• Journal of Multimedia Information System
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• v.2 no.1
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• pp.163-170
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• 2015

Many overlay texts are artificially superimposed on the broadcasting videos by humans. These texts provide additional information to the audiovisual content. Especially, the overlay text in news videos contains concise and direct description of the content. Therefore, it is most reliable clue for constructing a news video indexing system. To make the automatic person indexing of interview video in the TV news program, this paper proposes the method to only detect the name text line among the whole overlay texts in one frame. The experimental results on Korean television news videos show that the proposed framework efficiently detects the overlaid name text line.

• Journal of KIISE:Software and Applications
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• v.36 no.4
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• pp.306-319
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• 2009

A name is a key feature for distinguishing people, but we often fail to discriminate people because an author may have multiple names or multiple authors may share the same name. Such name ambiguity problems affect the performance of document retrieval, web search and database integration. Especially, in bibliography information, a number of errors may be included since there are different authors with the same name or an author name may be misspelled or represented with an abbreviation. For solving these problems, it is necessary to disambiguate the names inputted into the database. In this paper, we propose a method to solve the name ambiguity by using social networks constructed based on the relations between authors. We evaluated the effectiveness of the proposed system based on DBLP data that offer computer science bibliographic information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.10
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• pp.2750-2763
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• 2012

The Domain Name System (DNS) is a critical Internet infrastructure that provides name to address mapping services. In the past decade, Denial-of-Service (DoS) attacks have targeted the DNS infrastructure and threaten to disrupt this critical service. While the flooding DoS attacks may be alleviated by the DNS caching mechanism, we show in this paper that flooding DoS attacks utilizing name error queries is capable of bypassing the cache of resolvers and thereby impose overwhelming flooding attacks on the name servers. We analyze the impacts of such DoS attacks on both name servers and resolvers, which are further illustrated by May 19 China's DNS Collapse. We also propose the detection and defense approaches for protecting DNS servers from such DoS attacks. In the proposal, the victim zones and attacking clients are detected through monitoring the number of corresponding responses maintained in the negative cache. And the attacking queries can be mitigated by the resolvers with a sample proportion adaptive to the percent of queries for the existent domain names. We assess risks of the DoS attacks by experimental results. Measurements on the request rate of DNS name server show that this kind of attacks poses a substantial threat to the current DNS service.

• The Journal of the Acoustical Society of Korea
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• v.28 no.7
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• pp.674-679
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• 2009

This paper proposes a method of player event retrieval using combination of two functions: detection of player name in speech information and detection of sound event from audio information in golf videos. The system consists of indexing module and retrieval module. At the indexing time audio segmentation and noise reduction are applied to audio stream demultiplexed from the golf videos. The noise-reduced speech is then fed into speech recognizer, which outputs spoken descriptors. The player name and sound event are indexed by the spoken descriptors. At search time, text query is converted into phoneme sequences. The lists of each query term are retrieved through a description matcher to identify full and partial phrase hits. For the retrieval of the player name, this paper compares the results of word-based, phoneme-based, and hybrid approach.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.10
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• pp.447-454
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• 2015

This paper proposes geographical name denoising by machine learning of event detection based on twitter. Recently, the increasing number of smart phone users are leading the growing user of SNS. Especially, the functions of short message (less than 140 words) and follow service make twitter has the power of conveying and diffusing the information more quickly. These characteristics and mobile optimised feature make twitter has fast information conveying speed, which can play a role of conveying disasters or events. Related research used the individuals of twitter user as the sensor of event detection to detect events that occur in reality. This research employed geographical name as the keyword by using the characteristic that an event occurs in a specific place. However, it ignored the denoising of relationship between geographical name and homograph, it became an important factor to lower the accuracy of event detection. In this paper, we used removing and forecasting, these two method to applied denoising technique. First after processing the filtering step by using noise related database building, we have determined the existence of geographical name by using the Naive Bayesian classification. Finally by using the experimental data, we earned the probability value of machine learning. On the basis of forecast technique which is proposed in this paper, the reliability of the need for denoising technique has turned out to be 89.6%.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.668-670
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• 1999

