Browsing by Author "Lee, Po-Ming"

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Applying LCS to Affective Image Classification in Spatial-Frequency Domain
(2014-01-01)
Lee, Po-Ming
;
Hsiao, Tzu-Chien
;
分子醫學與生物工程研究所
;
資訊工程學系
;
友訊交大聯合研發中心
Affective image classification is a task aims on classifying images based on their affective characteristics of inducing human emotions. This study achieves the task by using Learning Classifier System (LCS) and spatial-frequency features. The model built by using LCS achieves Area Under Curve (AUC) = 0.91 and accuracy rate over 86%. The result of the LCS is compared with other traditional machine-learning algorithms (e.g., Radial-Basis Function Network (RBF Network)) that are normally used in classification tasks. The study presents user-independent results which indicate that the horizontal visual stimulations contribute more to the emotion elicitation than the vertical visual stimulation.
The Influence of Emotion on Keyboard Typing: An Experimental Study Using Auditory Stimuli
(2015-06-11)
Lee, Po-Ming
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Tsui, Wei-Hsuan
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Hsiao, Tzu-Chien
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分子醫學與生物工程研究所
;
資訊工程學系
In recent years, a novel approach for emotion recognition has been reported, which is by keystroke dynamics. The advantages of using this approach are that the data used is rather non-intrusive and easy to obtain. However, there were only limited investigations about the phenomenon itself in previous studies. Hence, this study aimed to examine the source of variance in keyboard typing patterns caused by emotions. A controlled experiment to collect subjects\' keystroke data in different emotional states induced by International Affective Digitized Sounds (IADS) was conducted. Two-way Valence (3) x Arousal (3) ANOVAs was used to examine the collected dataset. The results of the experiment indicate that the effect of arousal is significant in keystroke duration (p < .05), keystroke latency (p < .01), but not in the accuracy rate of keyboard typing. The size of the emotional effect is small, compared to the individual variability. Our findings support the conclusion that the keystroke duration and latency are influenced by arousal. The finding about the size of the effect suggests that the accuracy rate of emotion recognition technology could be further improved if personalized models are utilized. Notably, the experiment was conducted using standard instruments and hence is expected to be highly reproducible.
The influence of emotion on keyboard typing: an experimental study using visual stimuli
(2014-06-20)
Lee, Po-Ming
;
Tsui, Wei-Hsuan
;
Hsiao, Tzu-Chien
;
分子醫學與生物工程研究所
;
資訊工程學系
Background: Emotion recognition technology plays the essential role of enhancement in Human-Computer Interaction (HCI). In recent years, a novel approach for emotion recognition has been reported, which is by keystroke dynamics. This approach can be considered to be rather desirable in HCI because the data used is rather non-intrusive and easy to obtain. However, there were only limited investigations about the phenomenon itself in previous studies. This study aims to examine the source of variance in keystroke typing patterns caused by emotions. Methods: A controlled experiment to collect subjects' keystroke data in different emotional states induced by International Affective Picture System (IAPS) was conducted. Two-way Valence (3) x Arousal (3) ANOVAs were used to examine the collected dataset. Results: The results of the experiment indicate that the effect of emotion is significant (p < .001) in the keystroke duration, keystroke latency, and accuracy rate of the keyboard typing. However, the size of the emotional effect is small, compare to the individual variability. Conclusions: Our findings support the conclusion that the keystroke duration, keystroke latency, and also the accuracy rate of typing, are influenced by emotional states. Notably, the finding about the size of effect suggests that the accuracy rate of the emotion recognition could be further improved if personalized models are utilized. On the other hand, the finding also provides an explanation of why real-world applications which authenticate the identity of users by monitoring keystrokes may not be interfered by the emotional states of users. The experiment was conducted using standard instruments and hence is expected to be highly reproducible.
