• Science of Emotion and Sensibility
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• v.11 no.1
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• pp.39-56
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• 2008

Previous studies of music, user interface, and human-computer interaction have used sounds which include musical structure rather than real music. This study investigated whether real music affects objective and perceived usability. Silence, sound, and music conditions were compared in experiment 1 (kimchi refrigerator) and 2 (remote controller for air conditioner). Participants' performances of reaction time and accuracy, and the degree of subjective satisfaction were analyzed. The results showed that main effects on task performances were not different significantly; however, perceived usability of music condition was better than sound condition, which was better than silence condition. It means that musical user interface improves perceived usability while not interfering task performance. This study provides a basis of emotional and aesthetic effects of music in home appliances design, and can be applied to studies for the blind. More specific guideline for the musical user interface can be drafted if further studies consider more various tasks, context, musical structure and types for the appliances.

• Science of Emotion and Sensibility
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• v.14 no.1
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• pp.147-156
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• 2011

Basic emotions such as happiness, sadness, anger, fear, and disgust have been widely used to investigate emotion-specific autonomic nervous system activity in many studies. On the contrary, surprise emotion, Suggested also as one of the basic emotions suggested by Ekman et al. (1983), has been least investigated. The purpose of this study was to provide a description of cardiovascular responses on surprise stimulus using electrocardiograph (ECG) and photoplethysmograph (PPG). ECG and PPG were recorded from 76 undergraduate students, as they were exposed to a visuo-acoustic surprise stimulus. Heart rate (HR), standard deviation of R-R interval (SD-RR), root mean square of successive R-R interval difference (RMSSD-RR), respiratory sinus arrhythmia (RSA), finger blood volume pulse amplitude (FBVPA), and finger pulse transit time (FPTT) were calculated before and after the stimulus presentation. Results show significant increase in HR, SD-RR, and RMSSD-RR, decreased FBVPA, and shortened FPTT. Evidence suggests that surprise emotion can be characterized by vasoconstriction and accelerated heart rate, sympathetic activation, and increased heart rate variability, parasympathetic activation. These results can be useful in developing an emotion theory, or profiling surprise-specific physiological responses, as well as establishing the basis for emotion recognition system in human-computer interaction.

• Investigative Magnetic Resonance Imaging
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• v.7 no.2
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• pp.93-99
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• 2003

The different perceiving patterns of each picture, alphabetic words and Chinese characters, were widely investigated psychophysically. The more precise localisation can be done in terms of brain activity us-ing functional image technique such as PET and fMRI recently, Until now, there was no fMRI study to make direct comparison between perception of single Chinese character and simplified pictures (pictograph). We have made direct comparison of these two components using modern magnetic resonance techniques. We cannot confirm the right hemispheric dominance for perception of single Chinese character and pictographs. These two kinds of perceiving pattern can be underlying different mechanism.

• The KIPS Transactions:PartD
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• v.18D no.3
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• pp.179-184
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• 2011

Current mobile devices have surpassed its boundaries as a more communication tool to a smart device which provides additional features. These features have supported the smart life of its users, but have reached its limit from low-performance processors and short-battery time. These issues can be resolved b implementing higher performing hardware, but they come with a burden of high cost. This paper introduces a new way of managing computing resources in a mobile device by enhancing the quality of human-computer interaction. The real-speed felt by users are mainly influenced by the time it takes form a user's input to the device to display the completed result on the screen. Since the size of the screen for mobile devices are small, if the processor only fetch data to be used for displaying on screen, the time can be significantly reduced. MVC+Prefetch Controller pattern accomplished this goal by using the minimum amount of data from DB to fetch display and still manages to support high-speed data transfer to achieve seamless display. This idea has been realized by practice using Samsung mobile phone S8500, which demonstrated the superior performance on user's perspective.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.5
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• pp.482-492
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• 2019

The naval ship consists of a variety of classes, including patrol, frigate, destroyer and landing ship. There are various means of communication between combat ships belonging to the mobile fleet, including voice network, text message network and tactical data link. The operation at the existing mobile fleet level was conducted mainly on voice and text message, and tactical data link were used mainly to share tactical information between combat ships. This study intended to design HCI for commander like as KDDX of fleet level based on tactical data link, a means of communication between ships, to secure operational control of the mobile fleet. Recently, naval ships have been developed in the organization of various sensors, weapons and tactical data links integrated into the combat management system, and this study also designed the HCI based on the tactical data links integrated in the combat management system. The purpose of this study was to secure the operational efficiency of the tactical data link based on the combat management system in operation of the mobile fleet, and to improve the capability of the fleet commander to recognize the battlefield situation awareness.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.267-286
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• 2023

