• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.308-313
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• 1998

In this paper we will discuss a type of inductive learning called learning from examples, whose task is to induce general description of concepts from specific instances of these concepts. In many real life situations, however, new instances can be added to the set of instances. It is first proposed within the framework of rough set theory, for such cases, an algorithm to find minimal set of rules for decision tables without recalculation for overcall set of instances. The method of learning presented here is base don a rough set concept proposed by Pawlak[2][11]. It is shown an algorithm to find minimal set of rules using reduct change theorems giving criteria for minimum recalculation with an illustrative example. Finally, the proposed learning algorithm is applied to fuzzy system to learn sampled I/O data.

• English Language & Literature Teaching
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• v.11 no.3
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• pp.71-87
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• 2005

The role of interaction in second language (L2) classrooms has been examined from different angles, ranging from early studies of foreigner talk to the studies of the teacher- and task-based talk. However, most of the research on L2 classroom interaction has been based on a traditional psycholinguistic view of language and learning, failing to reconceptualize a broad and holistic understanding of L2 learning. Currently, many researchers have attempted to explore and describe classroom interaction in L2 classrooms from a sociocultural perspective. The purpose of this paper is to discuss Vygotsky's theoretical framework in terms of L2 classroom interaction and research from a sociocultural perspective, by describing three key concepts (zone of proximal development, private speech, and activity theory) in Vygotsky's theoretical framework and relating them to L2 classroom interaction. The results demonstrated the importance of social interaction for second language acquisition with the review of the related research study. It was also suggested that the dynamic and interactive processes of second language learning in the classroom should be valued by L2 researchers as well as L2 teachers. Finally, implications for the concepts for L2 classroom research and pedagogy are presented in the conclusion.

• Journal of Industrial Convergence
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• v.7 no.2
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• pp.33-44
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• 2009

I first present a set of features for distinguishing group learning from other concepts. I then develop a framework for understanding group learning that focuses on learning's basic processes at the group level of analysis: storage, and retrieval.

• Journal of Industrial Convergence
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• v.7 no.2
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• pp.45-57
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• 2009

I first present a set of features for distinguishing group learning from other concepts. I then develop a framework for understanding group learning that focuses on learning's basic processes at the group level of analysis: sharing.

• Journal of The Korean Association For Science Education
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• v.33 no.3
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• pp.650-663
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• 2013

Multidimensional scaling can be used to identify relationships among concepts, revealing the structure of the cognitive framework by measuring distances within perceptual maps. The current study sought to examine the relationships among concepts related to photosynthesis in 2,844 $3^{rd}-11^{th}$ grade science students. The questionnaire included items on 'location,' 'products,' 'reactants,' and 'environmental factors', presenting images related to each theme. Students provided responses corresponding to particular topics, and reported the extent to which the concept was related to the topic on a scale from 1 to 30. The survey results were as follows: first, students were not able to clearly distinguish between or understand the four main topics. Second, students organized their cognitive structures by closely associating related concepts after learning. Third, the presented concepts revealed a mixture of scientific and non-scientific concepts, suggesting that students needed to clearly distinguish the preconceptions through which they organized concepts, so that they are suitable for cognitive structures based on learning. Furthermore, non-scientific concepts within perceptions were consistently maintained throughout learning, affecting the proximity of scientific concepts.

• The Mathematical Education
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• v.50 no.3
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• pp.263-284
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• 2011

We took note of the fact that there were not many studies on improvement of mathematics learning in the field of statistics for the minority students from the families who belonged to the Low-SES. This study was to help them understand the concepts and principles of mathematics, motivate them for mathematics learning, and have them feel familiar with it. The subjects were 12 students from the low-SES families among the sophomores of 00 High School in Gyeonggi-do. Although it could not be achieved effectively in the short-term of learning for the slow learners, their understanding of basic concepts and confidence, interests and concerns in statistical learning were remarkably changed, compared to their work in the beginning period. Our discourse classes using various topics and examples were well perceived by the students whose performance was improved up to the 3rd thinking level of Mooney's framework. Also, a meaningful instructional model for slow learners(IMSL) was found through the discourse.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.331-337
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• 1997

In this paper we will discuss a type of inductive learning called learning from examples, whose task is to induce general descriptions of concepts from specific instances of these concepts. In many real life situations however new instances can be added to the set of instances. It is first proposed within the framework of rough set theory, for such cases, an algorithm to find minimal set of rules for decision tables without recalculation for overall set of instances. The method of learning presented here is based on a rough set concept proposed by Pawlak[2]. It is shown an algorithm to fund minimal set of rules using reduct change theorems giving criteria for minimum recalculation and an illustrative example.

• Journal of Ubiquitous Convergence Technology
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• v.1 no.1
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• pp.18-22
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• 2007

Multi-Instance Learning(MIL) performs well to deal with inherently ambiguity of images in multimedia retrieval. In this paper, an effective framework for Contented-Based Image Retrieval(CBIR) with MIL techniques is proposed, the effective mechanism is based on the image segmentation employing improved Mean Shift algorithm, and processes the segmentation results utilizing mathematical morphology, where the goal is to detect the semantic concepts contained in the query. Every sub-image detected is represented as a multiple features vector which is regarded as an instance. Each image is produced to a bag comprised of a flexible number of instances. And we apply a few number of MIL algorithms in this framework to perform the retrieval. Extensive experimental results illustrate the excellent performance in comparison with the existing methods of CBIR with MIL.

• ETRI Journal
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• v.42 no.6
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• pp.965-975
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• 2020

This paper proposes a new framework for the development and deployment of honeypots for evolving malware threats. As new technological concepts appear and evolve, attack surfaces are exploited. Internet of things significantly increases the attack surface available to malware developers. Previously independent devices are becoming accessible through new hardware and software attack vectors, and the existing taxonomies governing the development and deployment of honeypots are inadequate for evolving malicious programs and their variants. Malware-propagation and compromise methods are highly automated and repetitious. These automated and repetitive characteristics can be exploited by using embedded reinforcement learning within a honeypot. A honeypot for automated and repetitive malware (HARM) can be adaptive so that the best responses may be learnt during its interaction with attack sequences. HARM deployments can be agile through periodic policy evaluation to optimize redeployment. The necessary enhancements for adaptive, agile honeypots require a new development and deployment framework.

• 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.