• The Journal of the Korea Contents Association
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• v.11 no.2
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• pp.1-12
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• 2011

Movie contents requiring multiple shootings in places need much man power and many equipments. It is more serious in the case of multiple simultaneous shootings. In the points of small broadcasting companies, it is more difficult to cope with the situation, but it is also an essential task for the variety of video contents. We propose an one-person movie production scheme based on an IP camera-based live webcasting system which makes multiple shootings in places possible. It consists of three main functions of (1) collecting multiple video streams sent from IP cameras installed in places, (2) properly distributing them to Internet, and (3) receiving and editing them including recording video. By using the proposed scheme, we can be able to remotely utilize a new framework of Single-Person/ Multiple-Camera which beyonds the conventional Single-Person/Single-Camera framework. It becomes possible to take multiple simultaneous shoots in places. We can get an advantage of saving man power and time for producing various movie contents.

• Journal of the Korea Convergence Society
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• v.9 no.11
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• pp.69-74
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• 2018

Through the convergence of Internet technology and various information technologies, it is possible to collect and process vast amount of information and to exchange various knowledge according to user's personal preference. Especially, there is a tendency to prefer intimate contents connected with the user's preference through the flow of emotional changes contained in the movie media. Based on the information presented in the script, the user seeks to visualize the flow of the entire emotion, the flow of emotions in a specific scene, or a specific scene in order to understand it more quickly. In this paper, after obtaining the raw data from the movie web page, it transforms it into a standardized scenario format after refining process. After converting the refined data into an XML document to easily obtain various information, various sentences are predicted by inputting each paragraph into the emotion prediction system. We propose a system that can easily understand the change of the emotional state between the characters in the whole or a specific part of the various emotions required by the user by mixing the predicted emotions flow and the amount of information included in the script.

• Journal of Digital Convergence
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• v.10 no.9
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• pp.297-309
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• 2012

The MPAA(Motion Picture Association of America) warned about serious problems of piracy due to nearly a quarter of all Internet traffic around the globe was related to Internet piracy. Thus, strong legal action is enforced for piracy over nationally through strengthen the copyright law. We in this paper conducted a survey to see whether netizens prefer to download just for the matter of their convenience. Our study becomes a motivation to consider about seriousness of piracy by comparing between Korea and foreign cases. To our surprise, the survey reveals that Korean netizens conspicuously aware of their downloading behaviors outpaces Canadian netizens. Canada lacks the basic protections for the digital environment and is a safe haven for Internet pirates.

• International journal of advanced smart convergence
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• v.11 no.2
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• pp.118-127
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• 2022

Barrage language is a new media language based on Internet video. It is a representative expression of the Internet environment regardless of its format and content. As a unique movie viewing characteristic provided by the barrage function, the timeliness of feedback, entertainment, and interactivity of the content are excellent. Characteristic. Barrage language itself is closely related not only to the value of linguistic research, but also to the spread of the Internet in the Internet environment. Based on the current situation of Chinese video sites, this thesis explores how Barrage is an organic cycle of culture and consumption in a specific platform and group, and analyzes its propagation methods and effects. By analyzing the characteristics of content production patterns unique to the barrage culture, implications and reference values for production activities of other cultures, the effect of popularization on viewers and the production and consumption of the 'barrage' culture of the industry is studied, and furthermore, the 'barrage' culture is It was designed to be a reference for the development of the platform, internet culture, and animation industry culture.

• International Journal of Internet, Broadcasting and Communication
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• v.7 no.2
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• pp.56-64
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• 2015

The personal image information compiled through closed-circuit television (CCTV) will be open to the internet with the technology such as Long-Tail, Mash-Up, Collective Intelligence, Tagging, Open Application Programming Interface (Open-API), Syndication, Podcasting and Asynchronous JavaScript and XML (AJAX). The movie User Created Contents (UCC) connected to the internet with the skill of web 2.0 has the effects of abuse and threat without precedent. The purpose of this research is to develop the institutional and technological method to reduce these effects. As a result of this research, in terms of technology this paper suggests Privacy Zone Masking, IP Filtering, Intrusion-detection System (IDS), Secure Sockets Layer (SSL), public key infrastructure (PKI), Hash and PDF Socket. While in terms of management this paper suggests Privacy Commons and Privacy Zone. Based on CCTV-UCC linked to the above network, the research regarding personal image information security is expected to aid in realizing insight and practical personal image information as a specific device in the following research.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2948-2966
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• 2014

To promote recommendation services through prediction quality, some privacy-preserving collaborative filtering solutions are proposed to make e-commerce parties collaborate on partitioned data. It is almost probable that two parties hold ratings for the same users and items simultaneously; however, existing two-party privacy-preserving collaborative filtering solutions do not cover such overlaps. Since rating values and rated items are confidential, overlapping ratings make privacy-preservation more challenging. This study examines how to estimate predictions privately based on partitioned data with overlapped entries between two e-commerce companies. We consider both user-based and item-based collaborative filtering approaches and propose novel privacy-preserving collaborative filtering schemes in this sense. We also evaluate our schemes using real movie dataset, and the empirical outcomes show that the parties can promote collaborative services using our schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.8
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• pp.4090-4102
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• 2018

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.3
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• pp.71-76
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• 2019

In this paper, we propose a simple one-click marketing solution for mobile devices which can advertise a product which is embedded in a mobile video while watching the video on a smartphone. If a specific product of interest appears in the video to the user, one can simply click on the product in the video and a pop-up window with information about the product is proposed. The implementation of the system is expected to enable users to gain real-time information about the product while watching the video without having to search for the product again after watching the movie, and thereby facilitating more mobile commerce. We use a two-fold system to prevent the failure of tracking which often occurs on a single online tracking system, so that the user cannot always get the commercial product information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4684-4705
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• 2019

Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

• Journal of Internet Computing and Services
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• v.14 no.5
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• pp.59-67
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• 2013

In this paper, we propose the LAR_CF, latent attribute rating-based collaborative filtering, that is robust to data sparsity problem which is one of traditional problems caused of decreasing rating prediction accuracy. As compared with that existing collaborative filtering method uses a preference rating rated by users as feature vector to calculate similarity between objects, the proposed method improves data sparsity problem using unique attributes of two target objects with existing explicit preference. We consider MovieLens 100k dataset and its item attributes to evaluate the LAR_CF. As a result of artificial data sparsity and full-rating experiments, we confirmed that rating prediction accuracy can be improved rating prediction accuracy in data sparsity condition by the LAR_CF.