• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.714-716
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• 2020

We propose a unique client-driven music genre classification solution, that can identify the music genre using a deep convolutional neural network operating on the time-domain signal. The proposed method uses the client device (Jetson TX2) computational resources to identify the music genre. We use the industry famous GTZAN genre collection dataset to get reliable benchmarking performance. HTTP live streaming (HLS) client and server sides are designed locally to validate the effectiveness of the proposed method. HTTP persistent broadcast connection is adapted to reduce corresponding responses and network bandwidth. The proposed model can identify the genre of music files with 97% accuracy. Due to simplicity and it can support a wide range of client hardware.

• Journal of Intelligence and Information Systems
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• v.22 no.2
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• pp.97-113
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• 2016

Due to the growth of internet data and the rapid development of internet technology, "big data" analysis is actively conducted to analyze enormous data for various purposes. Especially in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of existing structured data analysis. Various studies on sentiment analysis, the part of text mining techniques, are actively studied to score opinions based on the distribution of polarity of words in documents. Usually, the sentiment analysis uses sentiment dictionary contains positivity and negativity of vocabularies. As a part of such studies, this study tries to construct sentiment dictionary which is customized to specific data domain. Using a common sentiment dictionary for sentiment analysis without considering data domain characteristic cannot reflect contextual expression only used in the specific data domain. So, we can expect using a modified sentiment dictionary customized to data domain can lead the improvement of sentiment analysis efficiency. Therefore, this study aims to suggest a way to construct customized dictionary to reflect characteristics of data domain. Especially, in this study, movie review data are divided by genre and construct genre-customized dictionaries. The performance of customized dictionary in sentiment analysis is compared with a common sentiment dictionary. In this study, IMDb data are chosen as the subject of analysis, and movie reviews are categorized by genre. Six genres in IMDb, 'action', 'animation', 'comedy', 'drama', 'horror', and 'sci-fi' are selected. Five highest ranking movies and five lowest ranking movies per genre are selected as training data set and two years' movie data from 2012 September 2012 to June 2014 are collected as test data set. Using SO-PMI (Semantic Orientation from Point-wise Mutual Information) technique, we build customized sentiment dictionary per genre and compare prediction accuracy on review rating. As a result of the analysis, the prediction using customized dictionaries improves prediction accuracy. The performance improvement is 2.82% in overall and is statistical significant. Especially, the customized dictionary on 'sci-fi' leads the highest accuracy improvement among six genres. Even though this study shows the usefulness of customized dictionaries in sentiment analysis, further studies are required to generalize the results. In this study, we only consider adjectives as additional terms in customized sentiment dictionary. Other part of text such as verb and adverb can be considered to improve sentiment analysis performance. Also, we need to apply customized sentiment dictionary to other domain such as product reviews.

• The Journal of Society for e-Business Studies
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• v.19 no.3
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• pp.125-141
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• 2014

Nowadays, overflowing data produced every second from the internet make people to be difficult to search for the useful information. That's why people have invented and developed unique tools that they get some relevant information. In this paper, the recommender system, one of the effective tools, is used and it helps us to get the useful information that we want by using demographic information to predict new items of interest. The demographic recommender system in this paper computes users' similarity using demographic information, age and gender. So we performed demographic analysis on movie ratings on Internet Movie Database (IMDB) web site that movies are rated by thousands of people, where users submitted a movie rating after they watched a recent popular film. Meanwhile, we can understand that user's ratings, among various determinants of box office, is very essential factor in the study on recommendation of movie. This paper is aimed at analyzing movie average ratings directly given by film viewers, categorizing them into groups by sex and age, investigating the entire group and finding the representative group by examining it with F-test and T-test. This result is used to promote and recommend for the target group only. Therefore, this study is considerably significant as presenting utilization for movie business as well as showing how to analyze demographic information on movie ratings on the web.

