• Journal of Information Technology Services
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• v.13 no.3
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• pp.323-331
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• 2014

This research is based on real-time service model that uses lock screen of smart devices which is mostly exposed to device users. The potential for lock screen space is immense due to their exposing time for user. The effect can be maximized by offering useful information contents on lock screen. This service model offers real-time keyword with abridged sentence. They match real-time keyword with news by using text matching algorithm and extracts kernel sentence from news to provide short sentence to user. News from the lock screen to match real-time query sentence, and then only to the original core of the ability to move a user evaluation was conducted after adding. The report provided a key statement users feel the lack of original Not if you go to an average of 5.71%. Most algorithms allow only real-time zoom key sentence extracted keywords can accurately determine the reason for that was confirmed.

• International journal of advanced smart convergence
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• v.7 no.4
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• pp.147-153
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• 2018

This study is about the interactive self-portrait which provides the experience of self-consciousness reflection of the viewer to modern people who are easily alienated in rapid social change. We proposed interactive self-portrait is implemented by an interactive mirror that reproduces the appearance of the viewer acquired using a webcam. The interactive mirror, which can directly project its own image, is drawn by searching news articles in real time and using the extracted characters as pixel information in real time. The viewer has the opportunity to experience a new style of active self-expression while watching his/herself composed of news characters that are issues of modern society. The virtual self-portrait designed with news characters can attract viewers' attention by visually expressing the interests of modern people and can act as an incentive to generate positive interaction.

• Journal of Digital Contents Society
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• v.19 no.4
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• pp.631-637
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• 2018

In order to read newspaper articles, the use of paper newspapers is decreasing and smartphone are increasingly used. As a result, the number of news apps continues to increase. Many of the news apps in the Android Play Store fall into two categories. The first is an app that is developed by a specific newspaper company and distributes only the articles of the newspaper company. The rest is an app that shows a list of newspapers and shows the homepage when a newspaper is selected. In this paper, we have designed and implemented a Real-Time News app for collecting articles from many newspapers and providing them in real time. Newspapers provide up-to-date articles with RSS feeds. The server program stores them in the DB, and transmits the articles requested in the Real-Time News app in real time. In order to see the latest news, it is possible to collect the articles of each newspaper without visiting the websites of the various newspapers, and it is possible to reduce the mobile data usage used to access each website.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.556-578
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• 2020

There are many different researches about how to analyze issues based on real-time news streams. But, there are few researches which analyze issues hierarchically from news articles and even a previous research of hierarchical issue analysis make clustering speed slower as the increment of news articles. In this paper, we propose a hierarchical and incremental clustering for semi real-time issue analysis on news articles. We trained siamese neural network based weighted cosine similarity model, applied this model to k-means algorithm which is used to make word clusters and converted news articles to document vectors by using these word clusters. Finally, we initialized an issue cluster tree from document vectors, updated this tree whenever news articles happen, and analyzed issues in semi real-time. Through the experiment and evaluation, we showed that up to about 0.26 performance has been improved in terms of NMI. Also, in terms of speed of incremental clustering, we also showed about 10 times faster than before.

• Proceedings of the KSPS conference
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• 2006.05a
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• pp.89-93
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• 2006

This paper presents ETRI broadcast news speech recognition system. There are two major issues on the broadcast news speech recognition: 1) real-time processing and 2) out-of-vocabulary handling. For real-time processing, we devised the dual decoder architecture. The input speech signal is segmented based on the long-pause between utterances, and each decoder processes the speech segment alternatively. One decoder can start to recognize the current speech segment without waiting for the other decoder to recognize the previous speech segment completely. Thus, the processing delay is not accumulated. For out-of-vocabulary handling, we updated both the vocabulary and the language model, based on the recent news articles on the internet. By updating the language model as well as the vocabulary, we can improve the performance up to 17.2% ERR.

• Journal of Korea Multimedia Society
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• v.21 no.3
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• pp.415-422
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• 2018

Advances of telecommunication technology bring various changes in journalism field. Reporters started to gather, edit, and transmit content to main server in media company using hand-held smart media and notebook computer. This paper tried to testify valid bit-rate of visual news content using LTE network and mobile phone. Field news like natural disasters need real-time transmission of video content. But broadcasting company normally use heavy ENG system and transmission satellite trucks. We prepared and experimented different types of visual content that has different bit-rates. Transmission tool was LU-60HD mobile system of LiveU Corporation. Transmission result shows that bit-rate of 2Mbps news content is not suitable for broadcasting and VBR (Variable Bit Rate) transmission has better definition quality than CBR (Constant Bit Rate) method. Three different bit-rate of VBR transmission result shows that 5Mbps clip has better quality than 1Mbps and 3Mbps. The higher bit-rate, the better video quality. But if the content has much movements, that cause delay and abnormal quality of video. So optimizing the balance between stability of signal and quality of bit-rate is crucial factor of real-time broadcasting news gathering business.

