• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.10
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• pp.1698-1711
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• 2011

Knowledge is often represented as a set of rules or a semantic network in intelligent systems. Recently, ontology has been widely used to represent semantic knowledge, because it organizes thesaurus and hierarchal information between concepts in a particular domain. However, it is not easy to collect semantic relationships among concepts. Much time and expense are incurred in ontology construction. Collective intelligence can be a good alternative approach to solve these problems. In this paper, we propose a collective intelligence approach of Games With A Purpose (GWAP) to collect various semantic resources, such as words and word-senses. We detail how to construct the semantic information model or ontology from the collected semantic resources, constructing a system named FunWords. FunWords is a Korean lexical-based semantic resource collection tool. Experiments demonstrated the resources were grouped as common nouns, abstract nouns, adjective and neologism. Finally, we analyzed their characteristics, acquiring the semantic relationships noted above. Common nouns, with structural semantic relationships, such as hypernym and hyponym, are highlighted. Abstract nouns, with descriptive and characteristic semantic relationships, such as synonym and antonym are underlined. Adjectives, with such semantic relationships, as description and status, illustration - for example, color and sound - are expressed more. Last, neologism, with the semantic relationships, such as description and characteristics, are emphasized. Weighting the semantic relationships with these characteristics can help reduce time and cost, because it need not consider unnecessary or slightly related factors. This can improve the expressive power, such as readability, concentrating on the weighted characteristics. Our proposal to collect semantic resources from the collective intelligence approach of GWAP (our FunWords) and to weight their semantic relationship can help construct the semantic information model or ontology would be a more effective and expressive alternative.

• Journal of The Korean Association of Information Education
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• v.21 no.1
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• pp.139-149
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• 2017

Artificial intelligence education is very important in the 21st century knowledge information society. Even if it is very important to understand artificial intelligence and practice computer programming in computer education in the fourth industrial revolution, but there is no teaching and learning model to understand artificial intelligence and computer programming education. In this paper, the proposed model consists of problem understanding step, data organizing step, artificial intelligence model setting step, programming step, and report writing step. At the program step, students can choose to copy, transform, create, and challenge steps to their level. In this study, the validity of the model was proved by the Delphi evaluation of elementary school teachers. The results of this study provide a good opportunity for elementary school students to practice artificial intelligence programs.

• Knowledge Management Research
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• v.19 no.3
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• pp.173-187
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• 2018

This study was aimed at suggesting an integrative framework for creating collective intelligence and enhancing group performance after reviewing previous studies including those related to learning organizations, organizational learning, knowledge management, and collective intelligence. In the first, we examined that the similarities and differences between collective intelligence and other similar concepts, such as learning organizations, organizational learning, and knowledge management. Next, an integrative framework for creating collective intelligence and channeling it into strong group performance were suggested. In this process, we reviewed conditions for creating collective intelligence and segmented the major variables as expectancy, valence, and instrumentality, according to Vroom's (1964) expectancy theory. Characteristics of problems and the roles of leaders were respectively considered as valence for inducing collaboration and expectancy for managing probability to achieve goals. Instrumental factors were also adopted from conditions for creating group intelligence suggested from several researchers, such as creativity, openness, willingness for working together, horizontal communication, centralization in decision making, and building effective information and communication technology system and active usage of it. We discussed two potentially disputable matters about the scope and level of collective intelligence and group performance and suggest several theoretical and practical implications in the Discussion.

• Journal of Intelligence and Information Systems
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• v.1 no.1
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• pp.23-41
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• 1995

Business researchers have traditionally used statistical techniques for classification. In late 1980's, inductive learning started to be used for business classification. Recently, neural network began to be a, pp.ied for business classification. This study reviews the business classification studies, identifies a neural network a, pp.oach as the most powerful classification tool, and discusses the problems and issues in neural network a, pp.ications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.2
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• pp.431-448
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• 2008

The change of management paradigm is that information technology change according to technology evolution at present is applied to corporate management, is that management level must be adapted to uncertainty management environment with activity and be made decision based on analyzed real time information through information system. This produces the effective target achievement and efficiency business productivity guarantee. At the present day, importation of business intelligence like enterprise information system has been the essential factor in business activities. Therefore, It is very important to give lessons the enterprises for building the business intelligence selecting the major success factors of more influence to managing results. In this paper, to authorize the research model and research constructions through theory study of literatures and surveying statics analysis prove the relational influences among the influencing factors related business intelligence system buliding.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.89-97
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• 2022