The influence of emotion on keyboard typing: an experimental study using visual stimuli
(2014-06-20)
Lee, Po-Ming
;
Tsui, Wei-Hsuan
;
Hsiao, Tzu-Chien
;
分子醫學與生物工程研究所
;
資訊工程學系
Background: Emotion recognition technology plays the essential role of enhancement in Human-Computer Interaction (HCI). In recent years, a novel approach for emotion recognition has been reported, which is by keystroke dynamics. This approach can be considered to be rather desirable in HCI because the data used is rather non-intrusive and easy to obtain. However, there were only limited investigations about the phenomenon itself in previous studies. This study aims to examine the source of variance in keystroke typing patterns caused by emotions. Methods: A controlled experiment to collect subjects\' keystroke data in different emotional states induced by International Affective Picture System (IAPS) was conducted. Two-way Valence (3) x Arousal (3) ANOVAs were used to examine the collected dataset. Results: The results of the experiment indicate that the effect of emotion is significant (p < .001) in the keystroke duration, keystroke latency, and accuracy rate of the keyboard typing. However, the size of the emotional effect is small, compare to the individual variability. Conclusions: Our findings support the conclusion that the keystroke duration, keystroke latency, and also the accuracy rate of typing, are influenced by emotional states. Notably, the finding about the size of effect suggests that the accuracy rate of the emotion recognition could be further improved if personalized models are utilized. On the other hand, the finding also provides an explanation of why real-world applications which authenticate the identity of users by monitoring keystrokes may not be interfered by the emotional states of users. The experiment was conducted using standard instruments and hence is expected to be highly reproducible.
Towards Automatically Detecting Whether Student Is in Flow
(2014-01-01)
Lee, Po-Ming
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Jheng, Sin-Yu
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Hsiao, Tzu-Chien
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分子醫學與生物工程研究所
;
資訊工程學系
Csikszentmihalyi\'s flow theory states the components (e. g., balance between skill and challenge) that lead to an optimal state (referred to as flow state, or under flow experience) of intrinsic motivation and personal experience. Recent research has begun to validate the claims stated by the theory and extend the provided statements to the design of pedagogical interactions. To incorporate the theory in a design, automatic detector of flow is required. However, little attention has been drawn to this filed, and the detection of flow is currently still dominated by using surveys. Hence, within this paper, we present an automated detector which is able to identify the students that are in flow. This detector is developed using a step regression approach, with data collected from college students learning linear algebra from a step-based tutoring system.
Towards Flow Theory on the Design of a Tutoring System for Improving Affective Quality
(2014-01-01)
Lee, Po-Ming
;
Jheng, Sin-Yu
;
Hsiao, Tzu-Chien
;
分子醫學與生物工程研究所
;
資訊工程學系
Csikszentmihalyi\'s flow theory states that the components that lead to an optimal state of intrinsic motivation and personal experience may further lead to optimal learning. However, little evidence suggests that a tutoring system (TS) aimed at providing flow preconditions impacts student learning when the contents are the same. Therefore, this study tests this hypothesis by modifying a TS used in an international English language institute (IELI) to provide flow preconditions of students and maintain a balance between the skill level of students and the difficulty level of learning tasks. Fifty-five students in the IELI were separated into two groups to use the modified TS and the original TS. Analysis results indicate an improved engagement and affective quality, as well as reduced frustration levels of the students who used the proposed TS.
XCSF for Prediction on Emotion Induced by Image Based on Dimensional Theory of Emotion
(2012-01-01)
Lee, Po-Ming
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Teng, Yun
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Hsiao, Tzu-Chien
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分子醫學與生物工程研究所
;
資訊工程學系
Affective image classification problem is a problem aims on classifying images according to their affective characteristics of inducing human emotions. This paper extends the discrete state classification problem into a continuous function approximation problem by applying the experimental paradigm of dimensional emotion model. The Extended Classifier System for Function Approximation (XCSF) was applied to the problem and the results suggest that it outperforms linear regression (LR) in accomplishing this task. The obtained results also indicate that without using content based features of the images, the effects of individual difference can be relatively small.