• Journal of Korea Multimedia Society
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• v.8 no.4
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• pp.566-574
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• 2005

In spite of recent increase in the use of avatar in Web and Virtual Reality, there has not been a service that allows users to control directly the avatar behaviors. In addition, the conventional behavior control languages required a lot of complicated information for controlling the avatar motions. Moreover, in order to apply written languages to a different task domain, it was necessary to modify or rewrite the languages. In this paper, we define Task-Level Behavior Description Language and Motion Representation Language for more simple control of the avatar behavior. The first thing allows describing the avatar behaviors in each task domain, and The second thing enables writing detailed data for motion control. And in this paper, we developed an interpreter which can automatically change the Behavior Description Language to the Motion Representation Language. So this system allow users control the avatar behavior simply with only use the Behavior Description Language. The system was applied to shopping mall and the Task-level Behavior Description Language was compared with conventional languages to see how it was more effective in behavior description.

• Education of Primary School Mathematics
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• v.8 no.1
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• pp.23-32
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• 2004

It is not too much to say that problem solving is still the focus of school mathematics though the trend of mathematics education for ten year from the one of 1980 is problem solving and the one of mathematics education for ten year from the one of 1990 is standards and constructivism. There are so many crucial clues or methods in good problem solving but I think that one of them is a representation. So, the purpose of this study is to investigate what is the meaning of representation in general and why representation is so important in elementary mathematics learning, Moreover, I have analyzed the gifted children's thinking of representation which is appeared in the previous internet home task of 40 gifted children who are selected through the examination of 1st, 2nd with paper and pencil and 3rd with practical skill and interview and finally I have presented some examples of children's representation how they use representation to model, investigate and understand special concept more easily in elementary school mathematics class.

• ETRI Journal
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• v.44 no.5
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• pp.794-804
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• 2022

Transformer models have had a great impact on natural language processing (NLP) in recent years by realizing outstanding and efficient contextualized language models. Recent studies have used transformer-based language models for various NLP tasks, including Persian named entity recognition (NER). However, in complex tasks, for example, NER, it is difficult to determine which contextualized embedding will produce the best representation for the tasks. Considering the lack of comparative studies to investigate the use of different contextualized pretrained models with sequence modeling classifiers, we conducted a comparative study about using different classifiers and embedding models. In this paper, we use different transformer-based language models tuned with different classifiers, and we evaluate these models on the Persian NER task. We perform a comparative analysis to assess the impact of text representation and text classification methods on Persian NER performance. We train and evaluate the models on three different Persian NER datasets, that is, MoNa, Peyma, and Arman. Experimental results demonstrate that XLM-R with a linear layer and conditional random field (CRF) layer exhibited the best performance. This model achieved phrase-based F-measures of 70.04, 86.37, and 79.25 and word-based F scores of 78, 84.02, and 89.73 on the MoNa, Peyma, and Arman datasets, respectively. These results represent state-of-the-art performance on the Persian NER task.

• Korean Journal of Child Studies
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• v.22 no.3
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• pp.17-29
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• 2001

In this study, children's representation of attachment was assessed by the Attachment Story Completion Task(Bretherton, Ridgeway, & Cassidy, 1990) administered to 101 5-to 6-year-old children(56boys, 45girls). Teacher-child relationship and social competence were evaluated by a questionnaire administered to preschool teachers. Based on the children's representation of attachment in their narrative responses to the story stems, 56.4% of the children were classified as having secure, 22.8% as insecure-avoidant, and 20.8% as insecure-disorganized attachments. Children with secure representation of attachment exhibited more social competence and fewer behavioral problems in the child-care setting than children with insecure representation of attachment. This study showed that the child's internal model of attachment formed from experience with caregiver is capable of transmitting social relationships outside the home.

• Phonetics and Speech Sciences
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• v.5 no.4
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• pp.157-169
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• 2013

Using a target detection task, this study compared the processing automaticity of phonemes and features in spoken syllable stimuli to determine the primitive representation in speech perception, phoneme or distinctive feature. For this, we modified the visual search task(Treisman et al., 1992) developed to investigate the processing of visual features(ex. color, shape or their conjunction) for auditory stimuli. In our task, the distinctive features(ex. aspiration or coronal) corresponded to visual primitive features(ex. color and shape), and the phonemes(ex. /$t^h$/) to visual conjunctive features(ex. colored shapes). The automaticity is measured by the set size effect that was the increasing amount of reaction time when the number of distracters increased. Three experiments were conducted. The laryngeal features(experiment 1), the manner features(experiment 2), and the place features(experiment 3) were compared with phonemes. The results showed that the distinctive features are consistently processed faster and automatically than the phonemes. Additionally there were differences in the processing automaticity among the classes of distinctive features. The laryngeal features are the most automatic, the manner features are moderately automatic and the place features are the least automatic. These results are consistent with the previous studies(Bae et al., 2002; Bae, 2010) that showed the perceptual hierarchy of distinctive features.

