• Journal of Digital Contents Society
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• v.19 no.6
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• pp.1051-1060
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• 2018

This study aims to propose instituting the serious games of language-based cognitive enhancement for the BIF children. The program consists of 4 cognitive areas (perception, attention, working memory, knowledge inference) in 4 language dimensions (phoneme, word, sentence, discourse). 16 games of 4 areas/2 dimensions with 3 difficulty levels were implemented in a mobile station and pilot-tested to children including BIFs. The results from the pilot tests supported for the validity and effectiveness of our games: Children's game performance correlated with their IQ scores (overall and sub-areas) revealing significant differences between the groups. The stroop scores in pre-and-post training hinted the increase of children's cognitive control.

• Journal of KIISE
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• v.44 no.2
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• pp.163-170
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• 2017

Despite the rise of studies in spoken to sign language translation, low-resource problems of sign language corpus have been rarely addressed. As a first step towards translating from spoken to sign language, we addressed the problems arising from resource scarcity when translating spoken language to manual signals translation using statistical machine translation techniques. More specifically, we proposed three preprocessing methods: 1) paraphrase generation, which increases the size of the corpora, 2) lemmatization, which increases the frequency of each word in the corpora and the translatability of new input words in spoken language, and 3) elimination of function words that are not glossed into manual signals, which match the corresponding constituents of the bilingual sentence pairs. In our experiments, we used different types of English-American sign language parallel corpora. The experimental results showed that the system with each method and the combination of the methods improved the quality of manual signals translation, regardless of the type of the corpora.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.659-671
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• 2003

This paper proposes a method of disambiguating the senses of words using decision lists, which consists of rules with confidence values. The rule of decision list is composed of a boolean function(=precondition) and a class(=sense). Decision lists classify the instance using the rule with the highest confidence value that is matched with it. Previous work disambiguated the senses using single feature decision lists, whose boolean function was composed of only one feature. However, this approach can be affected more severely by data sparseness problem and preprocessing errors. Hence, we propose multiple feature decision lists that have the boolean function consisting of more than one feature in order to identify the senses of words. Experiments are performed with 1 sense tagged corpus in Korean and 5 sense tagged corpus in English. The experimental results show that multiple feature decision lists are more effective than single feature decision lists in disambiguating senses.

• The Journal of the Acoustical Society of Korea
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• v.14 no.5
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• pp.74-82
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• 1995

This paper is study on the voice dialing using HMM and post processing of the connected digits. HMM algorithm is widely used in the speech recognition with a good result. But, the maximum likelihood estimation of HMM(Hidden Markov Model) training in the speech recognition does not lead to values which maximize recognition rate. To solve the problem, we applied the post processing to segmental K-means procedure are in the recognition experiment. Korea connected digits are influenced by the prolongation more than English connected digits. To decrease the segmentation error in the level building algorithm some word models which can be produced by the prolongation are added. Some rules for the added models are applied to the recognition result and it is updated. The recognition system was implemented with DSP board having a TMS320C30 processor and IBM PC. The reference patterns were made by 3 male speakers in the noisy laboratory. The recognition experiment was performed for 21 sort of telephone number, 252 data. The recognition rate was $6\%$ in the speaker dependent, and $80.5\%$ in the speaker independent recognition test.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.4
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• pp.2223-2227
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• 2016

Background: Breast cancer is the most common cancer among Iranian women. Since development of the disease in Iranian women occurs relatively early, the survival rate matters considerably. In different countries, survival of breast cancer patients varies considerably. Therefore, the one-year, three-year, five-year, and ten-year survival rates for breast cancer in Iran were assessed using a meta-analysis. Materials and Methods: This systematic review and meta-analysis was based on valid Iranian sources including SID, MAGIRAN and IRANMEDEX, along with reliable English databases, namely, PUBMED and SCOPUS. In domestic databases, a search was conducted based on key words of breast cancer and survival rate, and in international databases, with "breast cancer" and the equivalent of "neoplasm" of Mesh Word, "survival rate" and "Iran." Then all reviewed papers and theses which met the inclusion criteria were selected for investigation. To conduct the analysis STATA 11.2 software and random-effects models were used. Results: In 24 studies, 22,745 participants were included. The one-year, three-year, five-year and ten-year survival rates were 0.956, 0.808, 0.695 and 0.559, respectively. The minimum and maximum survival rates for 5-years were 0.48 and 0.87. The average age of the onset of the disease was 48.3. Conclusions: As in Iran, since the onset of the disease is at low age, in spite of the relatively high survival rate as compared to other cancers, prevention and screening programs at early age for early stage diagnosis seems necessary.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.8
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• pp.5565-5570
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• 2015

