• Journal of Korean society of Dental Hygiene
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• v.18 no.3
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• pp.385-397
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• 2018

Objectives: The study was a cross-sectional research to examine dental hygiene students' multi-cultural experiences, emotional intelligence and cultural competency and to understand the correlations among them. Methods: The study was conducted from September $1^{st}$ to October $31^{st}$ 2016, based on the survey of 449 students in the department of dental hygiene at 7 Universities. The questionnaire consisted of 57 questions including general characteristics (n=7), multi-cultural experiences (n=7), emotional intelligence (n=16) and cultural competency (n=27). Results: Each score of students' emotional intelligence and cultural competency is 3.43 and 3.01 respectively in 5-point scale. An analysis of correlations between emotional intelligence and cultural competency shows that the higher the emotional intelligence, the higher the cultural competency (r=0.342). The factors affecting the cultural competency include use of emotions (${\beta}=0.327$, p<0.001), control of emotions (${\beta}=0.254$, p=0.001), frequency of multi-cultural media (${\beta}=0.221$, p<0.001) and experience of multi-cultural class (${\beta}=0.221$, p=0.002). The modified explanatory power in this model is 28.2% (F=10.856, p<0.001). Conclusions: There was a positive correlation between emotional intelligence and cultural competency, and the contacts with multi-culture and experience of class are identified as the affecting factors. Dental hygiene students should acquire theoretical experiences regarding the multi-culture through curriculum or continuous educations and it is necessary to promote such educations in order to develop and apply the programs for the enhancement of emotional intelligence.

• ETRI Journal
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• v.46 no.2
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• pp.277-289
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• 2024

In this paper, we describe a neural network-based application that recommends multiple items using dialog context input and simultaneously outputs a response sentence. Further, we describe a multi-item recommendation by specifying it as a set of clothing recommendations. For this, a multimodal fusion approach that can process both cloth-related text and images is required. We also examine achieving the requirements of downstream models using a pretrained language model. Moreover, we propose a gate-based multimodal fusion and multiprompt learning based on a pretrained language model. Specifically, we propose an automatic evaluation technique to solve the one-to-many mapping problem of multi-item recommendations. A fashion-domain multimodal dataset based on Koreans is constructed and tested. Various experimental environment settings are verified using an automatic evaluation method. The results show that our proposed method can be used to obtain confidence scores for multi-item recommendation results, which is different from traditional accuracy evaluation.

• East Asian mathematical journal
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• v.37 no.4
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• pp.381-400
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• 2021

In this study, we compare the multiple intelligence of gifted students with the multiple intelligence of their children recognized by their parents. The subjects of the study are 118 students and their parents at the gifted education center affiliated with A University. First of all, there is a difference between the multiple intelligence of gifted students and the multiple intelligence recognized by their parents. Parents are highly regarded their children in all multiple intelligence components. Second, there are differences in multiple intelligence of gifted students' gender. The difference in multiple intelligence of children recognized by parents depending on the gender of the student was similar to the student's results. Third, there are not much difference in multiple intelligence between elementary and middle school students. However, there is a big difference between students and parents in the elementary school group compared to the middle school students. Therefore, since multi-intelligence can be developed by individual experience and environment throughout one's life, an educational environment that reflects objective evaluation and student needs rather than parental subjective judgment should be created.

• Journal of the Korean Society of Radiology
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• v.83 no.6
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• pp.1208-1218
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• 2022

In the medical field, artificial intelligence has been used in various ways with many developments. However, most artificial intelligence technologies are developed so that one model can perform only one task, which is a limitation in designing the complex reading process of doctors with artificial intelligence. Multi-task learning is an optimal way to overcome the limitations of single-task learning methods. Multi-task learning can create a model that is efficient and advantageous for generalization by simultaneously integrating various tasks into one model. This study investigated the concepts, types, and similar concepts as multi-task learning, and examined the status and future possibilities of multi-task learning in the medical research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2328-2344
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• 2022

2D human pose estimation still faces difficulty in low-resolution images. Most existing top-down approaches scale up the target human bonding box images to the large size and insert the scaled image into the network. Due to up-sampling, artifacts occur in the low-resolution target images, and the degraded images adversely affect the accurate estimation of the joint positions. To address this issue, we propose a multi-resolution input feature fusion network for human pose estimation. Specifically, the bounding box image of the target human is rescaled to multiple input images of various sizes, and the features extracted from the multiple images are fused in the network. Moreover, we introduce a guiding channel which induces the multi-resolution input features to alternatively affect the network according to the resolution of the target image. We conduct experiments on MS COCO dataset which is a representative dataset for 2D human pose estimation, where our method achieves superior performance compared to the strong baseline HRNet and the previous state-of-the-art methods.