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.83-89
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• 2017

In this paper, we propose an extended method of one-hot Long Short-Term Memory (LSTM) and evaluate the performance on spam filtering task. Most of traditional methods proposed for spam filtering task use word occurrences to represent spam or non-spam messages and all syntactic and semantic information are ignored. Major issue appears when both spam and non-spam messages share many common words and noise words. Therefore, it becomes challenging to the system to filter correct labels between spam and non-spam. Unlike previous studies on information filtering task, instead of using only word occurrence and word context as in probabilistic models, we apply a neural network-based approach to train the system filter for a better performance. In addition to one-hot representation, using term weight with attention mechanism allows classifier to focus on potential words which most likely appear in spam and non-spam collection. As a result, we obtained some improvement over the performances of the previous methods. We find out using region embedding and pooling features on the top of LSTM along with attention mechanism allows system to explore a better document representation for filtering task in general.

• Korean Journal of Cognitive Science
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• v.11 no.3_4
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• pp.73-81
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• 2000

This study was conducted to investigate some characteristics of representation of category knowledge and script knowledge. Using primed lexical decision task with higher level primers in the representation structure, Experiment 1 examined the interaction effects between knowledge type and concept typicality. It was found that the concept typicality has some effects in category representation, while it has no significant effect in script representation. In Experiment 2, primers of the lower hierarchy in the representation structure were employed. The results showed that the main effect of knowledge type was significant: the response time for category knowledge was faster than that for script knowledge. Typicality effect did not show in this experiment. The results of t the two experiments suggest that category knowledge is represented in hierarchy and typicality. while script knowledge may lack in that characteristics. Other aspects of the differences in characteristics of category- and script- knowledge representation were discussed,

• The KIPS Transactions:PartB
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• v.11B no.5
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• pp.597-602
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• 2004

In spite of recent increase in the use of avatar systems in Web and Virtual Reality, there has not been a service that allows users to control directly the avatar behaviors. In addition, the conventional behavior control languages required a lot of complicated information for controlling the behaviors, so that users had difficulty using them. To apply written languages to a different task domain, moreover, it was necessary to modify or rewrite the languages. In this paper, for the avatar behavior control more simply define, “Task-Level Behavior Description Language,” which allows description the avatar behaviors in each task domain and “Motion Representation Language,” which enables writing detailed data for motion control. The system, developed in this paper, “included an Interpreter,” which automatically creates the Motion Representation Language, allowing users to easily control the avatar behaviors simply with the Behavior Description Language. The system was also applied to cyber classes, and the Task-level Behavior Description Language was compared with conventional languages to see how it was more effective in behavior description.

• Journal of KIISE:Software and Applications
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• v.31 no.10
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• pp.1412-1417
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• 2004

Sparse coding or independent component analysis (ICA) which is a holistic representation, was successfully applied to elucidate early auditor${\gamma}$ processing and to the task of sound classification. In contrast, parts-based representation is an alternative way o) understanding object recognition in brain. In this thesis we employ the non-negative matrix factorization (NMF) which learns parts-based representation in the task of sound classification. Methods of feature extraction from the spectro-temporal sounds using the NMF in the absence or presence of noise, are explained. Experimental results show that NMF-based features improve the performance of sound classification over ICA-based features.

• The Journal of the Korea Contents Association
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• v.10 no.9
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• pp.97-106
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• 2010

Various time series representation methods have been suggested in order to process time series data efficiently and effectively. SAX is the representative time series representation method combining segmentation and discretization techniques, which has been successfully applied to the time series classification task. But SAX requires a large number of segments in order to represent the meaningful dynamic patterns of time series accurately, since it loss the dynamic property of time series in the course of smoothing the movement of time series. Therefore, this paper suggests a new time series representation method that combines PIPs detection and Persist discretization techniques. The suggested method represents the dynamic movement of high-diemensional time series in a lower dimensional space by detecting PIPs indicating the important inflection points of time series. And it determines the optimal discretizaton ranges by applying self-transition and marginal probabilities distributions to KL divergence measure. It minimizes the information loss in process of the dimensionality reduction. The suggested method enhances the performance of time series classification task by minimizing the information loss in the course of dimensionality reduction.