social network analysis (SNA) is introduced to investigate a variety of the collaboration patterns and also their impacts on the performance. To this end, the collaboration cases of 600 international construction projects performed by Korean contractors since 1990 were collected and classified into firm's size, project types, collaboration modes, and performance levels using social network analysis. The results showed a direction in establishing business strategy associated with experienced or inexperienced contractors in international construction projects.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.5
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• pp.169-176
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• 2018

The purpose of this study is to analyze the relationship between the centrality and the research performance by conducting the social network analysis for the social science journal in the last 10 years. As a result of the relationship analysis, the correlation between centrality and research productivity was highly correlated in most groups, but the impact factor and frequency of citations were not significant. In relation with the comprehensive research such as a H-index, middle productive group correlation was more significant than the upper productive group.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.10C
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• pp.1015-1026
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• 2002

As per increasing research interest in the field of collaborative computing in recent year, the importance of security issues on that area is also incrementally growing. Generally, the persistency of collaborative system is facilitated with conventional authentication and cryptography schemes. It is however, hard to meet the access control requirements of distributed collaborative computing environments by means of merely apply the existing access control mechanisms. The distributed collaborative system must consider the network openness, and various type of subjects and objects while, the existing access control schemes consider only some of the access control elements such as identity, rule, and role. However, this may cause the state of security level alteration phenomenon. In order to handle proper access control in collaborative system, various types of access control elements such as identity, role, group, degree of security, degree of integrity, and permission should be taken into account. Futhermore, if we simply define all the necessary access control elements to implement access control algorithm, then collaborative system consequently should consider too many available objects which in consequence, may lead drastic degradation of system performance. In order to improve the state problems, we propose a novel access control framework that is suitable for the distributed collaborative computing environments. The proposed scheme defines several different types of object elements for the accessed objects and subjects, and use them to implement access control which allows us to guarantee more solid access control. Futhermore, the objects are distinguished by three categories based on the characteristics of the object elements, and the proposed algorithm is implemented by the classified objects which lead to improve the systems' performance. Also, the proposed method can support scalability compared to the conventional one. Our simulation study shows that the performance results are almost similar to the two cases; one for the collaborative system has the proposed access control scheme, and the other for it has not.

• Fashion & Textile Research Journal
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• v.24 no.1
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• pp.53-65
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• 2022

The fashion industry is facing numerous sustainability-related challenges due to growing consciousness about the egregious extent of global environmental problems. This study examines research trends on sustainable fashion based on five macro trends related to sustainable innovation in the fashion industry. Using the content analysis and network analysis methods, 115 research papers published in domestic and international journals from 2017 to 2021 were collected and analyzed. The study conclusions are as follows. First, majority of domestic papers(55.41%) focused on circular economy, whereas other topics such as consumer awareness(1.35%) and corporate social responsibility(2.70%), are yet to be thoroughly examined; majority of international papers(53.65%) focused on sharing economy and collaborative consumption, whereas other topics such as technological innovation(2.44%), are yet to be thoroughly examined. Second, domestic papers have found that many brands(68.57%) are applying the concept of circular economy, whereas international papers have found that many brands(51.56%) are applying the concept of sharing economy and collaborative consumption. The study results provide useful data for corporate management in the fashion industry.

• Journal of the Korea Society of Computer and Information
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• v.24 no.8
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• pp.87-93
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• 2019

These days, as interest in the recommendation system with deep learning is increasing, a number of related studies to develop a performance for collaborative filtering through autoencoder, a state-of-the-art deep learning neural network architecture has advanced considerably. The purpose of this study is to propose autoencoder which is used by the recommendation system to predict ratings, and we added more hidden layers to the original architecture of autoencoder so that we implemented deep autoencoder with 3 to 5 hidden layers for much deeper architecture. In this paper, therefore we make a comparison between the performance of them. In this research, we use 2-dimensional arrays and 3-dimensional tensor as the input dataset. As a result, we found a correlation between matrix entry of the 3-dimensional dataset such as item-time and user-time and also figured out that deep autoencoder with extra hidden layers generalized even better performance than autoencoder.

