• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.133-153
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• 2023

In this paper, we propose a novel approach to investigating brain-signal measurement technology using Electroencephalography (EEG). Traditionally, researchers have combined EEG signals with bio-signals (BSs) to enhance the classification performance of emotional states. Our objective was to explore the synergistic effects of coupling EEG and BSs, and determine whether the combination of EEG+BS improves the classification accuracy of emotional states compared to using EEG alone or combining EEG with pseudo-random signals (PS) generated arbitrarily by random generators. Employing four feature extraction methods, we examined four combinations: EEG alone, EG+BS, EEG+BS+PS, and EEG+PS, utilizing data from two widely-used open datasets. Emotional states (task versus rest states) were classified using Support Vector Machine (SVM) and Long Short-Term Memory (LSTM) classifiers. Our results revealed that when using the highest accuracy SVM-FFT, the average error rates of EEG+BS were 4.7% and 6.5% higher than those of EEG+PS and EEG alone, respectively. We also conducted a thorough analysis of EEG+BS by combining numerous PSs. The error rate of EEG+BS+PS displayed a V-shaped curve, initially decreasing due to the deep double descent phenomenon, followed by an increase attributed to the curse of dimensionality. Consequently, our findings suggest that the combination of EEG+BS may not always yield promising classification performance.

• Journal of Engineering Education Research
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• v.10 no.3
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• pp.64-78
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• 2007

For Korean researchers, English is essential. In fact, this is the case for any researcher who is a non-native English speaker, as recognition and success is predicated on being published, while publications that reach the broadest audiences are in English. Unfortunately, university science and engineering programs in Korea often do not provide formal coursework to help students attain greater competence in English composition. Aggravating this situation is the general lack of literature covering this specific pedagogical issue. While there is plenty of information to help native speakers with technical writing and much covering general English composition for EFL learners, there is very little information available to help EFL learners become better technical writers. Thus, the purpose of this report is twofold. First, as most Korean educators in science and engineering are not well acquainted with pedagogical issues of EFL writing, this report provides a general introduction to some relevant issues. It reviews the importance of contrastive rhetoric as well as some considerations for choosing the appropriate teaching approach, class arrangement, and use of computer assisted learning tools. Secondly, a course proposal is discussed. Based on a review of student writing samples as well as student responses to a self-assessment questionnaire, the proposed course is intended to balance the needs of Korean EFL learners to develop grammar, process, and genre skills involved in technical writing. Although, the scope of this report is very modest, by sharing the considerations made towards the development of an EFL technical writing course it seeks to provide a small example to a field that is perhaps lacking examples.

• The Journal of the Convergence on Culture Technology
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• v.5 no.2
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• pp.195-200
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• 2019

The purpose of this study is to present a model for enhancing the academic excellence of adult college students. For this purpose, 408 adult college students attending 2-year and 4-year colleges in Busan, Daegu, and Gyeongbuk were surveyed and analyzed. The components of the model are curriculum, educational methods, evaluation of education, educational administration, educational environment, and institutional support and the results are as follows. First, the curriculum preferred by adult college students was to acquire diverse academic knowledge for a degree, to acquire knowledge and skills to develop skills for the workplace, and to acquire new information and knowledge regarding issues in society as a whole. Second, the professors' qualification among the educational methods preferred by adult college students was professional competence of the professors based on their theoretical and practical skills. The preferred teaching methods were lecture, discussion, action learning, and the project learning method in that order and video and PowerPoint were preferred as effective teaching mediums. Third, the preferred course for adult college students is operated on weekends, and three years was preferred to get a bachelor's degree. The possible hours of learning per day is 3~6 hours, indicating the necessity of e-learning, B-learning, and prior learning experience recognition systems. Fourth, the education evaluation method preferred by adult college students was a compromise method which is a mixture of absolute evaluation and relative evaluation, and it also showed the need for Pass or Non Pass evaluation method. Fifth, the internal factors of college selection preferred by adult college students were the acquisition of new knowledge and skills, and the external factors were desire to receive many opportunities related to employment and job improvement. The classroom, which provides an effective environment, was a fixed seat classroom and an indoor classroom environment was emphasized for desired educational environment. Sixth, institutional support preferred by adult college students was computer-related programs and learning club support services.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.9 no.3
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• pp.195-201
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• 2015

In this study, IT education contents have been developed based on the u-Learning approach for people with hearing impairment, focusing on allowing the user to learn from anywhere and anytime. Specifically, this study applies HTML5 to implementing IT education contents(JSP, Oracle) for the deaf because HTML5 enables the learner to access the contents through both web and mobile device on various platforms including android, Mac OS, and PC etc. The results of this study are as follows: First, the online computer courses are mostly supposed to be compatible with diverse types of mobile devices. However, some of the contents could not be run on applications residing in web and mobile devices because the contents tend to be developed using FLASH. HTML5 is the effective way to overcome the compatibility problem. Second, FLASH and HTML5 contents authoring tools have been compared in terms of their strong and weak points by applying the developed contents to those tools. The study also suggests that the future work would be needed in order to implement wide variety of event functions with HTML5. Lastly, design strategies enabling access through web and mobile devices have been analyzed in accordance with u-Learning design guidelines for the deaf and mobile application accessibility guidelines. However, in the latter case, the future work regarding design guidelines needs to be conducted to improve the educational accessibility depending on the level of impairment.