The conversational agents such as AI speakers utilize voice conversation for human-computer interaction. Voice recognition errors often occur in conversational situations. Recognition errors in user utterance records can be categorized into two types. The first type is misrecognition errors, where the agent fails to recognize the user's speech entirely. The second type is misinterpretation errors, where the user's speech is recognized and services are provided, but the interpretation differs from the user's intention. Among these, misinterpretation errors require separate error detection as they are recorded as successful service interactions. In this study, various text separation methods were applied to detect misinterpretation. For each of these text separation methods, the similarity of consecutive speech pairs using word embedding and document embedding techniques, which convert words and documents into vectors. This approach goes beyond simple word-based similarity calculation to explore a new method for detecting misinterpretation errors. The research method involved utilizing real user utterance records to train and develop a detection model by applying patterns of misinterpretation error causes. The results revealed that the most significant analysis result was obtained through initial consonant extraction for detecting misinterpretation errors caused by the use of unregistered neologisms. Through comparison with other separation methods, different error types could be observed. This study has two main implications. First, for misinterpretation errors that are difficult to detect due to lack of recognition, the study proposed diverse text separation methods and found a novel method that improved performance remarkably. Second, if this is applied to conversational agents or voice recognition services requiring neologism detection, patterns of errors occurring from the voice recognition stage can be specified. The study proposed and verified that even if not categorized as errors, services can be provided according to user-desired results.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.179-192
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• 2014

Keeping in line with the recent changes in the information technology environment, the online learning environment that supports multiple users' participation such as MOOC (Massive Open Online Courses) has become important. One of the largest professional associations in Information Technology, IEEE Computer Society, announced that "Supporting New Learning Styles" is a crucial trend in 2014. Popular MOOC services, CourseRa and edX, have continued to build active learning environment with a large number of lectures accessible anywhere using smart devices, and have been used by an increasing number of users. In addition, collaborative web services (e.g., blogs and Wikipedia) also support the creation of various user-uploaded learning materials, resulting in a vast amount of new lectures and learning materials being created every day in the online space. However, it is difficult for an online educational system to keep a learner' motivation as learning occurs remotely, with limited capability to share knowledge among the learners. Thus, it is essential to understand which materials are needed for each learner and how to motivate learners to actively participate in online learning system. To overcome these issues, leveraging the constructivism theory and collective intelligence, we have developed a social bookmarking system called WeStudy, which supports learning material sharing among the users and provides personalized learning material recommendations. Constructivism theory argues that knowledge is being constructed while learners interact with the world. Collective intelligence can be separated into two types: (1) collaborative collective intelligence, which can be built on the basis of direct collaboration among the participants (e.g., Wikipedia), and (2) integrative collective intelligence, which produces new forms of knowledge by combining independent and distributed information through highly advanced technologies and algorithms (e.g., Google PageRank, Recommender systems). Recommender system, one of the examples of integrative collective intelligence, is to utilize online activities of the users and recommend what users may be interested in. Our system included both collaborative collective intelligence functions and integrative collective intelligence functions. We analyzed well-known Web services based on collective intelligence such as Wikipedia, Slideshare, and Videolectures to identify main design factors that support collective intelligence. Based on this analysis, in addition to sharing online resources through social bookmarking, we selected three essential functions for our system: 1) multimodal visualization of learning materials through two forms (e.g., list and graph), 2) personalized recommendation of learning materials, and 3) explicit designation of learners of their interest. After developing web-based WeStudy system, we conducted usability testing through the heuristic evaluation method that included seven heuristic indices: features and functionality, cognitive page, navigation, search and filtering, control and feedback, forms, context and text. We recruited 10 experts who majored in Human Computer Interaction and worked in the same field, and requested both quantitative and qualitative evaluation of the system. The evaluation results show that, relative to the other functions evaluated, the list/graph page produced higher scores on all indices except for contexts & text. In case of contexts & text, learning material page produced the best score, compared with the other functions. In general, the explicit designation of learners of their interests, one of the distinctive functions, received lower scores on all usability indices because of its unfamiliar functionality to the users. In summary, the evaluation results show that our system has achieved high usability with good performance with some minor issues, which need to be fully addressed before the public release of the system to large-scale users. The study findings provide practical guidelines for the design and development of various systems that utilize collective intelligence.