• Journal of Information Science Theory and Practice
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• v.10 no.1
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• pp.18-29
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• 2022

This study aimed at overviewing the situation of information literacy education and research in the Association of Southeast Asian Nations (ASEAN) region, and developing an ASEAN network model on information literacy. This research used documentary and qualitative research methods. Key resources consisted of twenty bibliometric studies and related documents and two groups of key persons. The first group consisted of twenty-seven purposive key persons from eight countries, and the second group consisted of seven key persons from five countries. The research instruments comprised a data collection form and focus group/ interviewing forms. Data was collected by focus group discussion and online interviews, and qualitative content analysis was used in data analysis and presented descriptively. Research findings showed that: 1) information literacy education and research in the ASEAN region varied across countries and placed importance on the educational context. Singapore was found to be the most leading and productive country in ASEAN in information literacy with the highest number of journal articles on the international scale, and was among the most contributing groups at the regional and global level; 2) the ASEAN Network on Information Literacy (ASEAN-NIL) has been developed as a model with its principles, objectives, management system, activities, and promotion strategies. Its strengths are an integrated scope, multidimensional orientation, and interdisciplinary and collaborative partnerships at the national, regional, and international level, suitable for the ASEAN context, the online environment, and the digital educational ecosystem.

• Toxicological Research
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• v.17
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• pp.241-249
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• 2001

Toxicology is under challenge from several new trends in science and technology, namely computer, the Internet, genome projects, genomic technologies, and combinatorial chemistry. These new trends will drastically change research style of toxicology. In addition to conventional uni cellular tests and animal tests using rodents, computer simulation, DNA chips (microarrays), in vivo tests using simple model organisms such as nematodesor flies become important routine screening tests. How to arrange these tests in tiers will become a new problem. Endocrine disruptors hypothesis is a good example for this kind of futuristic approach. Computer, particularly the Internet, is also enabling toxicologists and regulatory experts to collaborate more closely. The IPCS (International Program for Chemical Safety) which is ajoint project of WHO, ILO and UNEP, is a well-known international collaborative research for chemical risk assessments. The GINC project of IPCS is an effort to utilize the Internet for such collaborations. Some efforts were also made to establish regional collaboration network in East Asia under this project.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.13-34
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• 2012

The Paper Explores The Problem Of Recommending Talks To Attend At International Research Conferences. When Researchers Participate In Conferences, Finding Interesting Talks To Attend Is A Real Challenge. Given That Several Presentation Sessions And Social Activities Are Typically Held At A Time, And There Is Little Time To Analyze All Alternatives, It Is Easy To Miss Important Talks. In Addition, Compared With Recommendations Of Products Such As Movies, Books, Music, Etc. The Recipients Of Talk Recommendations (i.e. Conference Attendees) Already Formed Their Own Research Community On The Center Of The Conference Topics. Hence, Recommending Conference Talks Contains Highly Social Context. This Study Suggests That This Domain Would Be Suitable For Social Network-Based Recommendations. In Order To Find Out The Most Effective Recommendation Approach, Three Sources Of Information Were Explored For Talk Recommendation-Whateach Talk Is About (Content), Who Scheduled The Talks (Collaborative), And How The Users Are Connected Socially (Social). Using These Three Sources Of Information, This Paper Examined Several Direct And Hybrid Recommendation Algorithms To Help Users Find Interesting Talks More Easily. Using A Dataset Of A Conference Scheduling System, Conference Navigator, Multiple Approaches Ranging From Classic Content-Based And Collaborative Filtering Recommendations To Social Network-Based Recommendations Were Compared. As The Result, For Cold-Start Users Who Have Insufficient Number Of Items To Express Their Preferences, The Recommendations Based On Their Social Networks Generated The Best Suggestions.