• Proceedings of the Korea Contents Association Conference
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• 2019.05a
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• pp.183-184
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• 2019

본 논문에서는 로봇과 사람의 자연스러운 감정 소통을 위한 비디오 시퀀스 표정생성 대화 시스템을 제안한다. 제안된 시스템에서는 실시간 비디오 데이터로 판단된 관객의 감정 상태를 반영한 대답을 하며, 딥러닝(Deep Learning)을 활용하여 대화의 맥락에 맞는 로봇의 표정을 실시간 생성한다. 본 논문에서 관객의 표정을 위해 3만여개의 비디오 데이터로 학습한 결과 88%의 학습 정확도로 표정 생성이 가능한 것으로 확인되었다. 본 연구는 로봇 표정 생성에 딥러닝 방식을 적용한 것에 그 의의가 있으며 향후 대화 시스템 자체에도 딥러닝 방식을 확대 적용하기 위한 초석이 될 수 있다는 점에 의의가 있다.

• Korean Journal of Cognitive Science
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• v.24 no.2
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• pp.133-171
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• 2013

The gist of a scene can be identified in less than 100msec, and violation in the gist can influence the way to allocate attention to the parts of a scene. In other words, people tend to allocate more attention to the object(s) inconsistent with the gist of a scene and to have better memory of them. To investigate the effects of contextual consistency on the attention allocation and object memory, two experiments were conducted. In both experiments, a $3{\times}2$ factorial design was used with scene presentation time(2s, 5s, and 10s) as a between-subject factor and object-background contextual consistency(consistent, inconsistent) as a within-subject factor. In Experiment 1, eye movements were recorded while the participants viewed line-drawing scenes. The results showed that the eye movement patterns were different according to whether the scenes were consistent or not. Context-inconsistent objects showed faster initial fixation indices, longer fixation times, more frequent returns than context-consistent ones. These results are entirely consistent with those of previous studies. If an object is identified as inconsistent with the gist of a scene, it attracts attention. Furthermore, the inconsistent objects and their locations in the scenes were recalled better than the consistent ones and their locations. Experiment 2 was the same as Experiment 1 except that a dual-task paradigm was used to reduce the amount of attention to allocate to the objects. Participants had to detect the positions of the probe occurring every second while they viewed the scenes. Nonetheless, the result patterns were the same as in Experiment 1. Even when the amount of attention to allocate to the scene contents was reduced, the same effects of contextual inconsistency were observed. These results indicate that the object-background contextual consistency has a strong influence on the way of allocating attention and the memory of objects in a scene.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.180-185
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• 2012

Social Learning is a new study model of future knowledge information society. In different existing study, it concentrate on relationship with others and design to connect studying with social effect as a study platform using social media such as Blog, SNS, UCC, Microblog. In my paper, social learning characteristics are described to understand social learning, that is 3 keyword such as context, connectivity, collaboration. Also we investigate social media characteristics and social media how to be used social learning. Also social learning system building method using facebook is presented.

• Annual Conference on Human and Language Technology
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• 2023.10a
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• pp.517-521
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• 2023

최근 거대 언어모델의 등장과 동시에, 많은 매개변수를 효과적으로 학습하는 방법인 효율적인 매개변수 미세조정(Parameter Efficient Fine-Tuning) 연구가 활발히 진행되고 있다. 이 중에서 Adapter는 사전학습 언어모델(Pretrained Language Models)에 몇 개의 추가 병목 구조 모듈을 삽입하여 이를 학습하는 방식으로, 등장한 이후 다양한 연구 영역에서 주목받고 있다. 그러나 몇몇 연구에서는 병목 차원을 증가시켜 미세 조정보다 더 나은 성능을 얻는다는 주장이 나오면서, 원래의 의도와는 다른 방향으로 발전하고 있다는 의견도 있다. 이러한 맥락에서, 본 연구에서는 기존의 Adapter 구조를 개선한 MAdapter를 제안한다. MAdapter는 본래 Adapter에 중간 층을 추가하되 학습 가능한 매개변수의 수는 오히려 줄이는 방법으로, 전체 매개변수 수 대비 1% 내외 만을 학습에 활용하며, Adapter 대비 절반 정도의 매개변수만을 사용하여 기존 결과와 비슷하거나 더 나은 성능을 얻을 수 있는 것을 확인할 수 있다. 또한, 병목차원 크기 비교와 중간 층 개수 분석을 통한 최적의 MAdapter 구조를 찾고, 이로써 효율적인 매개변수 미세조정 방법을 제시한다.

• Journal of Science Education
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• v.42 no.2
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• pp.165-181
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• 2018

This study compares and analyzes the characteristics and effects of various educational contextual variables according to students' achievement level and gender groups based on the results of PISA 2015 science domain. PISA 2015 included additional variables about teaching-learning and affective characteristics in the field of science, because science was the main domain of PISA 2015. The results of the mediation analysis using a multiple group structural equation model showed that the environment and strategy for the teaching and learning had a positive effect on the affective characteristics, and also positively affected science achievement through the mediator of the affective characteristics. Particularly, the environment and strategy for the teaching and learning was the most effective in improving the affective characteristics for the low achievement group. It was found that the difference of the mediated effect between achievement level groups was statistically significant, but that between male and female students was not. Therefore, the appropriate the environment and strategy for the teaching and learning will need to be emphasized consistently to improve students' cognitive and affective achievement. The implications and suggestions of these results were discussed.

