Orientalism within Us: Discourse Structure That Tames Us Unwittingly When cherry blossoms bloom, Lass, North Korean lass! I will kiss your lips for the ...

The present study develops an artificial agent that plays the iterative chicken game based on a computational model that describes human behavior in competitive social interactions in terms of fairness. The computational model we adopted in this study, named as the self-concept fairness model, decides the agent’s action according to the evaluation of fairness of both opponent and self. We implemented the artificial agent in a computer program with a set of parameters adjustable by researchers. These parameters allow researchers to (...) determine the extent to which the agent behaves aggressively or cooperatively. To demonstrate the use of the proposed method for the investigation of human behavior, we performed an experiment in which human participants played the iterative chicken game against the artificial agent. Participants were divided into two groups, each being informed to play with either a person or the computer. The behavioral analysis results showed that the proposed method can induce changes in the behavioral pattern of human players by changing the agent’s behavioral pattern. Also, we found that participants tended to be more sensitive to fairness when they played with a human opponent than with a computer opponent. These results support that the artificial agent developed in this study will be useful to investigate human behavior in competitive social interactions. (shrink)

1. Kwangja chŏngsin e kwanhan kich'ojŏk ihae -- 2. Yuga ŭi kwanggyŏn'gwan -- 3. Toga ŭi kwangŏn kwa kwangja chŏngsin -- 4. Ŭmju munhwa wa Wi-Chin ŭi imt'anjŏk kwang -- 5. Songdae ihakchadŭl ŭi kwangjagwan -- 6. Yangmyŏnghak ŭi kwanggyŏn'gwan -- 7. Chosŏnjo yuhakchadŭl ŭi kwanggyŏn'gwan -- 8. Chosŏnjo yuhakcha ŭi yokki yŏnggwi ihae -- 9. Sŏhwa tot'ongnon kwa kwanggyŏn esŏ chunghaeng ŭro -- 10. Kwanggyŏn mihak ŭi munyejŏk chŏgyong -- 11. Chungguk hoehwa e nat'anan kwanggyŏn mihak -- 12. Chungguk (...) sŏye e nat'anan kwanggyŏn mihak -- 13. Chosŏnjo hoehwa e nat'anan kwanggyŏn mihak -- 14. Chosŏnjo sŏye ŭi kwanggyŏn mihak. (shrink)

BackgroundThe COVID-19 pandemic has highlighted prosocial behavior as a professional healthcare core competency. Although medical students are expected to work in the best interests of their patients, in the pandemic context, there is a greater need for ethical attention to be paid to the way medical students deal with moral dilemmas that may conflict with their obligations.MethodsThis study was conducted in the spring semester of 2019 on 271 students majoring in health professions: medicine, dentistry, and veterinary medicine. All participants provided (...) informed consent and completed measures that assessed utilitarian moral views, cognitive reflections, cognitive reappraisal, and moral judgment.ResultsThe healthcare-affiliated students who scored higher on the instrumental harm subscale in the measurement of utilitarian moral views were more likely to endorse not only other-sacrificial actions but also self-sacrificial ones for the greater good in moral dilemma scenarios. In particular, those engaged in deliberative processes tended to make more self-sacrificial judgments. The mediation analysis also revealed that the effect of deliberative processes on self-sacrificial judgments was mediated by cognitive reappraisal.ConclusionsThese findings suggested that cognitive reappraisal through deliberative processes is involved when the students with utilitarian inclination make prosocial decisions, that it is necessary to consider both moral views and emotional regulation when admitting candidates, and that moral education programs are needed in the healthcare field. (shrink)

SignificanceElectrical burns can cause severe damage to the nervous system, resulting in autonomic dysfunction with reduced cerebral perfusion. However, few studies have investigated these consequences.AimTo elucidate changes in prefrontal cerebral hemodynamics using functional near-infrared spectroscopy (fNIRS) during the head-up tilt table test (HUT) for patients with electrical burns.ApproachWe recruited 17 patients with acute electrical burns within 1 week after their accidents and 10 healthy volunteers. The NIRS parameters acquired using an fNIRS device attached to the forehead were analyzed in five (...) distinct HUT phases.ResultsBased on their HUT response patterns, patients with electrical burns were classified into the group with abnormal HUT results (APG, n = 4) or normal HUT results (NPG, n = 13) and compared with the healthy control (HC, n = 10) participants. We found trends in hemodynamic changes during the HUT that distinguished HC, NPG, and APG. Reduced cerebral perfusion and decreased blood oxygenation during the HUT were found in both the NPG and APG groups. Patients with electrical burns had autonomic dysfunction compared to the HC participants.ConclusionsUsing fNIRS, we observed that acute-stage electrical burn injuries could affect cerebral perfusion. (shrink)

