• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.832-837
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• 2007

Many researchers are studying brain activity to using functional Magnetic Resonance Imaging (fMRI), Time Resolved Spectroscopy(TRS), Electroencephalography(EEG), and etc. They are used detection of seizures or epilepsy and deception detection in the main. In this paper, we focus on emotion recognition by recording brain waves. We specially use fMRI, TRS, and EEG for measuring brain activity Researchers are experimenting brain waves to get only a measuring apparatus or to use both fMRI and EEG. This paper is measured that we take images of fMRI and TRS about brain activity as human emotions and then we take data of EEG signals. Especially, we focus on EEG signals analysis. We analyze not only original features in brain waves but also transferred features to classify into five sections as frequency. And we eliminate low frequency from 0.2 to 4Hz for EEG artifacts elimination.

• Journal of KIISE
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• v.42 no.11
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• pp.1459-1466
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• 2015

Lockscreen is one of the most frequently encountered interfaces by smartphone users. Although users perform unlocking actions every day, there are no benefits in using lockscreens apart from security and authentication purposes. In this paper, we replace the traditional lockscreen with an application that analyzes facial expressions in order to collect facial expression data and provide real-time feedback to users. To evaluate this concept, we have implemented Quantified Lockscreen application, supporting the following contributions of this paper: 1) an unobtrusive interface for collecting facial expression data and evaluating emotional patterns, 2) an improvement in accuracy of facial expression detection through a personalized machine learning process, and 3) an enhancement of the validity of emotion data through bidirectional, multi-channel and multi-input methodology.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.79-88
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• 2024

The use of the social media has become part of our daily life activities. The social web channels provide the content generation facility to its users who can share their views, opinions and experiences towards certain topics. The researchers are using the social media content for various research areas. Sentiment analysis, one of the most active research areas in last decade, is the process to extract reviews, opinions and sentiments of people. Sentiment analysis is applied in diverse sub-areas such as subjectivity analysis, polarity detection, and emotion detection. Stance classification has emerged as a new and interesting research area as it aims to determine whether the content writer is in favor, against or neutral towards the target topic or issue. Stance classification is significant as it has many research applications like rumor stance classifications, stance classification towards public forums, claim stance classification, neural attention stance classification, online debate stance classification, dialogic properties stance classification etc. This research study explores different feature sets such as lexical, sentiment-specific, dialog-based which have been extracted using the standard datasets in the relevant area. Supervised learning approaches of generative algorithms such as Naïve Bayes and discriminative machine learning algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree and k-Nearest Neighbor have been applied and then ensemble-based algorithms like Random Forest and AdaBoost have been applied. The empirical based results have been evaluated using the standard performance measures of Accuracy, Precision, Recall, and F-measures.

• Science of Emotion and Sensibility
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• v.24 no.2
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• pp.3-12
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• 2021

Most researchers have experimentally evaluated the detection efficiency of reaction time-based concealed information tests (RT CIT). In experimental studies, two experimental paradigms have been mainly used to create a lying situation, mock-crime paradigm and personal-item paradigm. This study is aimed at testing the detection efficiency of RT CIT for the one that could be estimated as the same as the other, regardless of the experimental paradigms. In study 1, it was tested whether the effect size of RT CIT was different in the two experimental paradigms through meta-analysis. As a result of the meta-analysis of 39 studies, the effect size (Hedges'g = 1.330) of the mock-crime paradigm was slightly larger than that (Hedges'g = 1.145) of the personal-item paradigm, but no statistically significant difference was found. Study 2 was an experimental study using both the mock-crime and personal-item paradigms, it was conducted to determine whether the detection efficiency of RT CIT differs in the two experimental paradigms. As a result of ANOVA, it was found that the RT differences between the probe and irrelevant stimuli were not significant in the two experimental paradigms. In the experimental study, the effect size (Cohen's d) of the mock-crime and personal-item paradigms were 1.638 and 1.535, respectively. In the discussion section, the reason of the detection efficiency of RT CIT not affected by the experimental paradigms was discussed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.12C
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• pp.1200-1208
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• 2003

Affective computing plays an important role in intelligent Human Computer Interactions(HCI). To detect emotional events, it is desirable to construct a computing model for extracting emotion related features from video. In this paper, we propose a statistical model based on the probabilistic distribution of low level features in video shots. The proposed method extracts low level features from video shots and then from a GMM(Gaussian Mixture Model) for them to detect emotional shots. As low level features, we use color, camera motion and sequence of shot lengths. The features can be modeled as a GMM by using EM(Expectation Maximization) algorithm and the relations between time and emotions are estimated by MLE(Maximum Likelihood Estimation). Finally, the two statistical models are combined together using Bayesian framework to detect emotional events in video.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.517-526
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• 2012

This study investigated the impact of individual differences in motivational reactivity on cognitive effort, memory strength (sensitivity) and decision making (criterion bias) in response to Internet ads with positive and negative content. Individual variation in trait motivational activation was measured using the Motivational Activation Measurement developed by A. Lang and her colleagues (A. Lang, Bradley, Sparks, & Lee, 2007). MAM indexes an individual's tendency to approach pleasant stimuli (ASA, Appetitive System Activation) and avoid unpleasant stimuli (DSA, Defensive System Activation). Results showed that individuals higher in ASA exert more cognitive effort during positive ads than individuals lower in ASA. Individuals higher in DSA exert more cognitive effort during negative ads compared to individuals lower in DSA. ASA did not predict recognition memory. However, individuals higher in DSA recognized ads better than those lower in DSA. The criterion bias data revealed participants higher in ASA had more conservative decision criterion, compared to participants lower in ASA. Individuals higher in DSA also showed more conservative decision criterion compared to individuals lower in DSA. The theoretical and practical implications are discussed.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 1997.11a
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• pp.168-173
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• 1997

