• International journal of advanced smart convergence
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• v.12 no.2
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• pp.56-66
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• 2023

The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user's sensitive personal information, such as users' images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.

• Korean Journal of Child Studies
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• v.28 no.5
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• pp.175-191
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• 2007

This study selected 113 gifted underachievers and 128 gifted achievers from 17 elementary schools to examine gifted children's personal characteristics(self-concept, locus of control, and learning habits) that have an effect on underachievement. Self-concept(general self-concept and academic self-concept), locus of control, and learning habits(endurance, learning strategy, and learning motivation) variables were analyzed to determine gifted underachievers' personal characteristics. (1) Comparison of personal characteristics of gifted achievers with gifted underachievers indicated gifted underachievers had low self-concept, external locus to control, and problems in learning habits. (2) The sub factors of habits of learning motivation and learning strategy had the greatest effect on underachievement of gifted children.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1001-1007
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• 2020

In recent years, machine learning algorithms are continuously being used and expanded in various fields, such as facial recognition, signal processing, personal authentication, and stock prediction. In particular, various algorithms, such as deep learning, reinforcement learning, and Q-learning, are continuously being improved. Among these algorithms, the expansion of deep learning is rapidly changing. Nevertheless, machine learning algorithms have not yet been applied in several fields, such as personal authentication technology. This technology is an essential tool in the digital information era, walking recognition technology as promising biometrics, and technology for solving state-space problems. Therefore, algorithm technologies of deep learning, reinforcement learning, and Q-learning, which are typical machine learning algorithms in various fields, such as agricultural technology, personal authentication, wireless network, game, biometric recognition, and image recognition, are being improved and expanded in this paper.

• Korean Journal of Child Studies
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• v.28 no.5
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• pp.253-267
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• 2007

This study examined the relationships between multiple intelligences as cognitive factors and affective factors of learning motivation and academic self-concept. The data were collected from 276 4th grade elementary school students and analyzed by correlation, multi-variate analysis, and step-wise multiple regression. Results were that (1) multiple intelligences, learning motivation, and academic self-concept had statistically significant correlations among themselves. Multi-variate analysis showed that intra-personal intelligence explained 58.6% of the linear combination of learning motivation and academic self-concept. (2) Intra-personal intelligence explained 29% to 58% of learning motivation and its sub-factors of achievement motivation, internal locus of control, self-efficacy, and self-regulation. (3) Intra-personal intelligence, logical-mathematical intelligence, musical intelligence, and inter-personal intelligence were explanatory variables for academic self-concept and its sub-factors.

• Korean Medical Education Review
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• v.23 no.1
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• pp.11-22
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• 2021

This review of the literature explored the experiences and effects of peer-assisted learning in basic medical education. Peer-assisted learning is most commonly utilized to teach clinical skills (including technical skills) and medical knowledge (76.4%). It has also been used, albeit less frequently, to facilitate small-group discussions including problem-based learning, to promote students' personal and professional development, to provide mentoring for career development and adaptation to school, to give tutoring to at-risk students, and to implement work-based learning in clinical settings. Near-peer learning is a common type. The use of active learning techniques and digital technology has been increasingly reported. Students' leadership had frequently been described. Student tutor training, programs for teaching skills, institutional support, and assessments have been conducted for effective peer-assisted learning. There is considerable positive evidence that peer-assisted learning is effective in teaching simple clinical skills and medical knowledge for tutees. However, its effects on complex skills and knowledge, small-group discussions, personal and professional development, peer mentoring, and work-based learning have rarely been studied. Additionally, little evidence exists regarding whether peer-assisted learning is effective for student tutors. Further research is needed to develop peer-assisted learning programs and to investigate their learning effects on student tutors, small-group discussion facilitation, personal and professional development, peer mentoring, and peer-led work-based learning in the clinical setting in South Korea. Formal programs and system advancement for a student-led learning culture is needed for effective peer-assisted learning.

