• Journal of Software Assessment and Valuation
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• v.16 no.2
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• pp.87-98
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• 2020

Recently, IoT, artificial intelligence, cloud, big data, and mobile fields have converged, leading to a new industrial era called the 4th industrial revolution. This 4th industrial revolution has been expanded to all industry area and Software has been taken as important role in this revolution. Thus Software Safety is the huge factor because Software is highly relevant to human safety in accordance with Software expansion. However this Software Safety has been focused on not organization improvement activities but current design/development, In this paper, to solve this issue, Software Safety Maturity level and relevant Process Area is defined. This study is expected to contribute to systematic software safety activities.

• Journal of Software Assessment and Valuation
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• v.15 no.1
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• pp.123-131
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• 2019

The Internet of Things (IoT) environment must be able to meet the needs of users by providing access to various services that can be used to develop diverse user applications. However, QoS issues arise due to the characteristics of the IoT environment, such as numerous heterogeneous devices and potential resource constraints. In this paper, we propose a QoS prediction method that reflects trust between users in SOA based IoT. In order to increase the accuracy of QoS prediction, we analyze the trust and distrust relations between users and identify similarities among users and predict QoS based on them. The centrality is calculated to enhance trust relationships. Experimental results show that QoS prediction can be improved.

• Proceedings of the Korea Information Processing Society Conference
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• 2015.04a
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• pp.108-111
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• 2015

아직까지도 도서 구매자의 대다수는 도서를 구매할 시 오프라인 서점을 이용하며, 외부 의견은 도서 구매 결정에 커다란 영향을 마치는 것으로 나타났다. 이에 따라 대표적 외부 의견인 도서의 리뷰를 가공 분석하여 제공하는 모바일 기반의 시스템의 필요성이 대두되었다. 하지만 현재 마켓에 등록된 애플리케이션의 대다수는 도서에 대한 사용자의 리뷰를 제공하지 않거나 분류 분석되지 않은 상태의 리뷰를 제공한다. 따라서 본 논문에서는 각 도서의 리뷰를 수집하여 리뷰의 긍정 부정적 감정 추이를 분석하고 그 결과를 리뷰 핵심어에 따라 분류된 도서 평가 기준 별로 제공하며 이를 통해 사용자의 도서 구매 결정과 여러 도서간의 도서 선택에 도움을 줄 수 있는 모바일 애플리케이션을 설계하였다.

• Journal of Software Assessment and Valuation
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• v.12 no.2
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• pp.1-8
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• 2016

In this paper, we study abstracting and identifying license file from a package to prevent unintentional intellectual property infringement because of lost/modified/confliction of license information when redistributing open source software. To invest character of the license files, we analyzed 322 licenses by n-gram and TF-IDF methods, and abstract license files from the packages. We identified license information with a similarity of the registered licenses by cosine measurement.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.52-54
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• 2023

최근 인공지능 기술의 발전에 따라서 다양한 서비스에서 음성 인식을 활용한 서비스를 제공하면서 음성 인식에 대한 중요성이 증가하고 있다. 이 논문에서는 국내에서 많이 사용되고 있는 대표적인 인공지능 서비스 API 를 제공하는 구글, ETRI, 네이버에 대해서 감정 음성 관점에서 그 차이를 평가하였다. AI Hub 에서 제공하는 감성 대화 말뭉치 데이터 셋의 일부인 음성 테스트 데이터를 사용하여 평가한 결과 ETRI API 가 문자 오류율 (1.29%)과 단어 오류율(10.1%)의 성능 지표에 대해서 가장 우수한 음성 인식 성능을 보임을 확인하였다.

• Proceedings of the KAIS Fall Conference
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• 2006.05a
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• pp.252-257
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• 2006

