• Korean Journal of Cognitive Science
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• v.26 no.3
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• pp.301-322
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• 2015

This study examined the effect of emotional sounds on satisfaction of search (SOS). SOS occurs when detection of a target results in a lesser chance of finding subsequent targets when searching for an unknown number of targets. Previous studies have examined factors that may influence the phenomenon, but the effect of emotional sounds is yet to be identified. Therefore, the current study investigated how emotional sound affects magnitude of the SOS effect. In addition, participants' eye movements were recorded to determine the source of SOS errors. The search display included abstract T and L-shaped items on a cloudy background and positive and negative sounds. Results demonstrated that negative sounds produced the largest SOS effect by definition, but this was due to superior accuracy in low-salient single target trials. Response time, which represents efficiency, was consistently faster when negative sounds were provided, in all target conditions. On-target fixation classification revealed scanning error, which occurs because targets are not fixated, as the most prominent type of error. These results imply that the two dimensions of emotion - valence and arousal - interactively affect cognitive performance.

• Korean Journal of Cognitive Science
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• v.35 no.1
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• pp.23-48
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• 2024

Building human-aligned artificial intelligence (AI) for social support remains challenging despite the advancement of Large Language Models. We present a novel method, the Chain of Empathy (CoE) prompting, that utilizes insights from psychotherapy to induce LLMs to reason about human emotional states. This method is inspired by various psychotherapy approaches-Cognitive-Behavioral Therapy (CBT), Dialectical Behavior Therapy (DBT), Person-Centered Therapy (PCT), and Reality Therapy (RT)-each leading to different patterns of interpreting clients' mental states. LLMs without CoE reasoning generated predominantly exploratory responses. However, when LLMs used CoE reasoning, we found a more comprehensive range of empathic responses aligned with each psychotherapy model's different reasoning patterns. For empathic expression classification, the CBT-based CoE resulted in the most balanced classification of empathic expression labels and the text generation of empathic responses. However, regarding emotion reasoning, other approaches like DBT and PCT showed higher performance in emotion reaction classification. We further conducted qualitative analysis and alignment scoring of each prompt-generated output. The findings underscore the importance of understanding the emotional context and how it affects human-AI communication. Our research contributes to understanding how psychotherapy models can be incorporated into LLMs, facilitating the development of context-aware, safe, and empathically responsive AI.

• Journal of Korean Academy of Nursing Administration
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• v.6 no.2
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• pp.245-257
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• 2000

The purpose of this study was to validate caregiver outcomes included Nursing Outcomes Classification(NOC) developed by Johnson and Maas at the University of Iowa. A sample of 73 nurse experts working in university affiliated hospitals participated in this study. They were asked to rate indicators that examplified the outcomes on a scale of 1(indicator is not at all characteristic) to 5(indicator is very characteristic). A questionnaire with an adaptation of Fehring's methodology was used to establish the content validity of outcomes. The results were as follow: 1. Eight outcome label were considered to be 'supporting' and three outcome label were considered to be 'nonsupporting'. 2. 'Caregiver-Patient Relationship' attained an OCV score of 0.64 and the highest OCV score among caregiver outcomes.. 3. 'Caregiver Emotional Health' attained an OCV score of 0.54 and the lowest OCV score among caregiver outcomes. Replication study will be needed and outcomes sensitive to Korean culture need to be developed.

• Korean Journal of Adult Nursing
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• v.15 no.2
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• pp.173-182
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• 2003

Purpose: This study was founded to identify perception and ways of coping with stress according to classification of Sasangin(4 constitutions). Method: The subjects were 258 regular students who majored in oriental medicine in Jechon and Seoul. Data was collected by 3 types of questionnaires for 3 months; Perceived stess, ways of coping, Questionnaires of Sasang Constitution Classification (QSCCII). Data analysis was conducted by SPSS version 10. Result: 1) Difference of perceived stress in Sasangin: Perceived stress by the Sasangin indicated that Soeumin perceived more stress than Soyangin and Taeumin(P=.013). 2) Different method in coping with stress of Sasangin: It was found that Soeumin didn't make use of emotional coping way according to the analysis(P=.040). 3) Relationship between ways of coping for stress and perceived stress in Sasangin: It was shown that as Soeumin perceived stress so higher than Soyangin and Taeumin, they tended to use the solving problem-method therefore it was shown to have a negative correlation(P=.044). Conclusion: In conclusion, it was found that there were differences of Sasangin in perceived stress and ways to cope with it. The dose relationship between the perception and coping method of stress was found.

• Journal of the Korean Institute of Intelligent Systems
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• v.23 no.3
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• pp.262-267
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• 2013

The recognition of human emotional state is one of the most important components for efficient human-human and human- computer interaction. In this paper, four emotions such as fear, disgust, joy, and neutral was a main problem of classifying emotion recognition and an approach of visual-stimuli for eliciting emotion based on physiological signals of skin conductance (SC), skin temperature (SKT), and blood volume pulse (BVP) was used to design the experiment. In order to reach the goal of solving this problem, half-against-half (HAH) multi-class support vector machine (SVM) with Gaussian radial basis function (RBF) kernel was proposed showing the effective techniques to improve the accuracy rate of emotion classification. The experimental results proved that the proposed was an efficient method for solving the emotion recognition problems with the accuracy rate of 90% of neutral, 86.67% of joy, 85% of disgust, and 80% of fear.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.4
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• pp.364-371
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• 2013

