• 감성과학
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• 제25권1호
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• pp.21-28
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• 2022

People differ greatly in their capacity to persist in the face of challenges. Despite significant research, relatively little is known about cognitive factors that might be involved in perseverance. Building upon human threat-management mechanism, we predicted that perseverant people would be characterized by reduced sensitivity (i.e., longer detection latency) to threat cues. Our data from 5,898 job applicants showed that highly perseverant individuals required more time to correctly identify anger in faces, regardless of stimulus type (dynamic or static computer-morphed faces). Such individual differences were not observed in response to other facial expressions (happiness, sadness), and the effect was independent of gender, dispositional anxiety, or conscientiousness. Discussions were centered on the potential role of threat sensitivity in effortful pursuit of goals.

• 한국멀티미디어학회논문지
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• 제18권11호
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• pp.1268-1280
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• 2015

In video surveillance system, the exposure of a person's face is a serious threat to personal privacy. To protect the personal privacy in large amount of videos, an automatic face detection method is required to locate and mask the person's face. However, in real-world surveillance videos, the effectiveness of existing face detection methods could deteriorate due to large variations in facial appearance (e.g., facial pose, illumination etc.) or degraded face (e.g., occluded face, low-resolution face etc.). This paper proposes a new face detection method based on multi-level facial features. In a video frame, different kinds of spatial features are independently extracted, and analyzed, which could complement each other in the aforementioned challenges. Temporal domain analysis is also exploited to consolidate the proposed method. Experimental results show that, compared to competing methods, the proposed method is able to achieve very high recall rates while maintaining acceptable precision rates.

• Asian Journal for Public Opinion Research
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• 제8권3호
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• pp.180-196
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• 2020

This paper considers Japanese political interviews to integrate conversation and facial expression analysis. The behaviors of political leaders will be disclosed by analyzing questions and responses by using the turn-taking system in conversation analysis. Additionally, audiences who cannot understand verbal expressions alone will understand the psychology of political leaders by analyzing their facial expressions. Integral analyses promote understanding of the types of facial and verbal expressions of politicians and their effect on public opinion. Politicians have unique techniques to convince people. If people do not know these techniques and ways of various expressions, they will become confused, and politics may fall into populism as a result. To avoid this, a complete understanding of verbal and non-verbal behaviors is needed. This paper presents two analyses. The first analysis is a qualitative analysis that deals with Prime Minister Shinzō Abe and shows that differences between words and happy facial expressions occur. That result indicates that Abe expresses disgusted facial expressions when faced with the same question from an interviewer. The second is a quantitative multiple regression analysis where the dependent variables are six facial expressions: happy, sad, angry, surprised, scared, and disgusted. The independent variable is when politicians have a threat to face. Political interviews that directly inform audiences are used as a tool by politicians. Those interviews play an important role in modelling public opinion. The audience watches political interviews, and these mold support to the party. Watching political interviews contributes to the decision to support the political party when they vote in a coming election.

• 비교문화연구
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• 제27권
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• pp.435-470
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• 2012

This paper aims to examine directives and its adjacency pair directives-responses in the Chinese textbook Hanyu Kouyu from the speech act theory, and give some suggestions for Chinese speaking instruction. For analysis of directives, it is important to consider context surrounding it at first. Context includes space for speaking, person who speak with, adjacent speech act, etc., so this paper considers context firstly before explaining directives and discusses how each element of context effects interpretation of its meaning. From the politeness perspective, directives and rejection as one of the responses are easy to be FTAs(face-threatening acts) fundamentally, so they are required to have some strategies for minimizing the threat, and it is found that there are strategies for positive face and negative face in the textbook. In the textbook, it is also examined that directives-responses contribute for interlocutor to accomplish social acts, for example, negotiating, insisting, maintaining, making alternative ideas, etc.

• 문화기술의 융합
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• 제7권1호
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• pp.216-222
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• 2021

COVID-19 펜데믹 상황으로 인해 비군사·초국경적으로 발생하는 비전통적 안보위협에 대한 국가적 관심은 더욱 높아지고 있다. COVID-19로부터 발생한 국가적 피해 규모와 강도가 상상을 초월하기 때문이다. 이와 관련하여 육군은 다양한 집단지성 플랫폼을 통해 미래 한반도에서 발생할 수 있는 비전통적 안보위협을 예측하는 노력을 강구하고 있다. 향후 기후변화, 4차 산업혁명 도래에 따른 사회변화 및 기술발전으로 인해 비전통적 안보위협은 다양화될 것이다. 특히, 도시화율, 인터넷 보급률 및 대륙과 해양의 대기가 충돌하는 지리적 위치 등을 고려했을 때 우리나라에 가해질 비전통적 안보위협은 다른 나라의 경우보다 더욱 치명적일 것이다. 따라서 범정부 차원에서 민·관·군·산·학·연의 집단지성 플랫폼을 활용하여 비전통적 안보위협을 예측하는 것이 무엇보다도 중요할 것이다.

