• Korean Journal of Clinical Pharmacy
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• v.25 no.1
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• pp.27-33
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• 2015

Background: Since November 2012, some of over-the-counter (OTC) medications have been sold in convenience store without pharmacist' s supervision. We purposed to examine if the product labels of OTCs provide sufficient information that is appropriate for consumers who may have low health literacy. Methods: We compared the difficulty of words that are utilized in pharmaceutical product labels of interest (intervention) with those in the $6^{th}$ grade textbook (control). Pharmaceutical products of interest were comprised of 13 OTCs which have been sold currently in convenience stores. We grouped words into the 4 levels of difficulty based on the Korean Vocabulary Classification for Education, and statistically tested words frequency in each level between OTCs and control. Results: The 13 OTC labels included lay language (easier or equal to language used in primary school) about 10% less; professional language about 10% more (p < 0.001 in all). Labels for analgesics had the longest and most difficult information, followed by common cold preparations, muscle pain relievers as plaster or cataplasma and digestives. Conclusion: The 13 OTC labels might fail to provide appropriate information for safety use by consumers in terms of the difficulty level of words. The improvement of labels of OTC medications and consumer education strategies are called for safety use of OTC medications sold in convenience stores.

• Journal of Korea Multimedia Society
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• v.17 no.9
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• pp.1125-1133
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• 2014

Recently, there are many researches have been studying for analyzing user interests and emotions based on users profiles and diverse information from Social Network Services (SNSs) due to their popularities. However, most of traditional researches are focusing on their researches based on single resource such as text, image, hash tag, and more, in order to obtain what user emotions are. Hence, this paper propose a method for obtaining user emotional information by analyzing texts and images both from Instagram which is one of the well-known image based SNSs. In order to extract emotional information from given images, we firstly apply GRAB-CUT algorithm to retrieve objects from given images. These retrieved objects will be regenerated by their representative colors, and compared with emotional vocabulary table for extracting which vocabularies are the most appropriate for the given images. Afterward, we will extract emotional vocabularies from text information in the comments for the given images, based on frequencies of adjective words. Finally, we will measure WUP similarities between adjective words and emotional words which extracted from the previous step. We believe that it is possible to obtain more precise user emotional information if we analyzed images and texts both time.

• Journal of the Korea Furniture Society
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• v.23 no.2
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• pp.185-194
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• 2012

Contemporary society maintains mass-product system that keeps endless cycle of making and consuming. In this vein, everyday life becomes to be under the control of function and efficiency. On the contrary, the people are getting to have a desire of escaping from this everydayness, that is, the desire for non-everydayness. British architect, Nigel Coates understood the potentiality of contemporary metropolis which produce new experiences through their heterogeneities. During 1980s, Japanese economic bubble provided rich nourishment to the desire for non-everydayness based on consumers' tastes. Nigel Coates snatched this phenomena and designed commercial spaces aligned to the non-everydayness. He shows very eloquent version of escaping sense. We can find the exquisite quality of non-everydayness through design vocabulary of object's form and arrangement. In the viewpoint of object form, Coates adopted classical statues of Greek, that is antique, and modern gadgets such as airplane wings and seats. Also, we can find abundant gestures of curvilineal contours throughout the objects he designed. As for the objects' arrangement, he introduced repetition and curved composition that can stimulate human interaction with interior scape.

• Journal of the Korean Chemical Society
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• v.61 no.4
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• pp.218-222
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• 2017

Game shows are capable of grabbing students' attention in a lecture, and at the same time offers much delight to students to learn in a more interesting and entertaining way. A mind game was developed and incorporated into the teaching of basic organic chemistry course at the Universiti Malaysia Terengganu. The modified brainteaser game is comprised of a set of problems associated with vocabulary or concepts in basic organic chemistry, whereby students were required to solve within a stipulated time frame. Students' attitude changes were then evaluated with the administration of Attitude toward the Subject of Chemistry Inventory Version 2 (ASCIv2) questionnaire. The result of this study revealed that the intervention group experienced a significant change in attitude towards the course as compared to the control group. The intervention group also experienced a positive learning environment, resulting in an increased academic performance and interest.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1391-1399
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• 2015

In recent times, studies sentiment analysis are being actively conducted by implementing natural language processing technologies for analyzing subjective data such as opinions and attitudes of users expressed on the Web, blogs, and social networking services (SNSs). Conventionally, to classify the sentiments in texts, most studies determine positive/negative/neutral sentiments by assigning polarity values for sentiment vocabulary using sentiment lexicons. However, in this study, sentiments are classified based on Thayer's model, which is psychologically defined, unlike the polarity classification used in opinion mining. In this paper, as a method for classifying the sentiments, sentiment categories are proposed by extracting sentiment keywords for major sentiments by using hashtags, which are essential elements of Instagram. By applying sentiment categories to user posts, sentiments can be determined through the similarity measurement between the sentiment adjective candidates and the sentiment keywords. The test results of the proposed method show that the average accuracy rate for all the sentiment categories was 90.7%, which indicates good performance. If a sentiment classification system with a large capacity is prepared using the proposed method, then it is expected that sentiment analysis in various fields will be possible, such as for determining social phenomena through SNS.