Emotion Recognition is one of the important part to develop in human-human and human computer interaction. In this paper, we have focused on the performance of multi-class SVM (Support Vector Machine) with Gaussian RFB (Radial Basis function) kernel, which has been used to solve the problem of emotion recognition from physiological signals and to improve the accuracy of emotion recognition. The experimental paradigm for data acquisition, visual-stimuli of IAPS (International Affective Picture System) are used to induce emotional states, such as fear, disgust, joy, and neutral for each subject. The raw signals of acquisited data are splitted in the trial from each session to pre-process the data. The mean value and standard deviation are employed to extract the data for feature extraction and preparing in the next step of classification. The experimental results are proving that the proposed approach of multi-class SVM with Gaussian RBF kernel with OVO (One-Versus-One) method provided the successful performance, accuracies of classification, which has been performed over these four emotions.

• Journal of Korean Academy of Nursing
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• v.31 no.5
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• pp.897-911
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• 2001

The purpose of the study was to compare home care nursing intervention activities analyzed by the Nursing Intervention Classification (NIC) system for hospice and general patients. Method: For the descriptive survey study, data was collected by reviewing charts of 151 hospice patients and 421 general patients who registered in the department of home health care nursing at K Hospital. Results: According to the NIC system application, there were 2380 total nursing interventions used for the hospice patients and 8725 for the general home care patients. For both sets of patients (hospice vs. general), the most frequently used nursing intervention in level 1 was the Physiological: Complex domain (40.13 vs. 31.06 percent), followed by the Safety domain; in level 2, the Risk Management class (28.4 vs. 27.70 percent), followed by Tissue Perfusion Management; and in level 3, Vital Sign Monitoring (6.18 vs. 4.84 percent), followed by Health Screening. Conclusion: The study showed that there was a lack of specialized hospice nursing interventions such as emotional, family and spiritual support, and care for dying hospice patients.

• Journal of Korean Medical classics
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• v.32 no.2
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• pp.135-146
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• 2019

Objectives : This study examines the grounds on which Wulao(五勞) Liuji(六極) Qishang(七傷) which are categories of Xulao(虛勞) are differentiated, along with standards by which each category is further classified. Methods : Based on "Zhubingyuanhoulun(諸病源候論)", the first text to sort the different types and symptoms of Wulao(五勞) Liuji(六極) Qishang(七傷), each classification and its symptoms were analyzed. Texts which were written relatively close in time to "Zhubingyuanhoulun" were referenced in the process. Results & Conclusions : The differentiation of Wulao(五勞) Liuji(六極) Qishang(七傷) is based on the cause of illness. Wulao(五勞) is caused by mental activity which fatigues the Five Zang, Liuji(六極) is caused by exterior pathogens that damage the Five Body Elements, and Qishang(七傷) is caused by emotional factors as well as damaging practices. In close examination, Wulao(五勞) was further classified according to the different layers of mental activity, described in terms of taxation illness of the damaged Zang. Liuji(六極) is damage of the Five Body Elements by exterior pathogens, which was put into the spacial structure of nature and explained in six. Qishang(七傷) refers to the collective of representative symptoms and representative causes of Xulao.

• International Journal of Computer Science & Network Security
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• v.21 no.8
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• pp.238-246
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• 2021

The rapid rise of the Internet and social media has resulted in a large number of text-based reviews being placed on sites such as social media. In the age of social media, utilizing machine learning technologies to analyze the emotional context of comments aids in the understanding of QoS for any product or service. The classification and analysis of user reviews aids in the improvement of QoS. (Quality of Services). Machine Learning algorithms have evolved into a powerful tool for analyzing user sentiment. Unlike traditional categorization models, which are based on a set of rules. In sentiment categorization, Bidirectional Long Short-Term Memory (BiLSTM) has shown significant results, and Convolution Neural Network (CNN) has shown promising results. Using convolutions and pooling layers, CNN can successfully extract local information. BiLSTM uses dual LSTM orientations to increase the amount of background knowledge available to deep learning models. The suggested hybrid model combines the benefits of these two deep learning-based algorithms. The data source for analysis and classification was user reviews of Indian Railway Services on Twitter. The suggested hybrid model uses the Keras Embedding technique as an input source. The suggested model takes in data and generates lower-dimensional characteristics that result in a categorization result. The suggested hybrid model's performance was compared using Keras and Word2Vec, and the proposed model showed a significant improvement in response with an accuracy of 95.19 percent.

• International Journal of Computer Science & Network Security
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• v.23 no.8
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• pp.9-16
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• 2023

Speech can actively elicit feelings and attitudes by using words. It is important for researchers to identify the emotional content contained in speech signals as well as the sort of emotion that resulted from the speech that was made. In this study, we studied the emotion recognition system using a database in Arabic, especially in the Saudi dialect, the database is from a YouTube channel called Telfaz11, The four emotions that were examined were anger, happiness, sadness, and neutral. In our experiments, we extracted features from audio signals, such as Mel Frequency Cepstral Coefficient (MFCC) and Zero-Crossing Rate (ZCR), then we classified emotions using many classification algorithms such as machine learning algorithms (Support Vector Machine (SVM) and K-Nearest Neighbor (KNN)) and deep learning algorithms such as (Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM)). Our Experiments showed that the MFCC feature extraction method and CNN model obtained the best accuracy result with 95%, proving the effectiveness of this classification system in recognizing Arabic spoken emotions.