• Journal of The Korean Association For Science Education
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• v.35 no.1
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• pp.15-25
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• 2015

This study examined pre-service secondary physics teachers' epistemological beliefs about scientific knowledge, science learning, and science teaching in two different science content topics, Lamarckism and the impetus theory. Two sets of open-ended questionnaires, for each of the topics respectively, were developed in the same format. The pre-service teachers completed the questionnaires at one month intervals. The beliefs were analyzed in two dimensions, knowledge justification and knowledge change for each belief area. The findings show that the majority of pre-service teachers held sophisticated epistemological beliefs about scientific knowledge regardless of content topics. On the other hand, more pre-service teachers exhibited sophisticated beliefs about science learning in the context impetus theory than Lamarckism. In the area of science teaching, the majority of pre-service teachers demonstrated a sophisticated view in knowledge justification but a naive view in knowledge change. When consistency across science topics and belief areas were examined, few pre-service teachers held consistent epistemological beliefs across all topics and areas. The difference in the levels of sophistication in belief areas showed that the pre-service teachers did not connect their epistemological beliefs about science knowledge to their ideas about science teaching and learning. This disconnection seems to make the consistency across topics and areas complicated. The difference in epistemological beliefs about science learning and teaching between two science topics need further inquiry. Implications for teacher education are offered.

• Communications of Mathematical Education
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• v.19 no.2 s.22
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• pp.389-407
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• 2005

수학은 위계적이고 구조적인 특성을 가지고 있어서 학생들이 적절하게 학습하면 내적 동기유발이 가능하고 흥미 있게 학습해 나갈 수 있는 반면 단편적인 지식들로 학습하려 한다면 그 양이 방대해지고 제대로 이해하기가 어렵다. 그러므로 교사는 수학적 지식의 구조를 깨달아 지식의 본체가 내적으로 어떻게 조직되고 상호 관련되어 있는지 알아야 하고 학생들이 수학적인 아이디어와 절차를 획득하고 탐구하게 하는 적절한 문제를 제시하여 문제해결을 통해 가르쳐 가는 방법을 생각해야 할 것이다. 이 때에 학생들은 문제해결 과정에서 능동적인 역할을 하면서 자신이 학습하고 있는 것의 핵심을 인식하고 호기심을 갖고 유의미한 기능들을 이끌어내는 학습을 해야 하는데, 이는 오랜 전통의 탐구 학습과 그 맥락을 같이 하는 것이다. 수학교과 고유의 특성을 살려 지식의 구조를 가르침에 있어서 교수 방법으로의 문제해결을 통한 지도와 학습 방법으로의 탐구학습 과정은 잘 조화될 수 있다. 이러한 조화된 모습을 드러나게 하고자 초등학교 5학년 가 단계에서 '평면도형의 넓이와 둘레 사이의 관계'를 탐구하게 하는 문제해결을 통한 탐구학습 과제를 제시해 보았다. 30-40년을 거슬러 올라가는 역사를 갖는 지식의 구조나 탐구학습, 문제해결에 대한 관심은 오늘날에도 여전히 시사하는 바가 크다고 하겠다. 수학교육에 관한 연구들은 완전히 새로운 것이기보다는 이전의 것들이 주는 의미를 되새기고 오늘의 상황에 비추어 해석할 때 수학교육은 한 단계 올라서게 된다.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.101-125
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• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.

• 대한공업교육학회지
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• v.38 no.2
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• pp.1-26
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• 2013

This study inquired following research questions by a narrative research method : What was the job of an engineer in mechanical design field? How did he fulfill his job-related learning in his workplace? What were the context and the characteristic of the job-related learning in the workplace? And some implications of the job-related learning on engineering education were discussed. We identified that the research participant's career as a mechanical engineer has developed through three stages. At first, he engaged on conceptual design of a semi-conductor test machine through self-initiated learning from basic to whole system of the machine. At second stage, he leaded a design group for the concrete design of a ball type semi-conductor test machine. In this stage he learned the meaning of cooperation and cooperative learning. At third stage, he initiated to found an entrepreneur company that was specified to design a semi-conductor test machine. He became CEO of the company. He learned the R & D policy making through contacts with global company, visiting exhibition in abroad. Eventually his main task as a mechanical engineer was the problem solving in the process of machine design. He had experienced and learned through his works : project management, independent fulfilling of tasks, functional analysis and reverse engineering, conceptualizing and test, cohesive cooperation, dialogue and discussion, mediation of conflict, human relationship, leadership. The implication of the narrative analysis on engineering education is, proposed, to give the students more chances to experience and to learn such activities.

• Korean Journal of Cognitive Science
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• v.33 no.2
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• pp.109-133
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• 2022

Episodic memory consists of a core event and the associated contexts. Although the role of the hippocampus and its neighboring regions in contextual representations during encoding has become increasingly evident, it remains unclear how these regions handle various context-specific information other than spatio-temporal contexts. Using high-resolution functional MRI, we explored the patterns of the medial temporal lobe (MTL) and cortical regions' involvement during the encoding of various types of contextual information (i.e., journalism principle 5W1H): "Who did it?," "Why did it happen?," "What happened?," "When did it happen?," "Where did it happen?," and "How did it happen?" Participants answered six different contextual questions while looking at simple experimental events consisting of two faces with one object on the screen. The MTL was divided to sub-regions by hierarchical clustering from resting-state data. General linear model analyses revealed a stronger activation of MTL sub-regions, the prefrontal lobe (PFC), and the inferior parietal lobule (IPL) during social (Who), behavioral (How), and intentional (Why) contextual processing when compared with spatio-temporal (Where/When) contextual processing. To further investigate the functional networks involved in contextual encoding dissociation, a multivariate pattern analysis was conducted with features selected as the task-based connectivity links between the hippocampal subfields and PFC/IPL. Each social, behavioral, and intentional contextual processing was individually and successfully classified from spatio-temporal contextual processing, respectively. Thus, specific contexts in episodic memory, namely social, behavior, and intention, involve distinct functional connectivity patterns that are distinct from those for spatio-temporal contextual memory.