• Speech Sciences
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• v.11 no.2
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• pp.181-192
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• 2004

This paper presents an efficient speech interactive agent rendering smooth car navigation and Telematics services, by employing embedded automatic speech recognition (ASR), distributed speech recognition (DSR) and text-to-speech (ITS) modules, all while enabling safe driving. A speech interactive agent is essentially a conversational tool providing command and control functions to drivers such' as enabling navigation task, audio/video manipulation, and E-commerce services through natural voice/response interactions between user and interface. While the benefits of automatic speech recognition and speech synthesizer have become well known, involved hardware resources are often limited and internal communication protocols are complex to achieve real time responses. As a result, performance degradation always exists in the embedded H/W system. To implement the speech interactive agent to accommodate the demands of user commands in real time, we propose to optimize the hardware dependent architectural codes for speed-up. In particular, we propose to provide a composite solution through memory reconfiguration and efficient arithmetic operation conversion, as well as invoking an effective out-of-vocabulary rejection algorithm, all made suitable for system operation under limited resources.

• Journal of Information Management
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• v.32 no.2
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• pp.26-39
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• 2001

Various controlled vocabularies such as thesaurus and classification used for effective information retrieval contain different terms in expressing the same concept or meaning. National Library of Medicine has developed the Unified Medical Language System(UMLS) to solve the problems of information retrieval and integration resulted from the difference of concepts between different sources. The UMLS development was initiated in 1982 as a long-term project, and the 2001 edition of the UMLS consists of three parts : Metathesaurus, Semantic Network, and SPECIALIST Lexicon. This paper reviews background and structure of the UMLS including applications in PubMed, NLM Gateway.

• Phonetics and Speech Sciences
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• v.9 no.2
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• pp.125-135
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• 2017

The current study examines PCC (percentage of correct consonant), speech intelligibility, and oral motor function between the group of typically developing children and the group of children with Down's syndrome. To 15 children with Down's syndrome (mean CA: 9;7) and 15 typically developing children on receptive language age, the following tests were administered: K-WPPSI (2001), Picture Vocabulary Test (Kim et al., 1995), Oral and Speech Motor Control Protocol for total oral functional score (Robbins et al., 1987), DDK and Assessment of Phonology and Articulation for Children (APAC, Kim et al., 2007) for PCC and speech intelligibility. Pearson correlation coefficients were computed for the total oral functional score, PCC and DDK of each group. The statistical analysis showed that there is no significant difference in total functional score and DDK when IQ was controlled. There was a significant correlation between total oral functional score and PCC in the Down's syndrome group and a significant correlation between total oral functional score and intelligibility in the Down's syndrome group whether IQ was controlled or not. The findings suggest that both cognitive ability and overall oral motor function need to be considered for the intervention to enhance PCC or speech intelligibility of children with Down's syndrome.

• Science of Emotion and Sensibility
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• v.15 no.4
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• pp.503-516
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• 2012

According to the researches of robot and software agent until now, computational emotion model is dependent on system, so it is hard task that emotion models is separated from existing systems and then recycled into new systems. Therefore, I introduce the Engine of computational Emotion model (shall hereafter appear as EE) to integrate with any robots or agents. This is the engine, ie a software for independent form from inputs and outputs, so the EE is Emotion Generation to control only generation and processing of emotions without both phases of Inputs(Perception) and Outputs(Expression). The EE can be interfaced with any inputs and outputs, and produce emotions from not only emotion itself but also personality and emotions of person. In addition, the EE can be existed in any robot or agent by a kind of software library, or be used as a separate system to communicate. In EE, emotions is the Primary Emotions, ie Joy, Surprise, Disgust, Fear, Sadness, and Anger. It is vector that consist of string and coefficient about emotion, and EE receives this vectors from input interface and then sends its to output interface. In EE, each emotions are connected to lists of emotional experiences, and the lists consisted of string and coefficient of each emotional experiences are used to generate and process emotional states. The emotional experiences are consisted of emotion vocabulary understanding various emotional experiences of human. This study EE is available to use to make interaction products to response the appropriate reaction of human emotions. The significance of the study is on development of a system to induce that person feel that product has your sympathy. Therefore, the EE can help give an efficient service of emotional sympathy to products of HRI, HCI area.

• Smart Media Journal
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• v.12 no.11
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• pp.185-197
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• 2023

This study was conducted using text mining and network theory to extract useful information for application for occupancy and performance of permit tasks contained in the permit contents from the permit register, which is used only for the simple purpose of recording occupancy permit information. Based on text mining, we analyzed and compared the frequency of vocabulary occurrence and topic modeling in five regions, including Seoul, Gyeonggi, Gyeongsang, Jeolla, Chungcheong, and Gangwon, as well as normalization processes such as stopword removal and morpheme analysis. By applying four types of centrality algorithms, including stage, proximity, mediation, and eigenvector, which are widely used in network theory, we looked at keywords that are in a central position or act as an intermediary in the network. Through a comprehensive analysis of vocabulary appearance frequency, topic modeling, and network centrality, it was found that the 'installation' keyword was the most influential in all regions. This is believed to be the result of the Ministry of Environment's permit management office issuing many permits for constructing facilities or installing structures. In addition, it was found that keywords related to road facilities, flood control facilities, underground facilities, power/communication facilities, sports/park facilities, etc. were at a central position or played a role as an intermediary in topic modeling and networks. Most of the keywords appeared to have a Zipf's law statistical distribution with low frequency of occurrence and low distribution ratio.