This research intends the provide the influence of emotional intelligence and resilience on burnout of University sports athletes. For this purpose, purposive sampling method was used. The subject of this study were 257 University sports athletes from Korea. Data were collected through the self-administrated questionaire which were used by preceding study. The frequency analysis, factor analysis, correlation analysis and multiple regression analysis were used to solve the research problems in this study. The conclusion were drawn as follows. First the emotion intelligence influenced to resilience significantly. Second, the emotion intelligence influenced to burnout partially. Third, the resilience influenced to burnout partially.

• Annual Conference of KIPS
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• 2021.11a
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• pp.805-807
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• 2021

In-hospital cardiac arrest is a significant problem for medical systems. Although the traditional early warning systems have been widely applied, they still contain many drawbacks, such as the high false warning rate and low sensitivity. This paper proposed a strategy that involves a deep learning approach based on a novel interpretable deep tabular data learning architecture, named TabNet, for the Rapid Response System. This study has been processed and validated on a dataset collected from two hospitals of Chonnam National University, Korea, in over 10 years. The learning metrics used for the experiment are the area under the receiver operating characteristic curve score (AUROC) and the area under the precision-recall curve score (AUPRC). The experiment on a large real-time dataset shows that our method improves compared to other machine learning-based approaches.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.45-48
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• 2022

건축물 안전 점검은 대부분 전문가의 현장 방문을 통한 육안검사다. 그중 균열 검사는 건물 위험도를 나타내는 중요한 지표로써 발생 위치, 진행성, 크기를 조사하는데, 최근 균열 조사 방식에 대해 객관성과 체계성을 보완할 딥러닝 개발이 활발하다. 그러나 균열 이미지는 외부 현장에 모양, 규모도 많은 종류라 도메인이 다양해야 하는데 대부분 제한된 환경과 실제적인 균열 검사와는 무관한 데이터로 구성되어 실효적이지 않다. 본 연구에서는 균열 조사에 적합하고 Wild 환경에 적용 가능한 POC 데이터셋을 소개한다. 기존 균열 공인 데이터셋 4종의 특징과 한계점을 분석을 토대로 고해상도 이미지로써 균열의 세부 특징을 담았고 균열 유사 환경과 조건들을 추가 촬영해 균열 검출에 강인하게 학습되도록 지향하였다. 정제 및 라벨링 작업을 거친 POC 데이터 셋은 균열 검출모델인 YOLO-v5으로 성능을 실험하였고, mAP(mean Average Precision) 75.5%로 높은 검출률을 보였다. POC 데이터셋으로 더욱 도메인에 적응적(Domain-adapted)인 인공지능 모델을 개발하여 건물, 댐, 교량 등 각종 대형 건축물에 대한 안전하고 효과적인 안전 관리 도구로써 활용할 것을 기대한다.

• Annual Conference of KIPS
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• 2023.11a
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• pp.188-189
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• 2023

본 연구는 사용자가 USB에 내장된 스크립트를 실행하여 실시간으로 활성 및 비활성 데이터를 수집하는 라이브 포렌식 도구의 개발에 관한 것이다. 이 도구는 컴퓨터에 USB를 삽입하고 특정 스크립트를 실행하여 중요한 디지털 증거물을 추출하고 분석하는 기능을 제공한다. 도구는 Linux와 Windows 운영 체제용 32비트 및 64비트 버전으로 제작되었으며, 대량의 데이터 처리 시간과 저장 공간 문제를 해결하여 필요한 특정 데이터만 신속하게 추출할 수 있는 효율적인 방법을 제공한다. 이 도구는 활성 데이터와 비활성 데이터를 수집하며, 활성 데이터에는 레지스터, 네트워크 정보, 프로세스 정보, 사용자 정보 등이 포함되며, 비활성 데이터에는 메타데이터, 시스템 설정 정보, 로그 파일 등이 포함된다. 이 연구에서는 라이브 포렌식 도구의 사용 방법과 수집된 결과, 데이터 분석 방법, 그로 인한 보안 이점에 대해 다루고 있다.