• The Journal of Korean Academy of Sensory Integration
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• v.17 no.2
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• pp.56-68
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• 2019

Objective : The purpose of this study is to provide evidence for a systematic analysis of occupational therapy interventions for the activity and participation of children and adolescents with developmental disabilities. Methods : The articles used in this study were collected from the RISS, KISS, and DBpia databases. The key words used were "children and occupational therapy" "children and sensory integration," "adolescent and occupational therapy," "adolescent and sensory integration," "developmental disorder and occupational therapy," and "developmental disorder and sensory integration." The research period was limited to January 2008 to August 2018. Seven articles in total were selected for systematic analysis. Results : Most of the included works were single-case studies, and most subjects dealt with the autism spectrum disorder. The majority of the interventions used involved sensory integration. Occupational therapy interventions were self-care (33%), major life area (33%), learning and application (11%), communication (11%), and mobility (11%). Conclusion : This study will help with understanding the current state of occupational therapy interventions for the activity and participation. On the basis of this understanding, various studies on this subject are expected to be conducted in the future.

• Journal of The Korean Association For Science Education
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• v.41 no.6
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• pp.519-531
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• 2021

This study investigates the science teacher identity of pre-service science teachers (PSTs) in the context of a teaching practice course. Twenty-two PSTs who took the 'Biological Science Lab. for Inquiry Learning' course at the College of Education participated in this study. Artifacts created during the course were collected, and the teaching practices and reflections were recorded and transcribed. In addition, semi-structured interviews were conducted with nine PSTs, recorded, and transcribed. We found the science teacher identity was not well revealed at the beginning of the course. Authoritative discourse appeared in the early oral reflections of PSTs, indicating that the PSTs perceived oral reflection activities as 'evaluation activities for teaching practice'. This perception shows that pre-service teachers participate in teaching practice courses as students attending a university, performing tasks and receiving evaluations from instructors. After the middle of the course, discourses showing the science teacher identity of the PSTs were observed. In the oral reflection after the middle part, dialogic discourses often arose, showing that the PSTs perceive the oral reflection activities as a 'learning activity for professional development'. In addition, in the second half, discourse appeared to connect and interpret one's experience with the teacher's activity, indicating that the PSTs perceive themselves as teachers at this stage. In addition, the perception of experimental classes was expanded through the course. During the course, the practice of equalizing the authority of the participants, providing a role model for reflection, and experiencing various positions from multiple viewpoints in the class had a positive effect on the formation and continuation of the teacher identity. This study provides implications on the teacher education process for teacher identity formation in PSTs.

• Journal of Internet Computing and Services
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• v.22 no.2
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• pp.77-87
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• 2021

With the development of the Internet and personal computers, various and complex attacks begin to emerge. As the attacks become more complex, signature-based detection become difficult. It leads to the research on behavior-based log anomaly detection. Recent work utilizes deep learning to learn the order and it shows good performance. Despite its good performance, it does not provide any explanation for prediction. The lack of explanation can occur difficulty of finding contamination of data or the vulnerability of the model itself. As a result, the users lose their reliability of the model. To address this problem, this work proposes an explainable log anomaly detection system. In this study, log parsing is the first to proceed. Afterward, sequential rules are extracted by Bayesian posterior probability. As a result, the "If condition then results, post-probability" type rule set is extracted. If the sample is matched to the ruleset, it is normal, otherwise, it is an anomaly. We utilize HDFS datasets for the experiment, resulting in F1score 92.7% in test dataset.

• Journal of the Korea Society of Computer and Information
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• v.26 no.12
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• pp.53-59
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• 2021

In this paper, we propose a BERGPT-chatbot, a domestic AI chatbot that can alleviate negative emotions based on text input such as 'Replika'. We made BERGPT-chatbot into a chatbot capable of mitigating negative emotions by pipelined two models, KR-BERT and KoGPT2-chatbot. We applied a creative method of giving emotions to unrefined everyday datasets through KR-BERT, and learning additional datasets through KoGPT2-chatbot. The development background of BERGPT-chatbot is as follows. Currently, the number of people with depression is increasing all over the world. This phenomenon is emerging as a more serious problem due to COVID-19, which causes people to increase long-term indoor living or limit interpersonal relationships. Overseas artificial intelligence chatbots aimed at relieving negative emotions or taking care of mental health care, have increased in use due to the pandemic. In Korea, Psychological diagnosis chatbots similar to those of overseas cases are being operated. However, as the domestic chatbot is a system that outputs a button-based answer rather than a text input-based answer, when compared to overseas chatbots, domestic chatbots remain at a low level of diagnosing human psychology. Therefore, we proposed a chatbot that helps mitigating negative emotions through BERGPT-chatbot. Finally, we compared BERGPT-chatbot and KoGPT2-chatbot through 'Perplexity', an internal evaluation metric for evaluating language models, and showed the superity of BERGPT-chatbot.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.19 no.3
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• pp.9-15
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• 2019

Large global online shopping malls, such as Amazon, offer services in English or in the language of a country when their products are sold. Since many customers purchase products based on the product reviews, the shopping malls actively utilize the sentimental analysis technique in judging preference of each product using the large amount of review data that the customer has written. And the result of such analysis can be used for the marketing to look the potential shoppers. However, it is difficult to apply this English-based semantic analysis system to different languages used around the world. In this study, more than 500,000 data from Amazon fine food reviews was used for training a deep learning based system. First, sentiment analysis evaluation experiments were carried out with three models of English test data. Secondly, the same data was translated into seven languages (Korean, Japanese, Chinese, Vietnamese, French, German and English) and then the similar experiments were done. The result suggests that although the accuracy of the sentimental analysis was 2.77% lower than the average of the seven countries (91.59%) compared to the English (94.35%), it is believed that the results of the experiment can be used for practical applications.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.85-95
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• 2019

