• 경영과정보연구
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• 제10권
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• pp.33-66
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• 2002

Counterproductive work behaviors are behaviors by employees intended to harm their organization or organization members. Human is the being has a desire and behaviors. To understand behaviors of an individual, it is important to understand the personality which determines a difference between individuals. Narcissists has psychological traits to be likely to experience negative emotions, such as frustration, hostility or anger, and this psychological traits of narcissists are more likely to induce an aggression. In this view, the purpose of this study was to examine the relationships between counterproductive work behaviors and narcissistic personality characteristics and to explore psychological dynamics about how narcissistic personality characteristics had an effect on counterproductive work behaviors. As a result, facts known were as follows. First, narcissists has strong desires to maintain a sense of superiority over others and defend their egos against unpleasant evaluation information, even if the information is factual and accurate. Second, narcissists are hyper-sensitive to negative information and are more likely to encounter information or situations that challenge their positive self-appraisals by this view. Third, in response to these challenges, or ego threats, these individuals are more likely to experience negative emotions, such as anger, frustration, or hostility. Forth, this negative emotions lead to aggression and as a result, this is more likely to induce counterproductive work behaviors such as theft, sabotage, interpersonal aggression, work slowdowns, wasting time and materials, and spreading rumors. Thus, narcissism is another individual difference variable that may be an important factor in determining counterproductive work behaviors, particularly under conditions perceived to be difficult or stressful.

• Journal of Ubiquitous Convergence Technology
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• 제2권1호
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• pp.18-26
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• 2008

The invariance relation existing in the non-negative matrix factorization (NMF) is used for constructing robust image hashes in this work. The image is first re-scaled to a fixed size. Low-pass filtering is performed on the luminance component of the re-sized image to produce a normalized matrix. Entries in the normalized matrix are pseudo-randomly re-arranged under the control of a secret key to generate a secondary image. Non-negative matrix factorization is then performed on the secondary image. As the relation between most pairs of adjacent entries in the NMF's coefficient matrix is basically invariant to ordinary image processing, a coarse quantization scheme is devised to compress the extracted features contained in the coefficient matrix. The obtained binary elements are used to form the image hash after being scrambled based on another key. Similarity between hashes is measured by the Hamming distance. Experimental results show that the proposed scheme is robust against perceptually acceptable modifications to the image such as Gaussian filtering, moderate noise contamination, JPEG compression, re-scaling, and watermark embedding. Hashes of different images have very low collision probability. Tampering to local image areas can be detected by comparing the Hamming distance with a predetermined threshold, indicating the usefulness of the technique in digital forensics.

• The Journal of Asian Finance, Economics and Business
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• 제6권1호
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• pp.21-31
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• 2019

This paper examines how corporate social responsibility is related to the degree of asymmetric information in the Korean financial market. Recent theory argues that there is a negative relationship between a firm's corporate social responsibility and its information asymmetry. To test this hypothesis, we use the environment, social and governance (ESG) score, published by the Korean Corporate Governance Service, to proxy a firm's management practices toward socially responsible activities. In the entire sample of the Korean firms, we find contrasting results; the ESG score shows negative relationships with the price impact measure but statistically insignificant relationships with the dispersion of analyst forecasts. However, the ESG score shows negative relationships with both measures when we exclude chaebol affiliates from the sample. These findings are robust when we examine environmental, social and corporate governance scores separately. This set of results argues for the extant theory, expecting a negative relationship between a firm's engagement in corporate social responsibility and asymmetric information. It further argues for the importance of firm characteristics in determining the influence of socially responsible activities.

• International Journal of Computer Science & Network Security
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• 제23권3호
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• pp.101-109
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• 2023

This paper discusses the use of sentiment analysis and text summarization techniques to extract valuable information from the large volume of user-generated content such as reviews, comments, and feedback on online platforms and social media. The paper highlights the effectiveness of sentiment analysis in identifying positive and negative reviews and the importance of summarizing such text to facilitate comprehension and convey essential findings to readers. The proposed work focuses on summarizing all positive and negative reviews to enhance product quality, and the performance of the generated summaries is measured using ROUGE scores. The results show promising outcomes for the developed methods in summarizing user-generated content.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2006년도 한국컴퓨터종합학술대회 논문집 Vol.33 No.1 (A)
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• pp.391-393
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• 2006

인터넷의 급속한 확산과 대량 정보의 이동은 문서의 요약을 더욱 필요로 하고 있다. 본 논문은 비음수 행렬 인수분해로(NMF, non-negative matrix factorization) 얻어진 비음수 의미 가변 행렬(NSVM, non-negative semantic variable matrix)을 이용하여 자동으로 포괄적 문서요약 하는 새로운 방범을 제안하였다. 제안된 방법은 인간의 인식 과정과 유사한 비음수 제약을 사용한다. 이 결과 잠재의미색인에 비해 더욱 의미 있는 문장을 선택하여 문서를 요약할 수 있다. 또한, 비지도 학습에 의한 문서요약으로 사전 전문가에 의한 학습문장이 필요 없으며, 적은 계산비용을 통하여 쉽게 문장을 추출할 수 있는 장점을 갖는다.