• 정보보호학회논문지
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• 제34권4호
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• pp.763-780
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• 2024

대내외 환경이 급격하게 변화함에 따라, 기업이 직면하는 보안 위협에 대한 보호대책 구현의 중요성이 점차 증대되고 있다. 이러한 상황에서 설계 초기 단계부터 보안을 접목하는 SbD(Security by Design, 보안내재화) 접근법의 필요성이 부각되고 있으며, 위협 모델링은 SbD의 핵심적인 도구로 인식되고 있다. 특히, 비용과 시간을 절약하기 위해 보안 문제를 조기에 발견하고 해결하는 Shift Left 전략의 적용을 위해서는 소프트웨어 개발자와 같은 보안 전문성이 부족한 직원의 위협 모델링 수행이 요구된다. 다양한 자동화된 위협 모델링 도구들이 출시되고 있으나, 보안 전문성이 부족한 직원이 사용하기엔 사용성이 부족하여 위협 모델링 수행에 제약이 따른다. 이를 해소하기 위해 위협 모델링 도구 관련 연구들을 분석하여 GQM접근법 기반의 사용성 평가기준을 도출하였다. 도출한 기준에 대한 전문가 설문을 진행하여 타당성과 객관성을 확보하였다. 위협 모델링 도구 3종(MS TMT, SPARTA, PyTM)의 사용성 평가를 수행하였으며, 평가 결과 MS TMT의 사용성 수준이 타 도구 대비 우세함을 확인하였다. 본 연구는 사용성 평가기준을 제시하여 보안 전문성이 부족한 직원도 효과적으로 위협 모델링을 수행할 수 있는 환경을 조성하는데 기여하는 것을 목표로 한다.

• 대한통합의학회지
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• 제9권4호
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• pp.225-235
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• 2021

Purpose: Since coronavirus disease 2019 (COVID-19) spread around the world, non-face-to-face services have emerged as the new normal. The functioning of children with developmental disabilities and cerebral palsy improves when parents, therapists, and institutions cooperate. We aimed to investigate the difficult environment of pediatric physical therapy caused by COVID-19, and grasp parents' perceptions of home exercise programs. We intend to design home exercise programs for pediatric treatment in the future and create a non-face-to-face pediatric rehabilitation service system based on our findings. Methods: The first survey was conducted on 30 parents who had children with cerebral palsy and developmental disabilities. It was produced after consultation with relevant experts and discussion with the research team, based on a survey conducted in a previous study. The second survey was produced by revising and supplementing the opinions of parents who completed the first survey and consulting experts. Thereafter, an online survey was conducted on 118 parents who had children with cerebral palsy and developmental disabilities. Results: 61.01 % of the parents reported that they were anxious or afraid for their children to be treated for infectious diseases. 83.90 % of the parents reported that the role of parents had increased. 50.00 % of the parents complained of a lack of skill. 85.59 % of the parents reported that they needed a home exercise program provided by the organization. As a non-face-to-face exercise management method, 35.59 % of the parents wanted a real-time video recording electronically mailed to them, and 34.75 % wanted real-time education using video conferencing programs such as zoom. Conclusion: The threat of COVID-19 has further emphasized the importance of social cooperation and management, leading organizations to enter a new era of non-face-to-face rehabilitation services. It is necessary to collect and systematize related studies to reflect more diverse opinions and improve the perceptions of therapists and parents.

• 한국항공운항학회:학술대회논문집
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• 한국항공운항학회 2006년도 추계학술대회 논문집
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• pp.61-65
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• 2006

The flight deck crew are under the operational time pressure in the cockpit. The crew tend to make errors when they face the threats of operational time pressure because they are in a rush. The flight deck crew can reduce threats and errors which existing within the airlines by using threat and error management when the crew know these threats and errors. The airlines can implement meaningful safety management system by analyzing into the useful information for to identify the hazard and manage the risk to reduce these threats and errors since aircrafts accidents can be fatal.With the threats and errors that were found regard to operational time pressures, company may implements safety change process to improve the safety systematically and the crew can manage the threats and errors more effectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권9호
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• pp.2529-2549
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• 2023

For effectively lowering down the risk of cyber threating, the zero-trust architecture (ZTA) has been gradually deployed to the fields of smart city, Internet of Things, and cloud computing. The main concept of ZTA is to maintain a distrustful attitude towards all devices, identities, and communication requests, which only offering the minimum access and validity. Unfortunately, adopting the most secure and complex multifactor authentication has brought enterprise and employee a troublesome and unfriendly burden. Thus, authors aim to incorporate machine learning technology to build an employee behavior analysis ZTA. The new framework is characterized by the ability of adjusting the difficulty of identity verification through the user behavioral patterns and the risk degree of the resource. In particular, three key factors, including one-time password, face feature, and authorization code, have been applied to design the adaptive multifactor continuous authentication system. Simulations have demonstrated that the new work can eliminate the necessity of maintaining a heavy authentication and ensure an employee-friendly experience.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.1100-1118
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• 2021

In a wide range of ML applications, the training data contains privacy-sensitive information that should be kept secure. Training the ML systems by privacy-sensitive data makes the ML model inherent to the data. As the structure of the model has been fine-tuned by training data, the model can be abused for accessing the data by the estimation in a reverse process called model inversion attack (MIA). Although, MIA has been applied to shallow neural network models of recognizers in literature and its threat in privacy violation has been approved, in the case of a deep learning (DL) model, its efficiency was under question. It was due to the complexity of a DL model structure, big number of DL model parameters, the huge size of training data, big number of registered users to a DL model and thereof big number of class labels. This research work first analyses the possibility of MIA on a deep learning model of a recognition system, namely a face recognizer. Second, despite the conventional MIA under the white box scenario of having partial access to the users' non-sensitive information in addition to the model structure, the MIA is implemented on a deep face recognition system by just having the model structure and parameters but not any user information. In this aspect, it is under a semi-white box scenario or in other words a gray-box scenario. The experimental results in targeting five registered users of a CNN-based face recognition system approve the possibility of regeneration of users' face images even for a deep model by MIA under a gray box scenario. Although, for some images the evaluation recognition score is low and the generated images are not easily recognizable, but for some other images the score is high and facial features of the targeted identities are observable. The objective and subjective evaluations demonstrate that privacy cyber-attack by MIA on a deep recognition system not only is feasible but also is a serious threat with increasing alert state in the future as there is considerable potential for integration more advanced ML techniques to MIA.