• Journal of Information Processing Systems
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• v.16 no.5
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• pp.1034-1047
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• 2020

The personal authentication technique is an essential tool in this complex and modern digital information society. Traditionally, the most general mechanism of personal authentication was using alphanumeric passwords. However, passwords that are hard to guess or to break, are often hard to remember. There are demands for a technology capable of replacing the text-based password system. Graphical passwords can be an alternative, but it is vulnerable to shoulder-surfing attacks. This paper looks through a number of recently developed graphical password systems and introduces a personal authentication system using a machine learning technique with electroencephalography (EEG) signals as a new type of personal authentication system which is easier for a person to use and more difficult for others to steal than other preexisting authentication systems.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.19-25
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• 2024

This paper proposes an artificial intelligence-based personal information area object detection optimization method in an embedded system to de-identify personal information in video data. As an object detection optimization method, first, in order to increase the detection rate for personal information areas when detecting objects, a gyro sensor is used to collect the shooting angle of the image data when acquiring the image, and the image data is converted into a horizontal image through the collected shooting angle. Based on this, each learning model was created according to changes in the size of the image resolution of the learning data and changes in the learning method of the learning engine, and the effectiveness of the optimal learning model was selected and evaluated through an experimental method. As a de-identification method, a shuffling-based masking method was used, and double-key-based encryption of the masking information was used to prevent restoration by others. In order to reuse the original image, the original image could be restored through a security key. Through this, we were able to secure security for high personal information areas and improve usability through original image restoration. The research results of this paper are expected to contribute to industrial use of data without personal information leakage and to reducing the cost of personal information protection in industrial fields using video through de-identification of personal information areas included in video data.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.9-21
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• 2008

In theory, within ubiquitous learning environment, education is happening all around the student but the student may not even be conscious of the learning process. But, in this study, we consider the ubiquitous learning environment where PMP (Personal Multimedia Player) is being used for delivering the learning-contents. And the purpose of this study is to identify the problematic factors recognized by PMP users in u-learning environment. A need analysis was used to identify and evaluate needs of 74 PMP users as well non-users. Based on the interviews of participants, we give a brief summary results on motivation for using PMP, purchasing and operating costs, category of contents being used, dissatisfying and problematic factors with using PMP in u-learning environment. Therefore, we present six categories of factors preventing the u-learning with PMP from being diffused such as lacks of awareness, loss of confidence, side-effects, lacks of education and support, costs, and lacks of contents. In conclusion, we suggest a set of guidelines which might help remove these the resistance factors.

• Journal of Korean Elementary Science Education
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• v.25 no.1
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• pp.8-14
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• 2006

This study examined how pre-service elementary teachers' perceptions regarding the learning environment (learning focus/ability-meritocracy/cooperative climate) and achievement goals (mastery/performance-approach/performance-avoidance) in science education courses jointly contributed to their science teaching efficacy beliefs (personal science teaching efficacy belief/science teaching outcome expectancy). A path analysis supported a causal model in which the perception of the learning focus influenced the mastery goal, which in turn influenced the personal science teaching efficacy belief and science teaching outcome expectancy. The perception of learning focus also had a direct effect on science teaching outcome expectancy. The perception of ability-meritocracy influenced personal science teaching efficacy belief via the performance-approach (positively) or, conversely, the performance-avoidance goal (negatively). No link .was deduced from the perception of cooperative climate. The educational implications of these findings were also discussed.

• Korean Journal of Construction Engineering and Management
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• v.24 no.6
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• pp.81-90
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• 2023

Climate change has resulted in increased frequency and intensity of heat waves, which poses a significant threat to the health and safety of construction workers, particularly those engaged in labor-intensive and heat-stress vulnerable working environments. To address this challenge, this study aimed to propose an interpretable machine learning approach for forecasting personal heat strain by considering the cumulative effect of heat exposure as a situational variable, which has not been taken into account in the existing approach. As a result, the proposed model, which incorporated the cumulative working time along with environmental and personal variables, was found to have superior forecast performance and explanatory power. Specifically, the proposed Multi-Layer Perceptron (MLP) model achieved a Mean Absolute Error (MAE) of 0.034 (℃) and an R-squared of 99.3% (0.933). Feature importance analysis revealed that the cumulative working time, as a situational variable, had the most significant impact on personal heat strain. These findings highlight the importance of systematic management of personal heat strain at construction sites by comprehensively considering the cumulative working time as a situational variable as well as environmental and personal variables. This study provided a valuable contribution to the construction industry by offering a reliable and accurate heat strain forecasting model, enhancing the health and safety of construction workers.