• Journal of Software Assessment and Valuation
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• v.14 no.2
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• pp.35-44
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• 2018

Tools for detecting cross-language clones usually compare abstract-syntax-tree representations of source code, which lacks scalability. In order to compare large source code to a practical level, we need a similarity checking technique that works on a token level basis. In this paper, we define common tokens that represent all tokens commonly used in programming languages of different paradigms. Each source code of different language is then transformed into the list of common tokens that are compared. Experimental results using exEyes show that our proposed method using common tokens is effective in detecting cross-language clones.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.1034-1037
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• 2013

최근 감정노동으로 인해 고충을 겪고 있는 사람들이 늘어가고 있다. 이로 인해 감정노동이 사회적 이슈가 되고 있으며 감정 노동을 해결하기 위해 조직들은 다양한 해결 방법들을 모색하고 시행하고 있다. 감정노동이란 육체가 힘들기 보다는 정신적 스트레스와 상처가 문제이기 때문에 이를 해결하기 위해서 기업들은 심리상담, 심리치료 등의 서비스 제도를 시행하고 있지만 한계가 있다. 업무 특성상 감정 노동에 장시간 노출 되어 고통 받고 있는 조직원들의 상황을 조직 내 커뮤니케이션 빈도와 관계를 분석하고 커뮤니케이션의 핵심역할을 하고 있는 인재들을 소시오그램 형태로 시각적으로 보여주어 조직 발전에 도움을 주는 시스템 시각화 절차와 소시오그램 요소를 설계하였다.

• Journal of Korea Society of Industrial Information Systems
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• v.26 no.1
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• pp.1-10
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• 2021

This study proposed a Long-Short Term Memory network to consider changes in emotion over time, and applied an attention mechanism to give weights to the emotion states that appear at specific moments. We used 32 channel EEG data from DEAP database. A 2-level classification (Low and High) experiment and a 3-level classification experiment (Low, Middle, and High) were performed on Valence and Arousal emotion model. As a result, accuracy of the 2-level classification experiment was 90.1% for Valence and 88.1% for Arousal. The accuracy of 3-level classification was 83.5% for Valence and 82.5% for Arousal.

• Journal of Software Assessment and Valuation
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• v.13 no.2
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• pp.9-17
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• 2017

Software compliance is an essential process when Open Source Software is included in software development to avoid such as license violation issue. However, analyzing quite big software which involves many developers requires enormous time and hard difficulty. To resolve these kinds of problem, SPDX formalizes and standardize the metadata about the software package. When the use of SPDX is activated, software package analysis would be simple and could contribute fair Open Source Software distribution. In this paper, we develop blockchain based SPDX distribution platform which fulfills the requirement of SPDX lifecycle to provide SPDX database which does not depend on particular centralized service but serve as distributed ledger and control by user's certification and their purpose. Moreover, to contribute invigoration of blockchain based SPDX distribution platform, we develop SPDX document generation plug-in for integrated development environment such as Visual Studio.

• Proceedings of the Korea Information Processing Society Conference
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• 2007.05a
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• pp.560-563
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• 2007

본 논문은 소프트웨어의 완성도와 관련하여 발주사와 개발사간의 분쟁이 있을 시에 필요한 분쟁조정을 위한 평가방법을 연구하였다. 먼저 소프트웨어 개발 과정에서 발생할 수 있는 분쟁의 유형을 분류하였고, 객관적인 평가를 위한 감정 절차를 분류하였다. 본 연구에서는 분쟁조정의 객관성 확보를 위한 가중치 설정, 각 기능들에 대한 중요도를 설정하여 어느 정도의 완성도를 보였는지를 정량적으로 평가하였다. 또한, 프로그램의 복제 문제를 판단하기 위하여 필요한 감정 항목 설정 및 도용 여부를 판단하기 위한 기본 자료로 활용될 수 있을 것으로 기대한다.