We have developed a automatic surface inspection system for cold Rolled strips in steel making process for several years. We have experienced the various kinds of surface inspection systems, including linear CCD camera type and the laser type inspection system which was installed in cold rolled strips production lines. But, we did not satisfied with these inspection systems owing to insufficient detection and classification rate, real time processing performance and limited line speed of real production lines. In order to increase detection and computing power, we have used the Dark Field illumination with Infra_Red LED, Bright Field illumination with Xenon Lamp, Parallel Computing Processor with Area typed CCD camera and full software based image processing technique for the ease up_grading and maintenance. In this paper, we introduced the automatic inspection system and real time image processing technique using the Object Detection, Defect Detection, Classification algorithms. As a result of experiment, under the situation of the high speed processed line(max 1000 meter per minute) defect detection is above 90% for all occurred defects in real line, defect name classification rate is about 80% for most frequently occurred 8 defect, and defect grade classification rate is 84% for name classified defect.

• Advances in concrete construction
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• v.17 no.2
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• pp.53-66
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• 2024

Domain Name Systems (DNS) provide critical performance in directing Internet traffic. It is a significant duty of DNS service providers to protect DNS servers from bandwidth attacks. Data mining techniques may identify different trends in detecting anomalies, but these approaches are insufficient to provide adequate methods for querying traffic data in significant network environments. The patterns can enable the providers of DNS services to find anomalies. Accordingly, this research has used a new approach to find the anomalies using the Neural Network (NN) because intrusion detection techniques or conventional rule-based anomaly are insufficient to detect general DNS anomalies using multi-enterprise network traffic data obtained from network traffic data (from different organizations). NN was developed, and its results were measured to determine the best performance in anomaly detection using DNS query data. Going through the R² results, it was found that NN could satisfactorily perform the DNS anomaly detection process. Based on the results, the security weaknesses and problems related to unpredictable matters could be practically distinguished, and many could be avoided in advance. Based on the R² results, the NN could perform remarkably well in general DNS anomaly detection processing in this study.

• Journal of Korea Multimedia Society
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• v.19 no.8
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• pp.1466-1470
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• 2016

As individuals create many contents anytime and anywhere together with the widespread dissemination of smart devices as well as various social networking services (SNS), content centric networking (CCN) has regarded as a new networking technology. However, CCN is exposed to malicious attacks on the mobility management of mobile content sources during handover and high volume of control messages. Therefore, this paper presents a secure duplicate name detection (SecureDND) mechanism without additional control messages by signed information and secure token. It is shown from the performance evaluation that the proposed scheme can provide low control overhead, which results in the network scalability.

• Journal of Information Processing Systems
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• v.13 no.4
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• pp.716-730
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• 2017

In this paper, we propose a transliteration approach based on semantic information (i.e., language origin and gender) which are automatically learnt from the person name, aiming to transliterate the person name of Uyghur into Chinese. The proposed approach integrates semantic scores (i.e., performance on language origin and gender detection) with general transliteration model and generates the semantic knowledge-based model which can produce the best candidate transliteration results. In the experiment, we use the datasets which contain the person names of different language origins: Uyghur and Chinese. The results show that the proposed semantic transliteration model substantially outperforms the general transliteration model and greatly improves the mean reciprocal rank (MRR) performance on two datasets, as well as aids in developing more efficient transliteration for named entities.

• Science of Emotion and Sensibility
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• v.18 no.1
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• pp.39-48
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• 2015

It is known that P300-based concealed information test (P300 CIT) was not greatly affected by the traditional countermeasures. This study was to test whether P300 CIT is affected by the new countermeasures. We used three types of countermeasures. First type was a sequential countermeasure in which participants had to respond in alternating ways to irrelevants by pressing the left index finger covertly when the encountered irrelevant firstly, by wiggling the right big toe inside the shoe when encountered irrelevant secondly, by imaging his or her mother's name when encountered irrelevant thirdly, and by imaging his or her father's name when encountered irrelevant fourthly until all stimuli were presented. Second type was a partial matching and physical countermeasure. Participants in this type were asked to press the left index finger imperceptibly after one of the irrelevants and wiggle the right big toe after another of the irrelevants. Third type was a partial matching and mental countermeasure. Participants were required to imagine mother's name for one irrelevant and father's name for another irrelevant. The results showed that contrary to our expectation, the use of sequential countermeasure increased the detection rate from 77% to 92%. The partial matching countermeasure had a negative effect on P300 CIT. The physical countermeasure decreased the detection rate from 77% to 46%, and the mental countermeasure decreased the detection rate from 100% to 69%. The necessity for the development of methods to prevent or detect countermeasure is discussed.