감성은 인간의 생활에서 논리적 사고와 의사결정, 감정의 발생, 행동 등 모든 부분에 깊숙이 영향을 미치고 있어, 최근 감성의 개념을 도입한 공학적 제품의 도입이 활성화되어 여러 분야에 다양하게 사용 되어지고 있다. 그러나 감성을 평가함에 있어서는 단순한 해석의 의미 수준을 벗어 인간의 삶을 향상시키기 위한 제품이나 환경의 개발을 위해서는 인간의 감성을 정확하게 이해한다는 것은 체계적인 연구와 활용을 위한 선행 조건이라 할 수 있어, 생리신호등을 이용한 정량화된 감성평가 알고리즘의 개발 필요성이 있다. 특히, 최근 여러 IT기기들이 주변의 다양한 기술을 융합하여 다기능의 기기로 변모를 하고 있으며, 이러한 IT기기들에 인간의 감성을 평가할 수 있는 모듈을 부가하여 인간친화적인 기기로의 변모를 도모하고 있는 실정이다. 따라서, 본 연구에서는 측정이 용이한 소수의 생리신호만으로 간단하게 인간감성을 정량적으로 평가가 가능하며, SoC등에 간단하게 탑재할 수 있도록 시스템의 리소스를 적게 소비하는 소형 경량의 감성평가알고리즘을 개발하였다.

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.95-107
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• 2013

A linguistic element found in a movie, dialogue, plays a critical role in building up narrative structure. Still, analyses conducted on movies mostly focus on images due to the nature of a movie that conveys a story through its visual images while dialogue has either been underestimated or received less spotlight despite their importance. This study highlights the significance of lines in a movie. This study calls attention to dialogue, which has stayed out of the main focus and been on the periphery thus far when analyzing movies, so as to see how they contribute to constructing a narrative. It then spotlights the significance of dialogue in the movie. To this end, the study sorts out emotional expressions articulated by actors through their dialogues then to make polarity classification into affirmation and negation, followed by a quantitative analysis of how the polarity proportion of emotional expressions changes depending on the narrative structure. The study also suggests a narrative's relevance with emotions by pointing to dynamic emotional changes that shift between affirmation and negation depending on incidents, conflicts and resolution thereof throughout a movie.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.2
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• pp.222-226
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• 2010

With the continuously increasing volume of e-commerce transactions, it is now popular to buy some products and to evaluate them on the World Wide Web. The product reviews are very useful to customers because they can make better decisions based on the indirect experiences obtainable through these reviews. However, since online shopping malls do not provide ranking results, it is not easy for users to read all the relevant review documents effectively. Product reviews include subjective and emotional opinions. Thus, the review search is different from the general web search in terms of ranking strategy. In this paper, we propose an effective method of ranking the reviews that can reflect user's intention by using opinion mining techniques. The proposed method analyzes product reviews with query words, and sentimental polarity of subjective opinions. Through diverse experiments, we show that our proposed method outperforms conventional ones.

• Journal of Software Engineering Society
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• v.27 no.1
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• pp.8-14
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• 2018

As cloud computing has emerged as a hot trend in the IT industry, various types of cloud services have emerged. In addition, cloud service broker (CSB) technology has emerged to alleviate the complexity of the process of selecting the desired service that user wants among the various cloud services. One of the key features of the CSB is to recommend the best cloud services to users. In general, CSB can use a method to evaluate a service by receiving feedback about a service from users in order to recommend a cloud service. However, since each user has different criteria for giving a rating, there is a problem that reliability of service evaluation can be low when the rating is only used. In this paper, a method is proposed to supplement evaluation of rating based service by applying machine learning based sentiment analysis to cloud service user's review. In addition, the CSB prototype is implemented based on proposed method. Further, the results of comparing the performance of various learning algorithms is proposed that can be used for sentiment analysis through experiments using actual cloud service review as learning data. The proposed service evaluation method complements the disadvantages of the existing rating-based service evaluation and can reflect the service quality in terms of user experience.

• Smart Media Journal
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• v.12 no.5
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• pp.28-35
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• 2023

In this study, we propose a new light-weight model RoutingConvNet with fewer parameters to improve the applicability and practicality of speech emotion recognition. To reduce the number of learnable parameters, the proposed model connects bidirectional MFCCs on a channel-by-channel basis to learn long-term emotion dependence and extract contextual features. A light-weight deep CNN is constructed for low-level feature extraction, and self-attention is used to obtain information about channel and spatial signals in speech signals. In addition, we apply dynamic routing to improve the accuracy and construct a model that is robust to feature variations. The proposed model shows parameter reduction and accuracy improvement in the overall experiments of speech emotion datasets (EMO-DB, RAVDESS, and IEMOCAP), achieving 87.86%, 83.44%, and 66.06% accuracy respectively with about 156,000 parameters. In this study, we proposed a metric to calculate the trade-off between the number of parameters and accuracy for performance evaluation against light-weight.