시선의 추출을 통해 사용자의 관심 방향을 알고자하는 연구는 여러 분야에 응용될 수 있는데, 대표적인 것이 장애인의 컴퓨터 이용이나, 다중 윈도우에서 마우스의 기능 대용 및, VR에서의 위치 추적 장비의 대용 그리고 원격 회의 시스템에서의 view controlling등이다. 기존의 대부분의 연구들에서는 얼굴의 입력된 동영상으로부터 얼굴의 3차원 움직임량(rotation, translation)을 구하는데 중점을 두고 있으나 [1][2], 모니터, 카메라, 얼굴 좌표계간의 복잡한 변환 과정때문에 이를 바탕으로 사용자의 응시 위치를 파악하고자하는 연구는 거으 이루어지지 않고 있다. 본 논문에서는 일반 사무실 환경에서 입력된 얼굴 동영상으로부터 얼굴 영역 및 얼굴내의 눈, 코, 입 영역 등을 추출함으로써 모니터의 일정 영역을 응시하는 순간 변화된 특징점들의 위치 및 특징점들이 형성하는 기하학적 모양의 변화를 바탕으로 응시 위치를 계산하였다. 이때 앞의 세 좌표계간의 복잡한 변환 관계를 해결하기 위하여, 신경망 구조(다층 퍼셉트론)을 이용하였다. 신경망의 학습 과정을 위해서는 모니터 화면을 15영역(가로 5등분, 세로 3등분)으로 분할하여 각 영역의 중심점을 응시할 때 추출된 특징점들을 사용하였다. 이때 학습된 15개의 응시 위치이외에 또 다른 응시 영역에 대한 출력값을 얻기 위해, 출력 함수로 연속적이고 미분가능한 함수(linear output function)를 사용하였다. 실험 결과 신경망을 이용한 응시위치 파악 결과가 선형 보간법[3]을 사용한 결과보다 정확한 성능을 나타냈다.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.90-93
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• 2008

In natural language, it is common that repetitive constituents in an expression are to be left out and it is necessary to figure out the constituents omitted at analyzing the meaning of the sentence. This paper is on recognition of boundaries of parallel noun phrases by figuring out constituents omitted. Recognition of parallel noun phrases can greatly reduce complexity at the phase of sentence parsing. Moreover, in natural language information retrieval, recognition of noun with modifiers can play an important role in making indexes. We propose an unsupervised probabilistic model that identifies parallel cores as well as boundaries of parallel noun phrases conjoined by a conjunctive particle. It is based on the idea of swapping constituents, utilizing symmetry (two or more identical constituents are repeated) and reversibility (the order of constituents is changeable) in parallel structure. Semantic features of the modifiers around parallel noun phrase, are also used the probabilistic swapping model. The model is language-independent and in this paper presented on parallel noun phrases in Korean language. Experiment shows that our probabilistic model outperforms symmetry-based model and supervised machine learning based approaches.

• Journal of Information Science Theory and Practice
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• v.7 no.1
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• pp.52-71
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• 2019

An insider threat is a threat that comes from people within the organization being attacked. It can be described as a function of the motivation, opportunity, and capability of the insider. Compared to managing the dimensions of opportunity and capability, assessing one's motivation in committing malicious acts poses more challenges to organizations because it usually involves a more obtrusive process of psychological examination. The existing body of research in psycholinguistics suggests that automated text analysis of electronic communications can be an alternative for predicting and detecting insider threat through unobtrusive behavior monitoring. However, a major challenge in employing this approach is that it is difficult to minimize the risk of missing any potential threat while maintaining an acceptable false alarm rate. To deal with the trade-off between the risk of missed catches and the false alarm rate, we propose a unified psycholinguistic framework that consolidates multiple text analyzers to carry out sentiment analysis, emotion analysis, and topic modeling on electronic communications for unobtrusive psychological assessment. The user scenarios presented in this paper demonstrated how the trade-off issue can be attenuated with different text analyzers working collaboratively to provide more comprehensive summaries of users' psychological states.

• Journal of the Ergonomics Society of Korea
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• v.31 no.1
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• pp.23-32
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• 2012

Objective: This research proposes a methodology, which validates a grasp of the customers' emotions in the service design area. Background: As the era of service design has taken its approach, the need for a deliberate design that would reflect the customer's experience had emerged in the area of service. Therefore, a variety of methodologies has been adopted in the field of service design with the purpose of discovery of the customers' needs. Even though the importance of an emotion-sentient research of a service experience increases, its research progress remains to be inadequate in comparison to all the other areas. Method: Having had taken some resources from the emotional studies under other areas of expertise as a base, the concept of volatility of emotions has been introduced as the core element of this research, further followed by an elaboration of its special characteristics. The observation technique under Stakeholder's system: SOMC(Simultaneous Observation Method based on Cooperation) has been proposed in this study as it presents an effective way to grasp the concept of volatile emotions in contrast to the previously existent types of methodologies. Results: The SOMC rather supplements the existing research methods than substitutes the previous ones. In other words, although the existing research system allowed emotion detection, it was difficult to capture the change of momentary and fickle emotions. On the opposite, the SOMC provides a condition allowing a sufficient grasp of the customer's emotions and facilitates emotional capture. Conclusion: For that reason, it is hoped that this piece of research represents a valuable and effective approach in terms of grasping the true needs of the customers on the emotional level, which will in its turn contribute to the improvement of the service quality in the midst of a complicated service condition. Application: Moreover, the purpose of this research is that in its outcome it may serve as a sufficient contribution to the area of emotional studies within the field of service design.