• Journal of Broadcast Engineering
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• v.24 no.6
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• pp.1076-1084
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• 2019

In this paper, we introduced a system that extracts metadata by recognizing characters and objects in media using deep learning technology. In the field of broadcasting, multimedia contents such as video, audio, image, and text have been converted to digital contents for a long time, but the unconverted resources still remain vast. Building media archives requires a lot of manual work, which is time consuming and costly. Therefore, by implementing a deep learning-based metadata generation system, it is possible to save time and cost in constructing media archives. The whole system consists of four elements: training data generation module, object recognition module, character recognition module, and API server. The deep learning network module and the face recognition module are implemented to recognize characters and objects from the media and describe them as metadata. The training data generation module was designed separately to facilitate the construction of data for training neural network, and the functions of face recognition and object recognition were configured as an API server. We trained the two neural-networks using 1500 persons and 80 kinds of object data and confirmed that the accuracy is 98% in the character test data and 42% in the object data.

• Journal of KIISE:Databases
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• v.37 no.3
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• pp.127-136
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• 2010

With rapid growth of information and computer communication technologies, the numbers of digital documents including multimedia data have been recently exploded. In particular, news video database and news video mining have became the subject of extensive research, to develop effective and efficient tools for manipulation and analysis of news videos, because of their information richness. However, many research focus on browsing, retrieval and summarization of news videos. Up to date, it is a relatively early state to discover and to analyse the plentiful latent semantic knowledge from news videos. In this paper, we propose the news video mining system based on multi-modal approach and text mining, which uses the visual-textual information of news video clips and their scripts. The proposed system systematically constructs a taxonomy of news video stories in automatic manner with hierarchical clustering algorithm which is one of text mining methods. Then, it multilaterally analyzes the topics of news video stories by means of time-cluster trend graph, weighted cluster growth index, and network analysis. To clarify the validity of our approach, we analyzed the news videos on "The Second Summit of South and North Korea in 2007".

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.9
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• pp.440-446
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• 2016

Military secrets or confidential data of any organization are extremely important assets. They must be discluded from outside. To do this, methods for detecting anomalous attacks and intrusions inside the network have been proposed. However, most anomaly-detection methods only cover aspects of intrusion from outside and do not deal with internal leakage of data, inflicting greater damage than intrusions and attacks from outside. In addition, applying conventional anomaly-detection methods to data exfiltration creates many problems, because the methods do not consider a number of variables or the internal network environment. In this paper, we describe issues considered in data exfiltration detection for anomaly detection (DEDfAD) to improve the accuracy of the methods, classify the methods as profile-based detection or machine learning-based detection, and analyze their advantages and disadvantages. We also suggest future research challenges through comparative analysis of the issues with classification of the detection methods.

• Journal of KIISE:Computing Practices and Letters
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• v.11 no.5
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• pp.401-415
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• 2005

Owing to the improved wireless communication technologies, it is possible to provide streaming service of multimedia with PDAs and mobile phones in addition to desktop PCs. Since mobile client devices have low computing power and low network bandwidth due to wireless network, the transcoding technology to adapt media for mobile client devices considering their characteristics is necessary. Transcoding servers transcode the source media to the target media within corresponding grades and provide QoS in real-time. In particular, an effective load balancing policy for transcoding servers is inevitable to support QoS for large scale mobile users. In this paper, the resource weighted load distribution policy is proposed for a fair load balance and a more scalable performance in cluster-based transcoding servers. Our proposed policy is based on the resource weighted table and number of maximum supported users, which are pre-computed for each pre-defined grade. We implement the proposed policy on cluster-based transcoding servers and evaluate its fair load distribution and scalable performance with the number of transcoding servers.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.9 no.6
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• pp.681-689
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• 2019

The 46th Davos Forum of the World Economic Forum (WEF) predicts the continued growth of the 4th industry in the future. Currently, the 4th industry is attracting attention in various academic and practical fields. As a core technology of the 4th industry, Big Data is regarded as a major resource to lead the 4th industrial revolution along with artificial intelligence. As the growing interest in Big Data, researches on it are actively being done. However, literature studies on existing Big Data are focused on qualitative research, and quantitative research is insufficient. Therefore, this study aims to analyze the big data research flow in MIS field and to make academic thirst for quantification. This study has collected 145 abstracts of big data papers published in major journals in MIS field and confirmed that a majority of papers are published in Decision Support Systems Journal. Text mining and text network analysis were performed only for DSS journals to eliminate bias. As a result of the analysis, it was found out that researches on combining big data in the management field between 2012 and 2014, and researches on system development and analysis method for using big data from 2015 to 2017 were conducted.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.19-36
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• 2023

As advances in information and communication technology have made it easier for anyone to produce and disseminate information, a new problem has emerged: fake news, which is false information intentionally shared to mislead people. Initially spread mainly through text, fake news has gradually evolved and is now distributed in multimedia formats. Since its founding in 2005, YouTube has become the world's leading video platform and is used by most people worldwide. However, it has also become a primary source of fake news, causing social problems. Various researchers have been working on detecting fake news on YouTube. There are content-based and background information-based approaches to fake news detection. Still, content-based approaches are dominant when looking at conventional fake news research and YouTube fake news detection research. This study proposes a fake news detection method based on background information rather than content-based fake news detection. In detail, we suggest detecting fake news by utilizing related video information from YouTube. Specifically, the method detects fake news through CNN, a deep learning network, from the vectorized information obtained from related videos and the original video using Doc2vec, an embedding technique. The empirical analysis shows that the proposed method has better prediction performance than the existing content-based approach to detecting fake news on YouTube. The proposed method in this study contributes to making our society safer and more reliable by preventing the spread of fake news on YouTube, which is highly contagious.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.10
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• pp.603-612
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• 2016