Implicit citation sentence recognition is to locate citation sentences which lacks explicit citation markers, from articles' full-text. State-of-the-art approaches exploit word ngrams, clue words, researcher's surnames, mentions of previous methods, and distance relative to nearest explicit citation sentences, etc., reaching over 50% performance. However, most previous works have been conducted on English. As for Korean, a rule-based method using positive/negative clue patterns was reported to attain the performance of 42%, requiring further improvement. This study attempted to learn to recognize implicit citation sentences from Korean literatures' full-text using Korean lexical features. Different lexical feature units such as Eojeol, morpheme, and Eumjeol were evaluated to determine proper lexical features for Korean implicit citation sentence recognition. In addition, lexical features were combined with the position features representing backward/forward proximities to explicit citation sentences, improving the performance up to over 50%.

• Journal of KIISE:Computer Systems and Theory
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• v.33 no.1_2
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• pp.52-61
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• 2006

A crossword puzzle is one of the popular word games around the world in which you work out the answers and write them in the white squares of a pattern of small black and white squares. As the technology of computers develops, some people worked about making and solving crossword puzzle games, which led them to a commercial use. However, almost all of these commercial programs are ones where you do ready-made puzzles with a fixed size because it is very difficult to make puzzles in a certain size, picking up some among a great number of words to fit for them. Furthermore, these programs are only for a very few languages, such as English, French, not for Korean. Accordingly, ore took a look at what should be considered to make an automatic puzzle-generating program for Korean, and in this paper we implemented Korizzle, a system making the puzzles automatically. We introduce the algorithm used for Korizzle and evaluate the its performance.

• Journal of Digital Contents Society
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• v.10 no.1
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• pp.17-26
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• 2009

Recently, an interest in mobile games is increasing according to the extension of the high speed network infra and the development of mobile devices. Specially, the mobile game for learning can help to reinforce an academic performance and an interest for a brief time anytime anywhere. Thus, we propose new mobile contents named V-Killer which combines learning with a game. V-Killer is a word puzzle game which has functions of ranking and searching. The game can get feedback on your learning or progress and choose the degree of difficulty according to the ability of the user. The game lead to an interaction of user and games as sets questions by user, in addition, it is easy to operate and has a simple construction. In the paper, we implement the proposed game on the mobile and present the game.

• Journal of Korea Society of Industrial Information Systems
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• v.8 no.2
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• pp.14-20
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• 2003

This paper presents a text extraction method of camera based document image. It is more difficult to recognize camera based document image in comparison with scanner based image because of segmentation problem due to variable lighting condition and versatile fonts. Both document binarization and character extraction are important processes to recognize camera based document image. After converting color image into grey level image, gray level normalization is used to extract character region independent of lighting condition and background image. Local adaptive binarization method is then used to extract character from the background after the removal of noise. In this character extraction step, the information of the horizontal and vertical projection and the connected components is used to extract character line, word region and character region. To evaluate the proposed method, we have experimented with documents mixed Hangul, English, symbols and digits of the ETRI database. An encouraging binarization and character extraction results have been obtained.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.2
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• pp.131-136
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• 2016

An appropriate evaluation on learner's pronunciation has been an important part of foreign language education. The learners should be evaluated and receive proper feedback for pronunciation improvement. Due to the cost and consistency problem of human evaluation, automatic pronunciation evaluation system has been studied. The most of the current automatic evaluation systems utilizes underlying Automatic Speech Recognition (ASR) technology. We suggest in this work to evaluate learner's pronunciation accuracy and fluency in word-level using the ASR and non-native teacher's speech model. Through the performance evaluation on our system, we confirm the overall evaluation result of pronunciation accuracy and fluency actually represents the learner's English skill level quite accurately.