Article Per Year (5 Year)
p-Index From 2021 - 2026
0.778
P-Index
This Author published in this journals
All Journal JOIV : International Journal on Informatics Visualization
Cho, Jungwon
Unknown Affiliation
Computer Science & IT

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search
Journal : JOIV : International Journal on Informatics Visualization

 1 
Analysis of AI Ethical Competence to Computational Thinking Bae, Jinah; Lee, Junghun; Cho, Jungwon
JOIV : International Journal on Informatics Visualization Vol 6, No 2-2 (2022): A New Frontier in Informatics
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.2-2.1126

Abstract

Artificial Intelligence (AI) is a driving force leading the intelligent information society. Major advanced countries have established AI into key policy projects and made continuous efforts to nurture and develop future talents through AI education. Unlike conventional software, AI can infer results through training with data, and if there is a data bias, it may cause social and ethical problems. These problems incur extensive damage to society, so ethical consideration is essential in terms of effectiveness and efficiency in implementing AI. Computational thinking aims to perform effective and efficient problem-solving to address real-life problems using computing technology such as AI. Therefore, ethical considerations in AI education can be regarded as an important element of computational thinking. This study aims to analyze the relationship between computational thinking and AI ethical competence from problem-solving using AI. To this end, evaluations and analyses of computational thinking and AI ethical competence were performed based on the evaluation results of the education program with the integration of AI and AI ethics. The analysis demonstrated that the group with relatively high computational thinking skills also showed high AI ethical competence. The findings of this study are expected to facilitate research on nurturing computational thinking through AI-integrated education with sufficient consideration of AI ethics. To increase the effectiveness of the AI-integrated education program, it is necessary to develop a mid-to-long-term education program to systematically examine the process-focused evaluation by systematizing observational and portfolio assessments.
Real-time Estimation of Road Surfaces using Fast Monocular Depth Estimation and Normal Vector Clustering Yi, Chuho; Cho, Jungwon
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.641

Abstract

Estimating a road surface or planes for applying AR(Augmented Reality) or an autonomous vehicle using a camera requires significant computation. Vision sensors have lower accuracy in distance measurement than other types of sensor, and have the difficulty that additional algorithms for estimating data must be included. However, using a camera has the advantage of being able to extract various information such as weather conditions, sign information, and road markings that are difficult to measure with other sensors. Various methods differing in sensor type and configuration have been applied. Many of the existing studies had generally researched by performing the depth estimation after the feature extraction. However, recent studies have suggested using deep learning to skip multiple processes and use a single DNN(Deep Neural Network). Also, a method using a limited single camera instead of a method using a plurality of sensors has been proposed. This paper presents a single-camera method that performs quickly and efficiently by employing a DNN to extract distance information using a single camera, and proposes a modified method for using a depth map to obtain real-time surface characteristics. First, a DNN is used to estimate the depth map, and then for quick operation, normal vector that can connect similar planes to depth is calculated, and a clustering method that can be connected is provided. An experiment is used to show the validity of our method, and to evaluate the calculation time.
Computational Thinking Evaluation Tool Development for Early Childhood Software Education Lee, Kyunghee; Cho, Jungwon
JOIV : International Journal on Informatics Visualization Vol 5, No 3 (2021)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.5.3.672

Abstract

The early childhood software education is being actively conducted, but research on evaluation of computational thinking is in its infancy. The purpose of early childhood software education is to cultivate the computational thinking through activities centered on solving problems in everyday life. Evaluation in software education is very important in that it not only measures computational thinking simply but also improves computational thinking through evaluation. As such, guidelines for evaluating computational thinking that can be used in early childhood software education are needed, but they are very lacking. Therefore, in this study, the researcher developed an evaluation tool that can meet the ultimate purpose of software education, cultivating computational thinking. The developed evaluation tools are a software education effectiveness test tool and a computational thinking test tool. They were developed to the level of development and interaction of the early childhood. The developed evaluation tool has been validated by software experts, early childhood education experts, and early childhood teachers. As a result of the second step validity verification, all content validity was confirmed. Through this, it was confirmed that the evaluation tool developed in this study can be used as a tool for evaluating computational thinking. This study provides implications for evaluation of computational thinking for early childhood software education. In addition, it is meaningful that it has been suggested to be effectively used for proper evaluation in early childhood software education.
Malware Authorship Attribution Model using Runtime Modules based on Automated Analysis Lee, Sangwoo; Cho, Jungwon
JOIV : International Journal on Informatics Visualization Vol 6, No 1-2 (2022): Data Visualization, Modeling, and Representation
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.6.1-2.941

Abstract

Malware authorship attribution is a research field that identifies the author of malware by extracting and analyzing features that relate the authors from the source code or binary code of malware. Currently, it is being used as one of the detection techniques based on malware forensics or identifying patterns of continuous attacks such as APT attacks. The analysis methods to identify the author are as follows. One is a source code-based analysis method that extracts features from the source code, and the other is a binary-based analysis method that extracts features from the binary. However, to handle the modularization and the increasing amount of malicious code with these methods, both time and manpower are insufficient to figure out the characteristics of the malware. Therefore, we propose the model for malware authorship attribution by rapidly extracting and analyzing features using automated analysis. Automated analysis uses a tool and can be analyzed through a file of malware and the specific hash values without experts. Furthermore, it is the fastest to figure out among other malware analysis methods. We have experimented by applying various machine learning classification algorithms to six malware author groups, and Runtime Modules and Kernel32.dll API extracted from the automated analysis were selected as features for author identification. The result shows more high accuracy than the previous studies. By using the automated analysis, it extracts features of malware faster than source code and binary-based analysis methods.
Co-Authors Bae, Jinah Lee, Junghun Lee, Kyunghee Lee, Sangwoo Yi, Chuho