Relational database management system is the most popular database system. It is important to maintain data security from information leakage and data corruption. RDBMS can be attacked by an outsider or an insider. It is difficult to detect an insider attack because its patterns are constantly changing and evolving. In this paper, we propose an adaptive database intrusion detection system that can be resistant to potential insider misuse using evolutionary reinforcement learning, which combines reinforcement learning and evolutionary learning. The model (...) consists of two neural networks, an evaluation network and an action network. The action network detects the intrusion, and the evaluation network provides feedback to the detection of the action network. Evolutionary learning is effective for dynamic patterns and atypical patterns, and reinforcement learning enables online learning. Experimental results show that the performance for detecting abnormal queries improves as the proposed model learns the intrusion adaptively using Transaction Processing performance Council-E scenario-based virtual query data. The proposed method achieves the highest performance at 94.86%, and we demonstrate the usefulness of the proposed method by performing 5-fold cross-validation. (shrink)

Nowadays, the quality standards of higher education institutions pay special attention to the performance and evaluation of the students. Then, having a complete academic record of each student, such as number of attempts, average grade and so on, plays a key role. In this context, the existence of missing data, which can happen for different reasons, leads to affect adversely interesting future analysis. Therefore, the use of imputation techniques is presented as a helpful tool to estimate the value of missing (...) data. This work deals with the academic records of engineering students, in which imputation techniques are applied. More specifically, it is assessed and compared to the performance of the multivariate imputation by chained equations methodology, the adaptive assignation algorithm based on multivariate adaptive regression splines and a hybridization based on self-organisation maps with Mahalanobis distances and AAA algorithm. The results show that proposed methods obtain successfully results regardless the number of missing values, in general terms. (shrink)

Role-based database management systems are most widely used for information storage and analysis but are known as vulnerable to insider attacks. The core of intrusion detection lies in an adaptive system, where an insider attack can be judged if it is different from the predicted role by performing classification on the user’s queries accessing the database and comparing it with the authorized role. In order to handle the high similarity of user queries for misclassified roles, this paper proposes a deep (...) metric neural network with strategic sampling algorithm that properly extracts salient features and directly learns a quantitative measure of similarity. A strategic sampling method of heuristically generating and learning training pairs through Monte Carlo search is proposed to select a training pair that can represent the entire dataset. With the TPC-E–based benchmark data trained with 11,000 queries for 11 roles, the proposed model produces the classification accuracy of 95.41%, which is the highest compared with the previous models. The results are verified through comparison of quantitative and qualitative evaluations, and the feature space modelled in the neural network is analysed by t-SNE algorithm. (shrink)

Background Nurses providing care to patients with end-of-life or terminal illnesses often encounter ethically challenging situations leading to moral distress. However, existing quantitative studies have examined moral distress using instruments that address general clinical situations rather than those specific to end-of-life care. Furthermore, qualitative studies have often been limited to participants from a single unit or those experiencing moral distress-induced circumstances. A comprehensive and integrated understanding of the overarching process of moral distress is vital to discern the unique circumstances surrounding (...) end-of-life care and its consequential impacts. Research objectives To explore the moral distress experiences of nurses who are frequently involved in caring for patients with end-of-life or terminal illnesses and apply it to two existing theories: the model of moral distress and the ecological model. Research design A qualitative descriptive approach was employed. Participants and research context Seven focus group interviews involving 30 nurses were performed. The subsequent transcriptions underwent rigorous content analysis. Ethical considerations We obtained Institutional Review Board approval from a university. Focus group interviews were conducted with nurses who agreed to participate and signed the consent form. Findings The moral distress-inducing factors and nurses’ perceived impact of moral distress were identified and categorized based on moral distress theories and ecological models. A total of 15 categories and 30 subcategories across the following 4 domains were derived: (1) intrapersonal, (2) interpersonal, (3) organizational, and (4) structural factors. Conclusions End-of-life-specific circumstances induced moral distress among nurses, with both negative and positive impacts identified. Effective organizational and policy support is essential to manage conflicts, form a healthy organizational culture, provide training, and prevent unnecessary expenses due to the negative consequences of moral distress. (shrink)