• Journal of Practical Engineering Education
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• v.12 no.2
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• pp.265-273
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• 2020

In this paper, to design a basic data science practice curriculum as a liberal arts subject for non-majors, we proposed an educational method using an Excel(spreadsheet) data analysis tool. Tools for data collection, data processing, and data analysis include Excel, R, Python, and Structured Query Language (SQL). When it comes to practicing data science, R, Python and SQL need to understand programming languages and data structures together. On the other hand, the Excel tool is a data analysis tool familiar to the general public, and it does not have the burden of learning a programming language. And if you practice basic data science practice with Excel, you have the advantage of being able to concentrate on acquiring data science content. In this paper, a basic data science practice curriculum for one semester and weekly Excel practice contents were proposed. And, to demonstrate the substance of the educational content, examples of Linear Regression Analysis were presented using Excel data analysis tools.

• The Journal of Information Systems
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• v.28 no.4
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• pp.403-424
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• 2019

Purpose The objective of this study is to determine the programming language for improving algorithmic thinking of basic software education for non-majors, which has recently been receiving attention to nurture talents needed in the era of the Fourth Industrial Revolution. Design/methodology/approach In this study, Delphi method was used to select the suitable programming language for the features of each of five departments for basic software education for non-majors in order to develop the capability of algorithmic thinking. The survey was conducted three times to 21 experts, and the results were analyzed using quantitative analysis (CVR) values and stability. Findings For the most suitable programming language for each department determined in this study, App Inventor was selected for humanities department, RUR-PLE for natural science department, App Inventor for social science department, Python for engineering department, and Scratch for fine arts department. This is expected to be used as the basis for determining the direction of curriculum and operation of universities starting basic software education through programming language by department proposed in this study.

• Annual Conference on Human and Language Technology
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• 2014.10a
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• pp.133-136
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• 2014

파이썬은 간결한 아름다움을 추구하는 동시에 강력한 스트링 연산이 가능한 언어다. KoNLPy는 그러한 특장점을 살려, 파이썬으로 한국어 정보처리를 할 수 있게 하는 패키지이다. 꼬꼬마, 한나눔, MeCab-ko 등 국내외에서 개발된 여러 형태소 분석기를 포함하고, 자연어처리에 필요한 각종 사전, 말뭉치, 도구 및 다양한 튜토리얼을 포함하여 누구나 손쉽게 한국어 분석을 할 수 있도록 만들었다.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.73-78
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• 2018

Modern society has evolved to such an extent that computing technology has become an integral part of various fields, creating new and superior value to society. Education on computer literacy, including the ability to design and build software, is now becoming a universal education that must be acquired by everyone, regardless of the field of study. Many universities are imparting software education to students to improve their problem-solving ability, including to students who are not majoring in computers. However, software education contains courses that are meant for computer majors and many students encounter difficulty in learning the grammar of programming language. To solve this problem, this paper analyzes the research outcomes of the existing software education model and proposes a Python-based software education model for students who are not majoring in computer science. Along with a Python-based software education model, this paper proposed a curriculum that can be applied during one semester, including learning procedures, and teaching strategies. This curriculum was applied to a liberal arts class and a meaningful result was derived. If the proposed software education model is applied, the students will be interested in the computer literacy class and improve their computational thinking and problem-solving ability.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.369-376
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• 2021

One of the most active areas of research in the field of affective computing and signal processing is emotion recognition. This paper proposes emotion recognition of low-resource (Sindhi) language. This work's uniqueness is that it examines the emotions of languages for which there is currently no publicly accessible dataset. The proposed effort has provided a dataset named MAVDESS (Mehran Audio-Visual Dataset Mehran Audio-Visual Database of Emotional Speech in Sindhi) for the academic community of a significant Sindhi language that is mainly spoken in Pakistan; however, no generic data for such languages is accessible in machine learning except few. Furthermore, the analysis of various emotions of Sindhi language in MAVDESS has been carried out to annotate the emotions using line features such as pitch, volume, and base, as well as toolkits such as OpenSmile, Scikit-Learn, and some important classification schemes such as LR, SVC, DT, and KNN, which will be further classified and computed to the machine via Python language for training a machine. Meanwhile, the dataset can be accessed in future via https://doi.org/10.5281/zenodo.5213073.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.299-300
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• 2023