• 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.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.07a
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• pp.67-68
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• 2023

본 논문에서 제안한 Multi Agent Multi Action은 기존의 대화형 시스템 방식인 Single Agent Single Action 구조에 비해 확장성을 갖춘 대화 시스템을 구현하는 방식이다. 시스템을 여러 에이전트로 분할하고, 각 에이전트가 특정 액션에 대한 처리를 담당함으로써 보다 유연하고 효율적인 대화형 시스템을 구현할 수 있으며, 다양한 작업에 특화된 에이전트를 그룹화함으로써 작업의 효율성을 극대화하고, 사용자 경험을 향상 시킬 수 있다.

• ETRI Journal
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• v.46 no.1
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• pp.82-95
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• 2024

As automated essay scoring (AES) has progressed from handcrafted techniques to deep learning, holistic scoring capabilities have merged. However, specific trait assessment remains a challenge because of the limited depth of earlier methods in modeling dual assessments for holistic and multi-trait tasks. To overcome this challenge, we explore providing comprehensive feedback while modeling the interconnections between holistic and trait representations. We introduce the DualBERT-Trans-CNN model, which combines transformer-based representations with a novel dual-scale bidirectional encoder representations from transformers (BERT) encoding approach at the document-level. By explicitly leveraging multi-trait representations in a multi-task learning (MTL) framework, our DualBERT-Trans-CNN emphasizes the interrelation between holistic and trait-based score predictions, aiming for improved accuracy. For validation, we conducted extensive tests on the ASAP++ and TOEFL11 datasets. Against models of the same MTL setting, ours showed a 2.0% increase in its holistic score. Additionally, compared with single-task learning (STL) models, ours demonstrated a 3.6% enhancement in average multi-trait performance on the ASAP++ dataset.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.73-77
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• 2001

In this paper, we propose a virtual action system based on information agents, We call the system the MultiHammer, MultiHammer can be used for studying and analyzing online actions. MuiltiHammer provides functions of implement-ing a meta online action site and an experiment environ-ment. We have been using MultiHammer as an experiment as an experiment environment for BiddinBot. BiddingBot aims at assisting users to bid simultaneously in multiple online auctions. In order to bid simultaneously in multiple online auctions. In order to analyze the behavior of BiddngBot, we need to pur-chase a lot of items. It is hard for us to prepare a lot of fund to show usability and advantage of BiddingBot. MultiHam-mer enables us to effectively analyze the behavior of BiddingBot. MultiHammer consists of three types of agents for information collecting data storing and auctioning. Agents for information wrappers. To make agent work as wrarp-pers, we heed to realize software modules for each online action site. Implementing these modules reguires a lot of time and patience. To address this problem, we designed a support mechanism for developing the modules. Agents for data storing record the data gathered by agents for informa-tion collecting. Agents for auctioning provide online services using data recorded by agents for data storing. By recording the activities in auction sites. MultiHammer can recreate any situation and trace auction for experimentation, Users can participate in virtual using the same information in real online auctions. Users also participate in real auc-tions via wrapper agents for information collecting

• The Journal of Korean Institute of Communications and Information Sciences
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• v.37C no.10
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• pp.965-976
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• 2012

Multi-Platform Image and Intelligence Common data link(MPI-CDL) systems are designed to transmit the imaginary and signal intelligence data at an aeronautical to ground line of sight(LOS) link. This paper proposes a method to predict a link availability and analyzes the required link margin to satisfy a given link availability for MPI-CDL systems. To estimate a link availability the proposed method applies the conditional probability so that both a rain attenuation and a multipath fading are considered simultaneously. Link margins to meet the link availability for MPI-CDL systems are calculated according to an operating environment including frequencies, flight altitudes and transmission ranges. The required link margins for actual unmanned air vehicle systems are also given by simulation results.