This study will observe the following subjects based on the railroad: First, the development of St. Pancreas Station, which is the gateway to London from other Europe continent, and King's Cross Station, which connects all the intercity within London. Second, the planning characteristic of urban regeneration case which was driven from Camden district, the center of King's Cross Central, which is located in between the two stations, St. Pancreas and King's Cross. Third, based on the the two stations and urban regeneration, this study attempts to investigate the direction of urban regenerating plan and its detailed strategy. As a result, King's Cross Station, St. Pancreas Station and the King's Cross Central area, which is an adjacent area from the two stations, were a slum for a long time. However, the two close stations played a role as the United Kindom and London's railroad network by sharing the common denominator of having the international high-speed railway among the Europe continent and being the connection of National railroads within London. Eventually, based on such potential of railroad traffic, King's Cross Central area was newly regenerated. The consequence of this study has shown that not only the physical modernization of buildings, implementation of compact railroad network supporting both ground and underground of each area or traffic connection was organized in London, but also secured the pedestrian way for easier transfer and planned and allocated facilities by considering citizen's publicness and multilateral use.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.9
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• pp.59-70
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• 2016

The rapid development of the Chinese version of Twitter, the groom Weibo has become an important communication means for Chinese SNS users to obtain and share information. As a result, in China, the phenomenon of the power shift has emerged from the traditional opinion leaders to SNS opinion leasers. The relationship analysis of demographic variables of the Chinese SNS users and their Information on the relationship between keywords was made by utilizing the centrality analysis using Social Network Program NetMiner. China's SNS opinion leaders have general interest in daily activities with their families or friends rather than in social issues. And in case of SNS opinion leaders of high betweenness centrality, it was analyzed that general users was a key mediator role that organically out lead to the adjacent information. These properties are not independent of demographic variables, such as professional, therefore, the demographic characteristics of SNS opinion leaders showed a significant effect on the parameters of betweenness centrality. This study analyzed the characteristics of SNS users, especially opinion leaders in China by looking at the aspects of Chinese social phenomenon in terms of information. Through this study, we expect to provide basic information about the social characteristics of China through collective communication.

• International Journal of Computer Science & Network Security
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• v.24 no.7
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• pp.157-169
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• 2024

The article presents an analysis of innovative teaching technologies as a way to increase students' competitiveness. The author found that innovative technologies in education are information and communication technologies relying on computer-based learning. The structure, content of educational software, organization of Web-space are important when using innovative teaching technologies in English classes. We conducted the study in several stages: comparative analysis, synthesis, classification and systematization of the results of psychological and pedagogical, educational and methodological research; study of legislative acts, periodicals in order to identify the state of the research issue, and determining the directions of its solution, as well as subject, goal and objectives of the study. We used modelling to create situations of foreign language professional communication of future IT specialists. Empirical methods involved questionnaires used for identifying the motives of professional development and determining the features of the educational activities of future IT specialists in the process of training. The methods of mathematical statistics allowed to scientifically describe and systematize the obtained data, to identify the quantitative relationship between the studied phenomena, to analyse and summarize the results. We conducted a socio-psychological study during 2016 - 2019. It involved 255 first- and fourth-year students of National Technical University of Ukraine "Igor Sikorsky Kyiv Poly-technic Institute." Innovative information and communication technologies that improve the educational and cognitive activity of students, as well as increase the level of their knowledge have become important in teaching a foreign language in higher educational institutions. These technologies include MOODLE - Modular Object-Oriented Dynamic Learning Environment, business game, integrated pedagogical technology, case study technology. Thus, the information-rich learning process in combination with the use of innovative technologies, well-organized e-learning, interactive training courses, multimedia tools improves the program of teaching and learning foreign languages in general, and English in particular, improves the level of knowledge of future IT specialists and motivation to study and learn foreign languages, allows students to use a variety of authentic materials. We state that all these factors influence the process of individualization of learning and contribute to the successful mastery of a foreign language.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.5
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• pp.50-60
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• 2010

For the last half a century, the personal computer and software industries have been prosperous due to the incessant evolution of computer systems. In the 21st century, the embedded system market has greatly increased as the market shifted to the mobile gadget field. While a lot of multimedia gadgets such as mobile phone, navigation system, PMP, etc. are pouring into the market, most industrial control systems still rely on 8-bit micro-controllers and simple application software techniques. Unfortunately, the technological barrier which requires additional investment and higher quality manpower to overcome, and the business risks which come from the uncertainty of the market growth and the competitiveness of the resulting products have prevented the companies in the industry from taking advantage of such fancy technologies. However, high performance, low-power and low-cost hardware and software platforms will enable their high-technology products to be developed and recognized by potential clients in the future. This paper presents such a platform for industrial embedded systems. The platform was designed based on Telechips TCC8300 multimedia processor which embedded a variety of parallel hardware for the implementation of multimedia functions. And open-source Embedded Linux, TinyX and GTK+ are used for implementation of GUI to minimize technology costs. In order to estimate the expected performance and power consumption, the performance improvement and the power consumption due to each of enabled hardware sub-systems including YUV2RGB frame converter are measured. An analytic model was devised to check the feasibility of a new application and trade off its performance and power consumption. The validity of the model has been confirmed by implementing a real target system. The cost can be further mitigated by using the hardware parts which are being used for mass production products mostly in the cell-phone market.