• Journal of Korea Entertainment Industry Association
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• v.15 no.3
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• pp.41-50
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• 2021

While artificial intelligence is rapidly replacing human jobs, the art field where human creativity is important is considered an exception. There are currently several AI mastering services in the field of mastering music, a profession at the border between art and technology. In general, the quality of AI mastering is considered to be inferior to the work of a human professional mastering engineer. In this paper, acoustic analysis, listening experiments, and expert interviews were conducted to compare AI and human experts. In the acoustic analysis, In the analysis of audio, there was no significant difference between the results of professional mastering engineers and the results of artificial intelligence. In the listening experiment, the non-musicians could not distinguish between the sound quality of the professional mastering engineer's work and the artificial intelligence work. The group of musicians showed a preference for a specific sound source, but the preference for a specific mastering did not appear significantly. In an expert interview, In expert interviews, respondents answered that there was no significant difference in quality between the two mastering services, and the biggest difference was the communication method between the mastering service provider and the user. In addition, as data increases, it is expected that artificial intelligence mastering will achieve rapid quality improvement and further improvement in communication.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.518-519
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• 2014

The smartphone of pen input device makes a stage appearance by the development of the latest technology. Such smartphone chooses both touch method using hand and pen input device simultaneously. This research try to conduct usability evaluation in order to find the user's preference between pen input and touch input method. We recruit five novices and five expert for it. We presented all participants the task of touch input and pen input in series. This research conducts the unstructured interview after doing tasks as a pilot study. As a result, we found that it was influenced according to the feature of the task. However, most of the participants prefer to do touch method overall. Specially the result of this research reveals that the novice group prefers the pen input method. We expect to suggest the design guideline for product development to extend pen input method in smartphone.

• Journal of KIISE
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• v.42 no.1
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• pp.122-129
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• 2015

This paper proposes a method for providing the information about multimedia streams for telepresence services to heterogeneous services such as IMS (IP Multimedia Subsystem) and RTCWeb (Real-Time Communication in WEB-browsers). First of all, we design an interworking gateway for each service and suggest a procedure for providing the information about multimedia streams, which is defined by CLUE, a working group for standardization, to the heterogeneous services. We also apply the method of the actual CLUE information exchange and implement it in our experiment environment. Finally, we show that the proposed method can exchange more information than previous methods even though the media session re-establishment time is similar to legacy systems in terms of performance analysis. With the proposed method, the heterogeneous services can collect a variety of information about the telepresence service and use it according to user preference. In this way it provides rich multimedia streaming services for many areas.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.16 no.6
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• pp.77-91
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• 2013

The researcher noted the fact that wetland users are more and more diversified while people are more conscious of their ecological importance. Wetlands tend to be very sensitive in ecological terms, and therefore, they can hardly accommodate their users' needs indefinitely. With such basic perception in mind, the purpose of this study was to survey wetland users' eco-consciousness, determine their traits, analyze the corelation between their traits and preferences of wetland amenities, and thereby, provide the data useful to planning of an effective wetland management policy. To this end, the researcher sampled nation's largest wetland, Upo Marsh located in Changnyeong for a questionnaire survey. Wetland users' eco-consciousness was measured, using Dunlap's NEP (New Ecological Paradigm) approved by many researchers. Wetland users' preferences of the wetland amenities were measured, centered around 11 amenity types observed commonly at the domestic wetlands. As a result of the survey conducted in October, 2012, a total of 228 effective samples were acquired. Wetland users' eco-consciousness was higher than normal, scoring 3.45 on the 5-point scale consisting of 5 sub-scales. In particular, users were more conscious of 'the possibility of an eco-crisis,' while being less conscious of 'ejection of exemptionalism.' As a result of classifying the users into 3 sub-groups in reference to their eco-consciousness and analyzing their preferences of amenities comparatively, significant differences were found in all 3 sub-areas. In particular, the sub-group most eco-conscious tended to prefer the learning amenities, but the least eco-conscious sub-group tended to prefer the utilities. As a result of the post-hoc test, it was found that most and normal eco-conscious sub-groups were more or less homogeneous, while the least eco-conscious sub-group was significantly different from the former 2 sub-groups in terms of eco-consciousness. As the wetland users were found to be diversified in terms of their eco-consciousness, it is necessary to plan the wetland management policies in consideration of such differences. However, it is perceived that the wetland amenities need to be built to meet the more eco-conscious users.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.137-148
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• 2014

Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used