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문화적 불만과 계급투쟁의 조건을 탐구하기 위한 이론적 제언
윤재원(Jaewon Yoon) 한국비평이론학회 2023 비평과이론 Vol.28 No.2
In an era when the effectiveness of collective class struggle is questioned, this paper presents a way to retheorize the conditions of the struggle at the structural level based on Stephen Shapiro’s theoretical suggestion. This paper first reviews the discourses of existing Marxist cultural studies and examines their limitations: humanistic and somatic materialism with a focus on human labor as a sensuous human activity, as well as culturalism that understands capitalism as articulations of various social elements, experiences, and practices. As a comparison, this paper investigates how Shapiro draws upon Karl Marx’s Capital and theorizes the way in which economic and sociocultural conditions, or base and superstructure, are interrelated as a framework to consider cultural fix alongside the economic fix of capitalism and the conditions of class struggle. This paper argues that Shapiro’s discussion extends a theoretical spectrum of cultural studies, and it presents a way of understanding the economic dynamics of the capitalist world-system as historical objectivity while distancing from economic determinism, and of comprehending consensus, discontent, conflict, and antagonism between the economic and the cultural as conditions of struggle.
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다시점 동영상 부호화를 위한 가변형 다시점GOP 예측 구조
윤재원(Jaewon Yoon),서정동(Jungdong Seo),김용태(Yongtae Kim),박창섭(Changseob Park),손광훈(Kwanghoon Sohn) 한국방송·미디어공학회 2006 방송공학회논문지 Vol.11 No.4
In this paper, we propose a flexible GGOP prediction structure to improve coding efficiency for multi-view video coding. In general, reference software used for MVC uses the fixed GGOP prediction structure. However, the performance of MVC depends on the base view and numbers of B-pictures between I-picture(or P-picture) and P-picture. In order to implement the flexible GGOP prediction structure, the location of base view is decided according to the global disparities among the adjacent sequences. Numbers of B-pictures between I-picture(or P-picture) and P-picture are decided by camera arrangement such as the baseline distance among the cameras. The proposed method shows better result than the reference software of MVC. The proposed prediction structure shows considerable reduction of coded bits by 7.1%.
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텍스트 감성분석과 해석가능한 기계학습 기반의 포렌식 기법을 활용한 회계부정탐지 : 내부회계관리제도를 중심으로
정우준 ( Woojune Jung ),윤재원 ( Jaewon Yoon ),김경호 ( Kyungho Kim ) 한국회계학회 2021 회계학연구 Vol.46 No.6
본 연구는 기계학습을 기반으로 회계부정탐지모형을 개발하는 데 있어 내부회계관리 제도 운영보고서의 텍스트 감성지수가 추가적 예측기여도를 갖는지 탐색적으로 검증한 후 개발된 모형의 해석을 시도하였다. 회계부정의 대용변수로는 금융감독원의 감리지적사례를 사용하였고, 기계학습에 사용될 데이터는 재무비율과 내부회계관리제도 변수 등 총 183개의 포괄적 입력변수로 구성한 데이터세트(Non-SA Set)와 구글의 자연어처리 API를 이용한 감성지수를 추가한 데이터세트(SA Set)로 구성하였다. 2개의 데이터세트에 4가지 모형을 학습시켜 총 8개의 예측모형을 개발한 결과는 다음과 같다. 첫째, SA Set에서는 로지스틱 모형 기반의 예측모형(LGST-SA)이 가장 높은 예측성과를 보였다. 둘째, Non-SA Set에서는 랜덤포레스트 모형 기반의 예측모형(RF-NSA)이 가장 높은 예측성과를 보였다. 셋째, LGST-SA의 예측성과는 RF-NSA보다 유의하게 높은 것으로 나타나, 감성지수의 예측기여도를 검증하였다. 추가적으로 해석가능한 기계학습 방법 중 하나인 SHAP을 통해 감성지수가 회계부정과 양(+)의 관계를 가지는 것을 사례적으로 확인하였다. 본 연구는 회계부정탐지모형의 개발에 있어 내부회계관리제도 운영보고서 감성분석의 유용성을 검증한 최초의 논문이며, 기계학습의 한계를 극복하기 위한 방안을 제시하였다는 점에서 의의가 있다. This study tried to verify exploratorily whether the sentiment index of operation report of Internal Control over Financial Reporting (ICFR) has an additional contribution for developing accounting fraud detection models based on machine learning and to interpret the results. The sanction cases of Financial Supervisory Service (FSS) was used as a proxy for accounting fraud. Non-SA Set consists of comprehensive input variables, such as financial ratio and ICFR related variables (183) and SA Set is addition of the sentiment index of operation report of ICFR using Google's natural language process (NLP) API to Non-SA Set. The results of developing a total of 8 models by training 4 models on 2 each data sets are as follows. First, the logistic model based prediction model (LGST-SA) showed the highest predictive performance on SA Set. Second, the random forest model based prediction model (RF-NSA) showed the highest predictive performance on Non-SA Set. Third, the predictive performance of LGST-SA was significantly higher than that of RF-NSA, so the predictive contribution of the sentiment index was verified. Additionally, through SHAP, one of the interpretable machine learning (IML) methods, it was anecdotally confirmed that the sentiment index had a positive relationship on accounting fraud. This study has importance in that the first paper to verify the usefulness of the sentiment analysis of operation report of ICFR for developing an accounting fraud detection model and to suggest a way to overcome the limitations of the machine learning.
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