• Investigative Magnetic Resonance Imaging
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• v.8 no.1
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• pp.9-16
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• 2004

Purpose : The purpose of this study was to assess supplementary motor area (SMA) activation during motor, sensory, word generation, listening comprehension, and working memory tasks using functional magnetic resonance imaging (fMRI). Materials and Methods : Sixteen healthy right-handed subjects (9M, 7F) were imaged on a Siemens 1.5T scanner. Whole brain functional maps were acquired using BOLD EPI sequences in the axial plane. Each paradigm consisted of five epochs of activation vs. the control condition. The activation tasks consisted of left finger complex movement, hot sensory stimulation of the left hand, word generation, listening comprehension, and working memory. The reference function was a boxcar waveform. Activation maps were thresholded at an uncorrected p=0.0001. The thresholded activation maps were placed into MNI space and the anatomic localization of activation within the SMA was compared across tasks. Results : SMA activation was observed in 16 volunteers for the motor task, 11 for the sensory task, 15 for the word generation task, 5 for the listening comprehension task, and 15 for the working memory task. The rostral aspects of the SMA showed activity during the word generation and working memory tasks, and the caudal aspects of the SMA showed activity during the motor and sensory tasks. Right (contralateral) SMA activation was observed during the motor and sensory tasks, and left SMA activation during the word generation and memory tasks. Conclusion : Our results suggest that SMA is involved in a variety of functional tasks including motor, sensory, word generation, and working memory. The results obtained also support the notion that functionally specific subregions exist within the region classically defined as the SMA.

• Investigative Magnetic Resonance Imaging
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• v.13 no.1
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• pp.47-53
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• 2009

Purpose : Although it's been known for half a century that unique structures have evolved in the cerebellum and they then became greatly enlarged in the human brain, the function of these structures still remains unknown. The purpose of this study was to assess cerebellar activation during motor, sensory, word generation, listening comprehension, and working memory tasks with using functional magnetic resonance imaging (fMRI). Materials and Methods : Eleven healthy right-handed subjects (Male: female, 6:5, mean age: 27.4years) were imaged on a Siemens 1.5T scanner. Whole brain functional maps were acquired using BOLD EPI sequences in the axial plane. Each paradigm consisted of five epochs of activation vs. the control condition. The activation tasks consisted of left finger complex movement, sensory stimulation of the left hand, word generation, listening comprehension, and working memory tasks. The reference function was a boxcar waveform. The activation maps were thresholded at p = 0.001. SPM 5 evaluated the activated areas and responses within the cerebellum. Results : Cerebellar activation was observed on motor task, word generation task, and working memory task. There were 949 activated areas and the mean fitted and adjusted response was 0.68 during the motor task. There were 319 activated areas and the mean fitted and adjusted response was 0.15 during the word generation task. There were 330 activated areas and the mean fitted and adjusted response was 0.26 during the working memory task. Conclusion : Our results suggest that the cerebellum is involved in a variety of functional tasks, including motor, word generation, and working memory tasks. However, during the motor task, the cerebellum showed a large activated area and a high response. Cerebellar function can be evaluated by fMRI.

• Journal of KIISE
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• v.45 no.2
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• pp.150-156
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• 2018

Software visualization can assist developers to understand a software system and change its code. The recent development of bottom-up visualization tools demonstrates the advantages by revealing the code that is directly related to a software evolution task. However, the information provided by these tools is limited to the code already investigated by the developers in that task session. To broaden the scope and provide the code information that developers should explore, we propose to present the latest revision of a software system via a class diagram. When a developer clicks on a button, the proposed tool reveals the code changes committed to a configuration management system, and facilitates the understanding of code changes. We also conduct case studies illustrating the advantages of the proposed tool.

• Journal of Korean language education
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• v.28 no.4
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• pp.63-86
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• 2017

This research aims to evaluate the effects and the shortcomings of strategies for teaching paraphrasing to foreign university students. For this study, the effects of teaching paraphrasing skills on student's academic writing was investigated through the assessment of pre- and post-test writing tasks and through a focus group interview. The teaching of paraphrasing was divided into 3 instructional sessions and the focus group interview was conducted at the end of the last session. After each instructional session three trained assessors evaluated the outcomes of pre and post task writings; the in-depth focus group interviews were conducted on seven of the students in order to define better positive effects as well as shortcomings. By comparing the scores of pre- and post-task assignments it is possible to affirm that there is a meaningful difference for overall scores; moreover, in an analysis conducted on each single sub-element of the paraphrasing skill, it emerged that except for "task completion", all other four elements of "structure", "understanding core content", "change of expression" and "academic expressions" showed a statistically significant improvement in performance. On the other hand, as a result of the focus group interview, it was possible to individuate as main shortcomings insufficient vocabulary knowledge, poor overall comprehension of the reference material, lack of practice and difficulty in connect sentences organically. It is possible to consider these elements as limiting factors in understanding the reference material and making correct citations by the students. Therefore, we suggest as supplementary instructional strategies a deeper attention to the difficulty level of the text, the introduction of devices to understand better the content of the reference material and an integrated practice of the paraphrasing skill.