• KIPS Transactions on Computer and Communication Systems
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• v.7 no.10
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• pp.251-258
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• 2018

Ransomware, a malicious software that requires a ransom by encrypting a file, is becoming more threatening with its rapid propagation and intelligence. Rapid detection and risk analysis are required, but real-time analysis and reporting are lacking. In this paper, we propose a ransomware infection detection system using social big data mining technology to enable real-time analysis. The system analyzes the twitter stream in real time and crawls tweets with keywords related to ransomware. It also extracts keywords related to ransomware by crawling the news server through the news feed parser and extracts news or statistical data on the servers of the security company or search engine. The collected data is analyzed by data mining algorithms. By comparing the number of related tweets, google trends (statistical information), and articles related wannacry and locky ransomware infection spreading in 2017, we show that our system has the possibility of ransomware infection detection using tweets. Moreover, the performance of proposed system is shown through entropy and chi-square analysis.

• International Journal of Advanced Culture Technology
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• v.6 no.2
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• pp.65-71
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• 2018

In the digital era, news consumption is not confined in geological boundaries. Technological advances bring the instant dissemination of news into life and allow news audience to consume events that occur far away almost in real time. The transmission has blurred the boundary between traditional media and new media, and the one between physical and virtual world. That is, what if a journalist applies news framing to the news translation process? This paper aims to investigate the gap between the ST and the TT created when the source news texts undergo a translation process. To achieve this aim, the appraisal theory developed by White (2003) is employed to identify a difference between the ST and the TT. Furthermore, we have attempted to identify differences between soft news stories and hard news stories while the STs from both news stories are translated into the TTs. Two time-sensitive events, Hugh Grant's marriage and a U.S. and North Korea summit, were selected. The former (a soft news story) is extracted from the Telegraph and the latter (a hard news story) is from the Washington post. As a result, it was found that such strategies as attitude, engagement, and judgment were used when the source news texts from the hard news story are translated into the target news texts. Under the appraisal theory, the strategies involve evaluative language which refers to positive or negative language that judges the worth of entities. In general, it is said that a journalist frames the SS (especially from the hard news story) to convey his ideology to news consumers. Hypothetically, we assume that a similar framing process takes place in deriving the TT from the SS of the hard news story. Thus, we could conclude that the TT from the hard news story differs from the TT from the soft news story and that the difference can be explained within the framework of White's appraisal theory.

• Speech Sciences
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• v.3
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• pp.119-131
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• 1998

We have developed a keyword spotting system for automatic speech editing. This system recognizes the only keyword 'MBC news' and then sends the time information to the host system. We adopted a vocabulary dependent model based on continuous hidden Markov model, and the Viterbi search was used for recognizing the keyword. In recognizing the keyword, the system uses a parallel network where HMM models are connected independently and back-tracking information for reducing false alarms and missing. We especially focused on implementing a stable and practical real-time system.

• Smart Media Journal
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• v.6 no.3
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• pp.41-48
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• 2017

Event detection using social media has been widespread since social network services have been an active communication channel for connecting with others, diffusing news message. Especially, the real-time characteristic of social media has created the opportunity for supporting for real-time applications/systems. Social network such as Twitter is the potential data source to explore useful information by mining messages posted by the user community. This paper proposed a novel system for temporal event detection by analyzing social data. As a result, this information can be used by first responders, decision makers, or news agents to gain insight of the situation. The proposed approach takes advantages of deep learning methods that play core techniques on the main tasks including informative data identifying from a noisy environment and temporal event detection. The former is the responsibility of Convolutional Neural Network model trained from labeled Twitter data. The latter is for event detection supported by Recurrent Neural Network module. We demonstrated our approach and experimental results on the case study of earthquake situations. Our system is more adaptive than other systems used traditional methods since deep learning enables to extract the features of data without spending lots of time constructing feature by hand. This benefit makes our approach adaptive to extend to a new context of practice. Moreover, the proposed system promised to respond to acceptable delay within several minutes that will helpful mean for supporting news channel agents or belief plan in case of disaster events.