• Journal of The Korean Association of Information Education
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• v.23 no.6
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• pp.607-619
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• 2019

As software education was introduced through the 2015 revised curriculum, various research activities have been carried out to improve the computational thinking of learners beyond the existing ICT literacy and software utilization education. With this change, the purpose of this study is to examine the research trends of various research activities related to computational thinking which is emphasized in software education. To this end, we extracted the key words from 190 papers related to computational thinking subject published from January 2014 to September 2019, and conducted frequency analysis, word cloud, connection centrality, and topic modeling analysis on the words. As a result of the topical modeling analysis, we found that the main studies so far have included studies on 'computational thinking education program', 'computational thinking education for pre-service teacher education', 'robot utilization education for computational thinking', 'assessment of computational thinking', and 'computational thinking connected education'. Through this research method, it was possible to grasp the research trend related to computational thinking that has been conducted mainly up to now, and it is possible to know which part of computational thinking education is more important to researchers.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.225-230
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• 2020

This paper highlights the influence and the importance of the syntactic-grammatical knowledge on "the reading machine", appeared in Jackendoff (1999). Due to the lack of the detailed testing and implementation in his research, this paper tests an extensive data array using a component of Google Translate, currently available freely and most widely on the internet. Although outdated, Jackendoff's paper, "Why can't Computers use English?", argues that syntactic-grammatical knowledge plays a key role in the outputs of computers and computer-based reading machines. The current research has implemented some testings of his thought-provoking examples, in order to find out whether Google Translate can handle the same problems after two decades or so. As a result, it is argued that in the field of NLP, I-language in the sense of Chomsky (1986, 1995 etc) is real and the syntactic, grammatical, and categorial knowledge is essential in the faculty of language. Therefore, it is reassured in this paper that when it comes to human language, even the most advanced "machine" is still no match for human faculty of language, the syntactic-grammatical knowledge.

• 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.

• KIPS Transactions on Computer and Communication Systems
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• v.10 no.10
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• pp.285-290
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• 2021

In the medical field, disease diagnosis and prediction research using artificial intelligence technology is being actively conducted. It is being released as a variety of products for disease diagnosis and prediction, which are most widely used in the application of artificial intelligence technology based on medical images. Artificial intelligence is being applied to diagnose diseases, to classify diseases into benign and malignant, and to separate disease regions for use in identification or reading according to the risk of disease. Recently, in connection with cloud technology, its utility as a service product is increasing. Among the diseases dealt with in this paper, liver disease is a disease with very high risk because it is difficult to diagnose early due to the lack of pain. Artificial intelligence technology was introduced based on medical images as a non-invasive diagnostic method for diagnosing these diseases. We describe the development of a web service to help the most meaningful clinical reading of liver cirrhosis patients. Then, it shows the web service process and shows the operation screen of each process and the final result screen. It is expected that the proposed service will be able to diagnose liver cirrhosis at an early stage and help patients recover through rapid treatment.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of The Korean Association of Information Education
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• v.25 no.3
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• pp.459-469
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• 2021

In this paper, a case study of basic data science practice education for field teachers and pre-service teachers was studied. In this paper, for basic data science education, spreadsheet software was used as a data collection and analysis tool. After that, we trained on statistics for data processing, predictive hypothesis, and predictive model verification. In addition, an educational case for collecting and processing thousands of public big data and verifying the population prediction hypothesis and prediction model was proposed. A 34-hour, 17-week curriculum using a spreadsheet tool was presented with the contents of such basic education in data science. As a tool for data collection, processing, and analysis, unlike Python, spreadsheets do not have the burden of learning program- ming languages and data structures, and have the advantage of visually learning theories of processing and anal- ysis of qualitative and quantitative data. As a result of this educational case study, three predictive hypothesis test cases were presented and analyzed. First, quantitative public data were collected to verify the hypothesis of predicting the difference in the mean value for each group of the population. Second, by collecting qualitative public data, the hypothesis of predicting the association within the qualitative data of the population was verified. Third, by collecting quantitative public data, the regression prediction model was verified according to the hypothesis of correlation prediction within the quantitative data of the population. And through the satisfaction analysis of pre-service and field teachers, the effectiveness of this education case in data science education was analyzed.