Recently, studies on the fourth industrial revolution have been actively conducted in the areas of government, business, and academia. Corporate business models that utilize the major agendas of the fourth industrial revolution such as robots, artificial intelligence, Internet of things (IoT), and block chains have been created, and various changes have occurred in not only business, education, and living environments but also in international relations. In this study, we looked at changes in social contribution activities from the perspective of a company facing impacts of the fourth industrial revolution. This study examines the definition and activities of corporate social contribution and how we can contribute to society through corporate activities. 'AT Educom', LG Uplus 'Social Contribution through IoT', KT's anti-infectious disease prevention platform and cases of Intel using IoT. In addition, we have presented what we need to do in the future to promote corporate social contribution activities that will make more meaningful impacts on how corporate social contribution activities will change according to technology development. The first, measuring the performance of corporate social contribution activities needs a standardized methodology and social contribution activities through platform business and ICT should be actively pursued. Lastly, social contribution activities between companies and sectors will increase.

• Journal of IKEEE
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• v.22 no.4
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• pp.1140-1146
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• 2018

Recently, a methodology for analyzing complex bio-signals using a deep learning model has emerged among studies that recognize human emotions. At this time, the accuracy of emotion classification may be changed depending on the evaluation method and reliability depending on the kind of data to be learned. In the case of biological signals, the reliability of data is determined according to the noise ratio, so that the noise detection method is as important as that. Also, according to the methodology for defining emotions, appropriate emotional evaluation methods will be needed. In this paper, we propose a wavelet -based noise threshold setting algorithm for verifying the reliability of data for multimodal bio-signal data labeled Valence and Arousal and a method for improving the emotion recognition rate by weighting the evaluation data. After extracting the wavelet component of the signal using the wavelet transform, the distortion and kurtosis of the component are obtained, the noise is detected at the threshold calculated by the hampel identifier, and the training data is selected considering the noise ratio of the original signal. In addition, weighting is applied to the overall evaluation of the emotion recognition rate using the euclidean distance from the median value of the Valence-Arousal plane when classifying emotional data. To verify the proposed algorithm, we use ASCERTAIN data set to observe the degree of emotion recognition rate improvement.

• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.105-120
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• 2019

The World Health Organization (WHO) and other government agencies aroundthe world have warned against antibiotic-resistant bacteria due to abuse of antibiotics and are strengthening their care and monitoring to prevent infection. However, it is highly necessary to develop an expeditious and accurate prediction and estimating method for preemptive measures. Because it takes several days to cultivate the infecting bacteria to identify the infection, quarantine and contact are not effective to prevent spread of infection. In this study, the disease diagnosis and antibiotic prescriptions included in Electronic Health Records were embedded through neural embedding model and matrix factorization, and deep learning based classification predictive model was proposed. The f1-score of the deep learning model increased from 0.525 to 0.617when embedding information on disease and antibiotics, which are the main causes of antibiotic resistance, added to the patient's basic information and hospital use information. And deep learning model outperformed the traditional machine hospital use information. And deep learning model outperformed the traditional machine learning models.As a result of analyzing the characteristics of antibiotic resistant patients, resistant patients were more likely to use antibiotics in J01 than nonresistant patients who were diagnosed with the same diseases and were prescribed 6.3 times more than DDD.

• Journal of Digital Convergence
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• v.17 no.1
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• pp.239-247
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• 2019

An accurate prediction of emotion is a very important issue for the sake of patient-centered medical device development and emotion-related psychology fields. Although there have been many studies on emotion prediction, no studies have applied the heart rate variability and neuro-fuzzy approach to emotion prediction. We propose ANFEP(Adaptive Neuro Fuzzy System for Emotion Prediction) HRV. The ANFEP bases its core functions on an ANFIS(Adaptive Neuro-Fuzzy Inference System) which integrates neural networks with fuzzy systems as a vehicle for training predictive models. To prove the proposed model, 50 participants were invited to join the experiment and Heart rate variability was obtained and used to input the ANFEP model. The ANFEP model with STDRR and RMSSD as inputs and two membership functions per input variable showed the best results. The result out of applying the ANFEP to the HRV metrics proved to be significantly robust when compared with benchmarking methods like linear regression, support vector regression, neural network, and random forest. The results show that reliable prediction of emotion is possible with less input and it is necessary to develop a more accurate and reliable emotion recognition system.

• Communications of Mathematical Education
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• v.35 no.1
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• pp.97-118
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• 2021

Since the characteristics of teachers that affect mathematics academic achievement are constantly changing and affecting mathematics achievement, longitudinal studies that can predict and analyze growth are needed. This study used data from middle and high school students from 2013(first year of middle school) to 2017(second year of high school) of the Seoul Education Longitudibal Study(SELS). By classifying the longitudinal changes in mathematics academic achievement into similar subgroups, the direct influence of teachers' characteristics(professionalism, expectations, academic feedback) perceived by students on the longitudinal changes in mathematics academic achievement was examined. As a result of the study, it was found that the characteristics of mathematics teachers(professional performance, expectation, and academic feedback) in group 1(343 students), which included the top 14.5% of students, did not directly affect longitudinal changes in mathematics academic achievement. Students in the middle 2nd group(745, 32.2%) had academic feedback from the mathematics teacher, and the 2nd group(1225 students) in the lower 53%, which included most of the students, showed that the expectations of the mathematics teacher were the longitudinal mathematics achievement. The change has been shown to have a direct effect. This suggests that support for teaching and learning should also reflect this, as the direct influence of teachers' professionalism, expectations, and academic feedback on longitudinal changes in mathematics academic achievement is different according to the characteristics and dispositions of students.