• Journal of Digital Convergence
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• v.17 no.12
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• pp.467-472
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• 2019

Growing smartphone usage in South Korea has also accelerated the pace of development of mobile games, but competition is intensifying as the market grows. As one of the factors for the success of the game in this mobile game market, UI has been presented, suggesting that the design of such mobile game UI should be approached in terms of designing the user's experience, along with its function, aesthetic expression, function-oriented design and information delivery before. In this paper, we propose an effective lobby layout of mobile RPG using experience among UX factors. Through the layout classification of Ernest Adams and Andrew Rollings, we selected 9 mobile RPGs in the 20th place of google play cumulative sales rankings and analyzed the layout of the lobby. As a result, the lobby layout of the game, which led the first market success of the mobile RPG genre, The result was that it became the standard of the boxed game. It can be interpreted that the lobby layout, which is similar to the game used previously by the user, is effective because low entry barriers and learning are unnecessary due to the experience of using the existing RPG. Future studies may produce a common layout of a broad genre if studies are conducted to measure the optimum UX for other genres than RPG.

• Proceedings of the Korean Society of Computer Information Conference
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• 2023.01a
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• pp.39-42
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• 2023

최근 온라인 게임 산업이 급속도로 확장됨과 더불어 Gamebot과 같은 비정상적인 프로그램으로 인한 게임 서비스 피해사례가 급격하게 증가하고 있다. 특히, 대표적인 게임 장르 중 하나인 FPS(First-Person Shooter)에서 Aimbot의 사용은 정상적인 이용자들에게 재미 요소를 잃어버리게 하고 상대적 박탈감을 일으켜 게임의 수명을 줄이는 원인이 된다. 비정상 게임 이용자의 근절을 위해서 메모리 변조 및 불법 변조 프로그램 접근 차단 기법과 불법 프로그램 사용의 패턴 모니터링과 같은 기법들이 제안되었지만, 우회 프로그램 및 새로운 패턴을 이용한 비정상적인 프로그램의 개발에는 취약하다는 단점이 있다. 따라서, 본 논문에서는 정상적인 게임 이용자의 패턴만 학습함으로써 비정상 이용자 검출을 가능하게 하는 딥러닝 기반 단일 클래스 분류 기법을 제안하며, 가장 빈번하게 발생하는 치트(Cheat) 유형인 FPS 게임 내 Aimbot 사용 감지에 초점을 두었다. 제안된 비정상 게임 이용자 감지 시스템은 정상적인 사용자의 마우스 좌표를 데카르트 좌표계(Cartesian coordinates)와 극좌표계(Polar coordinates)의 형태로 패턴을 추출하는 과정과 정상적인 마우스 동작 기록으로 부터 학습된 LSTM 기반 Autoencoder의 복원 에러에 따른 검출 과정으로 구성된다. 실험에서 제안된 모델은 FPS 게임 내 마우스 동작을 기록한 공개 데이터셋인 CSGO 게임 데이터셋으로 부터 학습되었으며, 학습된 모델의 테스트 결과는 데카르트 좌표계로부터 훈련된 제안 모델이 비정상 게임 이용자를 분류하는데 적합함을 입증하였다.

• Journal of Korea Multimedia Society
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• v.13 no.8
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• pp.1256-1262
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• 2010

The game has been rapidly evolving through various media such as computer, console game machine, cellular phone and PMP based on the advanced development of scientific technology. In terms of demand, the interest in and desire to consume the game as a way of spending people's spare time have been on the increase constantly while the level of income has been improved. Eventually, the game has gradually expanded its scope of supply and demand, has established its own status as one of the media that is scientifically-intensive and has been developed into a game industry, a large-scale industry. Unlike image media, the methods of exposure in PPL are varied in accordance with the genre of games. This study divides the causes that have influence over the effect of PPL in the game into the genre of game and the skill of gamer. The results of the experiment on how much the aforementioned two elements have influence over the effect of PPL are as in the following: It has been demonstrated that the effect of PPL could appear different according to the genre of game and the skill of gamer on the game. Besides, the genre of game that is dynamic in its screen change in the game has relatively lower effect of PPL than that is not dynamic. Meanwhile, the persons who are highly skilled in the game have higher degree of recognition and preference to the inserted PPL than those who are lowly skilled. In this regard, it has given us a theoretical ground that the fees system for PPL ads should be established variously in accordance with the genre of game and the level of online game users.