최근 인공지능의 현실화와 더불어 프로그래밍 언어인 Python의 독주가 한창이다. 그렇다면 과연 메타버스 시대가 현실화 된다면 어떤 프로그래밍 언어가 대세가 될 것인가. 현재 메타버스 플랫폼인 로블록스에서 사용되고 있는 루아스크립트, 제페토 월드에서 사용되고 있는 Typescript에서 착안해서 미래의 메타버스 개발에 공용으로 사용될 효율적인 언어를 살펴보았다.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1537-1542
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• 2017

Emotion plays a key role in both generating and understanding narrative. In this article we analyzed the emotions represented in a movie script based on 8 emotion types from the wheel of emotions by Plutchik. First we conducted manual emotion tagging scene by scene. The most dominant emotions by manual tagging were anger, fear, and surprise. It makes sense when the film script we analyzed is a thriller-genre. We assumed that the emotions around the climax of the story would be heightened as the tension grew up. From manual tagging we could identify three such duration when the tension is high. Next we analyzed the emotions in the same script using Python-based NLTK VADERSentiment tool. The result showed that the emotions of anger and fear were most matched. The emotion of surprise, anticipation, and disgust, however, scored lower matching.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.7 no.11
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• pp.515-524
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• 2017

In this paper, we design and implement an expert system for diagnosing ADHD. As a result of the analysis with DSM-IV-TR, the ADHD diagnostic criteria are changed according to the age group. With this analyzed diagnostic, objects and their values are set and rules are created. We design a diagnostic system consisting of 'ADHD diagnostic system engine' and 'user query response program'. The ADHD diagnostic system engine is a rule-based reasoning engine that is implemented in the Prolog language and receives INPUT from the user query response program. By INPUT, the rule is executed based on the ADHD diagnostic criteria and the OUTPUT is sent back to the 'user query response program' by inferring the diagnostic result. The 'user query response program' is implemented in the Python language and serves as an interface for handling conversation with the user. The bridge between 'ADHD diagnostic system engine' and 'user query response program' is performed through the Pyswip library. As a result, the ADHD Diagnostic Expert System will help you plan your treatment with reduced diagnostic costs and use-complexity.

• Journal of The Korean Association of Information Education
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• v.24 no.1
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• pp.1-10
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• 2020

According to the Fourth Industrial Revolution, SW education is emphasized around the world to educate student with new abilities. Following to these global trends, SW education has become mandatory in Korea's 2015 revised curriculum. However, Korean elementary SW education is focused on the use of block-based programming languages. In addition, the point of view of selecting goals and organizing content of SW Education, the affective domain is ignored and focused only on the cognitive and psychomotor domains. So, this study explored method of SW education using the concept of Team-Shared Mental Model for develop of community capacity and Python, which is textual programming language gaining popularity recently. As a result of performing the post test t-test on two groups with similar Team-Shared Mental Model formation, we found that it was effective in forming a Team-Shared Mental Model of the group applying the SW teaching method suggested in the study.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.231-233
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• 2018

Programming learning is difficult for learners, and there are many cases where students lose interest in programming or give up. In this situation, the resilience is the ability of learners to stand up and get resilient and gain confidence in learning. Also, depending on the programming language and tools, the learner may feel the cognitive burden and the learning motivation may be different. In this study, we compared and analyzed the differences of resilience among the students of the scratch course and the Python course of the liberal programming lecture. As a result of the study, the differences in resilience of Python lectures and scratch lectures were significant but not significant. Through this study, learning strategies based on programming languages and tools and learners' tendencies were established.