• Title/Summary/Keyword: scientists' episode

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Program Development of Scientists' Episode: Focusing on Scientists' Joy, Anger, Sorrow, and Pleasure (과학자의 희로애락(喜怒哀樂)이 담긴 과학사 에피소드 활용 교육 프로그램 개발)

  • Lee, Yun-Kyung;Shin, Dong-Hee
    • Journal of The Korean Association For Science Education
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    • v.34 no.5
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    • pp.469-478
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    • 2014
  • To provide students an alternative image of science and scientist, we developed five lesson plans that include scientists' joy, anger, sorrow, and pleasure in their life. Through the 10 hour lessons with the five topics, we investigated the effect of our program on students' image change toward scientists, their science learning, and their career development in science field. Twenty high school students participated in our program and five of them were analyzed. The qualitative data included opinionnaire survey before and after the program, field note, video recording, students' worksheets, and interview. The science episode lessons that reflect the human side of scientists were designed in five steps. The first step is the one about imaging of scientists, the second step is the one about reading scientists' episode in their life, the third step is the one about investigating human side of scientists, the fourth step is the one about feeling sympathy in scientists' context, and the last step is the one about judging human side of scientists. Students participated in this program got to feel familiarity in scientists as well as confidence in science. By obtaining the alternative image of scientists after the class, it is expected that students will play roles of well-prepared supporters with scientific literacy.

ViStoryNet: Neural Networks with Successive Event Order Embedding and BiLSTMs for Video Story Regeneration (ViStoryNet: 비디오 스토리 재현을 위한 연속 이벤트 임베딩 및 BiLSTM 기반 신경망)

  • Heo, Min-Oh;Kim, Kyung-Min;Zhang, Byoung-Tak
    • KIISE Transactions on Computing Practices
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    • v.24 no.3
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    • pp.138-144
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    • 2018
  • A video is a vivid medium similar to human's visual-linguistic experiences, since it can inculcate a sequence of situations, actions or dialogues that can be told as a story. In this study, we propose story learning/regeneration frameworks from videos with successive event order supervision for contextual coherence. The supervision induces each episode to have a form of trajectory in the latent space, which constructs a composite representation of ordering and semantics. In this study, we incorporated the use of kids videos as a training data. Some of the advantages associated with the kids videos include omnibus style, simple/explicit storyline in short, chronological narrative order, and relatively limited number of characters and spatial environments. We build the encoder-decoder structure with successive event order embedding, and train bi-directional LSTMs as sequence models considering multi-step sequence prediction. Using a series of approximately 200 episodes of kids videos named 'Pororo the Little Penguin', we give empirical results for story regeneration tasks and SEOE. In addition, each episode shows a trajectory-like shape on the latent space of the model, which gives the geometric information for the sequence models.

Hunan Interaction Recognition with a Network of Dynamic Probabilistic Models (동적 확률 모델 네트워크 기반 휴먼 상호 행동 인식)

  • Suk, Heung-Il;Lee, Seong-Whan
    • Journal of KIISE:Software and Applications
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    • v.36 no.11
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    • pp.955-959
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    • 2009
  • In this paper, we propose a novel method for analyzing human interactions based on the walking trajectories of human subjects. Our principal assumption is that an interaction episode is composed of meaningful smaller unit interactions, which we call 'sub-interactions.' The whole interactions are represented by an ordered concatenation or a network of sub-interaction models. From the experiments, we could confirm the effectiveness and robustness of the proposed method by analyzing the inner workings of an interaction network and comparing the performance with other previous approaches.