• 한국전자거래학회지
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• 제21권1호
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• pp.131-145
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• 2016

최근 빅데이터와 같은 디지털 환경으로 다양한 정보 매체를 통해 정보와 지식을 생산되고 있는 반면 이렇게 생산된 정보가 법적인 테두리를 벗어나 무분별하게 확대되고 재생산 되는등 정보의 역기능 역시 커지고 있다. 특히, 개인정보의 경우 기존의 목적 외에 사용되거나 잘못된 형태로 사용이 되면 피해가 발생된다. 일반적으로 사용자가 위험을 감수하더라도 개인 스스로 자신의 정보를 제공하거나 공유하고 있는 이유는 기업이나 조직이 개인정보를 안전하게 지켜줄 것이라고 믿기 때문이다. 본 연구는 정보 프라이버시에 대한 위험과 이를 억제하는 정책이 개인정보 제공의도에 어떠한 영향을 미치는지 분석하여 검증하고자 하였다. 이를 위해 정보 프라이버시 위험과 정책이 정보 프라이버시 염려와 신뢰, 개인정보 제공의도에 어떠한 영향을 미치는지에 대한 영향도를 분석하였다. 연구결과, 빅데이터 시대에 정보 프라이버시 위험을 낮추고 정책을 명확하게 제시한다면 정보 프라이버시 염려는 낮아지고 기업에 대한 신뢰가 높아져 개인정보를 제공할 것이라는 것을 밝혔다.

• Journal of the Korean Data and Information Science Society
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• 제19권2호
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• pp.569-576
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• 2008

Association rule mining searches for interesting relationships among items in a given database. Association rules are frequently used by retail stores to assist in marketing, advertising, floor placement, and inventory control. There are three primary quality measures for association rule, support and confidence and lift. Association rule is an interesting rule among purchased items in transaction, but the negative association rule is an interesting rule that includes items which are not purchased. Boolean Analyzer is the method to produce the negative association rule using PIM. But, PIM is subjective. In this paper, we present statistical objective criterion in negative association rules using Boolean Analyzer.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2008년도 한국컴퓨터종합학술대회논문집 Vol.35 No.1 (C)
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• pp.212-216
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• 2008

기존의 알려진 데이터에 기반하여 분류 알고리즘을 통해 새로운 생물학적인 사실을 예측하는 것은 생물학 연구에 매우 유용하다. 하지만 생물학 데이터 분류 문제에서 positive 데이터만 존재할 뿐, negative 데이터는 존재하지 않는 경우가 많다. 이와 같은 상황에서는 많은 경우에 임의로 negative data를 구성하여 사용하게 된다. 하지만, negative 데이터는 실제로 negative임이 보장된 것이 아니고, 임의로 생성된 데이터의 특성에 따라 분류 성능 및 모델의 특성에 많은 차이를 보일 수 있다. 따라서 본 논문에서는 단일 클래스 분류 알고리즘 중 하나인 support vector data description(SVDD) 방법을 이용하여 실제 microRNA target 예측 문제에서 positive 데이터만을 이용하여 학습하고 분류를 수행하였다. 이를 통해 일반적인 이진 분류 방법에 비해 이와 같은 방법이 실제 생물학 문제에 보다 적합하게 적용될 수 있음을 확인한다.

• 한국의류산업학회지
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• 제15권5호
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• pp.763-776
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• 2013

This study investigated the uncertainty, regret experience, and negative behavior intention of fashion consumers according to the types of internet shopping malls. The data was obtained from internet fashion consumers, and 394 responses were used in the data analysis. The statistical analysis methods were frequency analysis, factor analysis, reliability analysis, t-test, ANOVA, and regression analysis. As results, the uncertainty of internet fashion consumers was composed of two factors; information uncertainty and preference uncertainty. The regret experience was composed of function or service regret, suitability regret, and product regret. Also, the negative behavior intention was composed of purchase switching intention and purchase deferral intention. The information uncertainty of fashion consumers positively affected the negative behavior intention in all types of internet shopping malls (e.g., open market, integrated shopping mall, and fashion specialized shopping mall). In open market, the preference uncertainty negatively affected the purchase switching intention; however, the preference uncertainty positively affected the purchase deferral intention. In open market and fashion specialized shopping mall, the product regret of internet fashion consumers positively affected the negative behavior intention. In addition, there were partially significant differences in the factors of uncertainty, regret experience, and negative behavior intention by gender and marital status of demographic characteristics. The results of this study will provide useful information to the marketing strategies considering fashion consumer's negative emotion and behaviors according to the types of internet shopping malls.

• 대한수학회논문집
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• 제22권1호
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• pp.137-143
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• 2007

In this paper, a Berstein-Hoeffding type inequality is established for linearly negative quadrant dependent random variables. A condition is given for almost sure convergence and the associated rate of convergence is specified.