• Cartoon and Animation Studies
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• s.45
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• pp.27-50
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• 2016

The present study argues that documentary-animation films, which are based on actual human voices, on the level of representation, constitute a new expansion for the medium of animation films, which serve as testimonies to the real world. Animation films are produced using very diverse techniques so that they are complex to the degree of being indefinable, and documentary films, though based on objective representation, increase in complexity in that there exist various types of artificial interventions such as direction and digital image processing. Having emerged as a hybrid genre of the two media, documentary-animation films draw into themselves actual events and elements so that they conceptually share reality-based narratives and are visually characterized by the trappings of animation films. Generally classified as 'animated documentaries', this genre triggered discussions following the release of , a work that is mistaken as having used rotoscoping transforming live action in terms of the technique. When analyzed in detail, however, this work is presented as an ambiguous medium where the characteristics of animation films, which are virtual simulacra without reality, and of documentaries, which are based on the objective indexicality of the referents, coexist because of its mixed use of typical animation techniques, 3D programs, and live-action images. Discussed in the present study, , , and share the characteristics of the medium of documentaries in that the narratives develop as testimonies of historical figures but, at the same time, are connected to animation films because of their production techniques and direction characteristics. Consequently, this medium must be discussed as a new expansion rather than being included in the existing classification system, and such a presupposition is an indispensable process for directly facing the reality of the works and for developing discussions. Through works that directly use the interviewees' voices yet do not transcend the characteristics of animation films, the present study seeks to define documentary-animation films and to discuss the possibility of the medium, which has expanded as a testimony to the real world.

• Science of Emotion and Sensibility
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• v.16 no.2
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• pp.207-220
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• 2013

For video reality improvement, there has been much advancement in the methods of providing visual, auditory, and tactile information. On the other hand, there is little research on olfaction for video reality because it is difficult to define and knotty to manipulate. As a first step for video reality improvement using olfactory information, this research investigated users' acceptability of smell when they watch videos of various kinds and then classified the video clips based on their acceptability of different criteria. To do so, we first selected three questions of whether the scene in the video appears to have an odor (odor presence), whether a matching odor is likely to improve a sense of reality (effect on sense of reality), and whether s/he would like a matching odor to be present (preference for the matching odor). Then after collecting 51 video clips of various genres that would receive either high or low ratings for the questions above, we had participants to watch the videos and rate them for the above three questions on 7-point scale. For video classification, we paired each two questions to construct 2D space to draw scatterplot of video clips where the scales of the two questions represent X or Y axis. Clusters of video clips that locate at different quadrants of the 2D space would provide important insights in providing olfactory information for video reality improvement.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.6 no.2
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• pp.95-106
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• 2007

This paper proposes an effective tempo feature extraction method for music information retrieval. The tempo information is modeled by the narrow-band temporal modulation components, which are decomposed into a modulation spectrum via joint frequency analysis. In implementation, the tempo feature is directly extracted from the modified discrete cosine transform coefficients, which is the output of partial MP3(MPEG 1 Layer 3) decoder. Then, different features are extracted from the amplitudes of modulation spectrum and applied to different music information retrieval tasks. The logarithmic scale modulation frequency coefficients are employed in automatic music emotion classification and music genre classification. The classification precision in both systems is improved significantly. The bit vectors derived from adaptive modulation spectrum is used in audio fingerprinting task That is proved to be able to achieve high robustness in this application. The experimental results in these tasks validate the effectiveness of the proposed tempo feature.

• The KIPS Transactions:PartB
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• v.17B no.4
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• pp.263-274
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• 2010

To provide context-aware music recommendation service, first of all, we need to catch music mood that a user prefers depending on his situation or context. Among various music characteristics, music mood has a close relation with people‘s emotion. Based on this relationship, some researchers have studied on music mood detection, where they manually select a representative segment of music and classify its mood. Although such approaches show good performance on music mood classification, it's difficult to apply them to new music due to the manual intervention. Moreover, it is more difficult to detect music mood because the mood usually varies with time. To cope with these problems, this paper presents an automatic method to classify the music mood. First, a whole music is segmented into several groups that have similar characteristics by structural information. Then, the mood of each segments is detected, where each individual's preference on mood is modelled by regression based on Thayer's two-dimensional mood model. Experimental results show that the proposed method achieves 80% or higher accuracy.