• Journal of Software Assessment and Valuation
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• v.17 no.2
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• pp.19-29
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• 2021

Blockchain technology fulfills the reliance requirement and is now entering a new stage of performance. However, the current blockchain technology has significant disadvantages in scalability and latency because of its architecture. Therefore, to adopt blockchain technology to real industry, we must overcome the performance issue by redesigning blockchain architecture. This paper introduces several element technologies and a novel blockchain architecture TPAC, that preserves blockchain's technical advantage but shows more stable and faster transaction processing performance and low latency.

• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.69-76
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• 2020

In this paper, the problem of determining the optimal time to market of software was studied using error correction time, an indicator of error correction difficulty. In particular, it was intended to modify the assumption that error detection time and correction time are independent in the software reliability growth model considering the existing error correction time, and to establish a general framework model that expresses the correlation between error detection time and correction time to determine when the software will be released. The results showed that it was important from an economic perspective to detect errors that took time to correct early in the test. It was concluded that it was very important to analyze the correlation between error detection time and error correction time in determining when to release the optimal software.

• The Transactions of the Korea Information Processing Society
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• v.13 no.6
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• pp.284-290
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• 2024

Speech emotion recognition (SER) is a technique that is used to analyze the speaker's voice patterns, including vibration, intensity, and tone, to determine their emotional state. There has been an increase in interest in artificial intelligence (AI) techniques, which are now widely used in medicine, education, industry, and the military. Nevertheless, existing researchers have attained impressive results by utilizing acted-out speech from skilled actors in a controlled environment for various scenarios. In particular, there is a mismatch between acted and spontaneous speech since acted speech includes more explicit emotional expressions than spontaneous speech. For this reason, spontaneous speech-emotion recognition remains a challenging task. This paper aims to conduct emotion recognition and improve performance using spontaneous speech data. To this end, we implement deep learning-based speech emotion recognition using the VGG (Visual Geometry Group) after converting 1-dimensional audio signals into a 2-dimensional spectrogram image. The experimental evaluations are performed on the Korean spontaneous emotional speech database from AI-Hub, consisting of 7 emotions, i.e., joy, love, anger, fear, sadness, surprise, and neutral. As a result, we achieved an average accuracy of 83.5% and 73.0% for adults and young people using a time-frequency 2-dimension spectrogram, respectively. In conclusion, our findings demonstrated that the suggested framework outperformed current state-of-the-art techniques for spontaneous speech and showed a promising performance despite the difficulty in quantifying spontaneous speech emotional expression.

• Journal of Software Assessment and Valuation
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• v.14 no.2
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• pp.1-9
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• 2018

The proportion of users and companies using open source continues to grow. The size of open source software market is growing rapidly not only in foreign countries but also in Korea. However, compared to the continuous development of open source software, there is little research on open source software subject classification, and the classification system of software is not specified either. At present, the user uses a method of directly inputting or tagging the subject, and there is a misclassification and hassle as a result. Research on open source software classification can also be used as a basis for open source software evaluation, recommendation, and filtering. Therefore, in this study, we propose a method to classify open source software by using machine learning model and propose performance comparison by machine learning model.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.2
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• pp.53-60
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• 2020

Sentiment analysis is the automated process of understanding attitudes and opinions about a given topic from written or spoken text. One of the sentiment analysis approaches is a dictionary-based approach, in which a sentiment dictionary plays an much important role. In this paper, we propose a method to automatically generate Korean sentiment lexicon from the well-known English sentiment lexicon called VADER (Valence Aware Dictionary and sEntiment Reasoner). The proposed method consists of three steps. The first step is to build a Korean-English bilingual lexicon using a Korean-English parallel corpus. The bilingual lexicon is a set of pairs between VADER sentiment words and Korean morphemes as candidates of Korean sentiment words. The second step is to construct a bilingual words graph using the bilingual lexicon. The third step is to run the label propagation algorithm throughout the bilingual graph. Finally a new Korean sentiment lexicon is generated by repeatedly applying the propagation algorithm until the values of all vertices converge. Empirically, the dictionary-based sentiment classifier using the Korean sentiment lexicon outperforms machine learning-based approaches on the KMU sentiment corpus and the Naver sentiment corpus. In the future, we will apply the proposed approach to generate multilingual sentiment lexica.