• Speech Sciences
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• v.14 no.4
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• pp.7-24
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• 2007

This paper discusses some current issues regarding how prosodic structure is manifested in fine-grained phonetic details, how prosodically-conditioned articulatory variation is explained in terms of speech dynamics, and how such phonetic manifestation of prosodic structure may be exploited in spoken word recognition. Prosodic structure is phonetically manifested in prosodically important landmark locations such as prosodic domain-final position, domain-initial position and stressed/accented syllables. It will be discussed how each of the prosodic landmarks engenders particular phonetic patterns, ow articulatory variation in such locations are dynamically accounted for, and how prosodically-driven fine-grained phonetic detail is exploited by listeners in speech comprehension.

• Journal of the Korea Safety Management & Science
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• v.14 no.2
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• pp.177-183
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• 2012

Obtaining and applying information is considered as a critical task in the modern informationized society. Finding the one's necessary information and processing it into a detailed knowledge are becoming more priortized in the enormous amount of information. Data modelling is the process that does not only reflect the demands of the user but the one that also facilitates the user's comprehension of the model itself. Ultimately, data modelling fully supports the processes that are requisite for the implementation of a data base and minimizes the alternations of the model during the development of applications.

• Genomics & Informatics
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• v.17 no.2
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• pp.18.1-18.10
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• 2019

Prediction of the relations among drug and other molecular or social entities is the main knowledge discovery pattern for the purpose of drug-related knowledge discovery. Computational approaches have combined the information from different sources and levels for drug-related knowledge discovery, which provides a sophisticated comprehension of the relationship among drugs, targets, diseases, and targeted genes, at the molecular level, or relationships among drugs, usage, side effect, safety, and user preference, at a social level. In this research, previous work from the BioNLP community and matrix or matrix decomposition was reviewed, compared, and concluded, and eventually, the BioNLP open-shared task was introduced as a promising case study representing this area.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.91-98
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• 2023

Construction accidents result from a combination of factors, including both the actions of workers and the safety conditions on site. Despite advancements in enhancing construction site safety, there remains a gap in comprehending the cognitive processes underlying workers' unsafe behavior. This paper investigates and validates a qualitative model that delves into the potential causes of workers' unsafe actions by examining their cognitive processes, employing a system dynamics approach. By analyzing the interplay of various loops within this model, it offers both short- and long-term safety strategies for managers intent on minimizing unsafe behavior among workers. Specifically, safety managers should prioritize increasing workers' awareness of hazards through education and fostering a positive safety mindset. Moreover, they should task frontline supervisors with directly addressing and rectifying instances of unsafe behavior by workers. Lastly, construction safety managers ought to formulate safety strategies that take into account the cognitive states of workers to mitigate any adverse consequences of biased safety management. The outcomes of this research contribute to our comprehension of methods to enhance hazard perception among workers, curtail unsafe actions, and ultimately reduce construction accidents from a cognitive standpoint.

• Korean Journal of Cognitive Science
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• v.23 no.2
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• pp.185-204
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• 2012

A three factor experiment was conducted to explore the effects of giving prompts and asking for self explanation and the modulating effect of the need for cognition (NC) on the two interventions. The effects of giving prompts and asking for self explanation were different depending on the task and the level of need for cognition. Participants of low NC conditions showed the prompt effect and the self explanation effect in the memory tests. On the other hand, participants in the high NC conditions performed better in the comprehension test when they were given prompts and were asked to self explain. However, high NC participants performed worse when they were asked to self explain without providing the prompts. The results were interpreted to suggest the importance of detecting the right parts in getting the self explanation effect.

• Journal of the Korea Society of Computer and Information
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• v.27 no.2
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• pp.15-23
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• 2022

In the field of patents, as NLP(Natural Language Processing) is a challenging task due to the linguistic specificity of patent literature, there is an urgent need to research a language model optimized for Korean patent literature. Recently, in the field of NLP, there have been continuous attempts to establish a pre-trained language model for specific domains to improve performance in various tasks of related fields. Among them, ELECTRA is a pre-trained language model by Google using a new method called RTD(Replaced Token Detection), after BERT, for increasing training efficiency. The purpose of this paper is to propose KorPatELECTRA pre-trained on a large amount of Korean patent literature data. In addition, optimal pre-training was conducted by preprocessing the training corpus according to the characteristics of the patent literature and applying patent vocabulary and tokenizer. In order to confirm the performance, KorPatELECTRA was tested for NER(Named Entity Recognition), MRC(Machine Reading Comprehension), and patent classification tasks using actual patent data, and the most excellent performance was verified in all the three tasks compared to comparative general-purpose language models.