• Child Health Nursing Research
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• v.8 no.4
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• pp.458-468
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• 2002

The purpose of this study was to identify metacommunicative behaviors between nurses and patients in a pediatric unit. The research method included observation using videotaping. Data were collected from December, 2001 to February, 2002. Total six nurses, and eight patients and their mothers in a pediatric unit participated in this study. The interactions were videotaped under the participants' consent. The participants were observed for total 8 hours over 2-day period. Special episodes which were identified as metacommunicative behaviors in the taped interactions were transcribed. Transcription included verbal and nonverbal interactions. Selected episodes were classified using Mitchell's definition. Each classified definitions were named, and categorized by its purpose. The results were as follows: Nineteen metacommunicative behaviors which used frequently by nurses-approaching, mediating eye level, eye contact, touching, encouraging, turnabout, mimic voice, giving choices, friendly demand, expansion, tagging, repeating and confirming, identification, reflection, baby talk, symbolization, description of acts, relaxed posture, turning away- were identified and organized into four categories. They were call for attention, facilitating response, empathy, and tension release. In conclusion, nurses in this study used metacommunicative behaviors frequently and these behaviors were effective in interacting with children. It is suggested that any educational programs to teach communication skills to nurses need to include techniques on metacommunicative behaviors. This will help nurses to be more sensitive to different characteristics of their patients.

• Journal of the Korean Society for Library and Information Science
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• v.44 no.4
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• pp.155-178
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• 2010

Assessment of information credibility is a ubiquitous human activity given that people constantly make decisions and selections based on the value of information in a variety of information seeking and use contexts. Today, people are increasingly engaging in diverse online activities beyond searching for and reading information, including activities such as creating, tagging and rating content, shopping, and listening to and watching multimedia content. The Web 2.0 environment presents new challenges for people because the burden of information evaluation is shifted from professional gatekeepers to individual information consumers. At the same time, however, it also provides unprecedented opportunities for people to use tools and features that help them to make informed credibility judgments by relying on other people's ratings and recommendations. This paper introduces fundamental notions and dimensions of credibility, and contends that credibility assessment can be best understood with respect to human information behavior because it encompasses both the level of effort people exert as well as the heuristics they employ to evaluate information. The paper reports on a survey study investigating people's credibility judgments with respect to online information, focusing on the constructs, heuristics, and interactions involved in people's credibility assessment processes within the context of their everyday life information activities. Using an online activity diary method, empirical data about people's online activities and their associated credibility assessments were collected at multiple points throughout the day for three days. The results indicate that distinct credibility assessment heuristics are emerging as people engage in diverse online activities involving more user-generated and multimedia content. A heuristic approach suggests that people apply mental shortcuts or rules of thumb in order to minimize the amount of cognitive effort and time required to make credibility judgments. The paper discusses why a heuristic approach is key to reaching a more comprehensive understanding of people's credibility assessments within the information-abundant online environment.

• Journal of the Korea Society of Computer and Information
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• v.25 no.8
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• pp.47-54
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• 2020

Developmental disorders are impairments of brain and/or central nervous system and refer to a disorder of brain function that affects languages, communication skills, perception, sociality and so on. In diagnosis of developmental disorders, behavioral response such as expressing emotions in proper situation is one of observable indicators that tells whether or not individual has the disorders. However, diagnosis by observation can allow subjective evaluation that leads erroneous conclusion. This research presents the technological environment and data acquisition system for AI based screening of autism disorder. The environment was built considering activities for two screening protocols, namely Autism Diagnostic Observation Schedule (ADOS) and Behavior Development Screening for Toddler (BeDevel). The activities between therapist and baby during the screening are fully recorded. The proposed software in this research was designed to support recording, monitoring and data tagging for learning AI algorithms.

• The Plant Pathology Journal
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• v.25 no.2
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• pp.136-141
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• 2009

Marking biocontrol bacteria is an essential step to monitor bacterial behavior in natural environments before application in agricultural ecosystem. In this study, we presented the simple green fluorescent protein (GFP) reporter system driven by the promoter active in Bacillus species for tagging of the biocontrol bacteria. A constitutive promoter P43 from Bacillus subtilis was fused to an enhanced promoterless gfp gene by overlap extension PCR. The GFP expression was demonstrated by the high fluorescence intensity detected in B. subtilis and Escherichia coli transformed with the P43-gfp fusion construct, respectively. The GFP reporter system was further investigated in two bacterial biocontrol strains B. licheniformis and Pseudomonas fluorescens. When the reconstructed plasmid pWH34G was introduced into B. licheniformis, GFP level measured with the fluorescence intensity in B. licheniformis was almost equivalent to that in B. subtilis. However, GFP expression level was extremely low in other biocontrol bacteria P. fluorescens by transposon based stable insertion of the P43-gfp construct into the bacterial chromosome. This study provides information regarding to the efficient biomarker P43-gfp fusion construct for bio-control Bacillus species.

• Ocean and Polar Research
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• v.43 no.4
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• pp.321-334
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• 2021

'Marine mammals-based observations' refers to data acquisition activities from marine mammals by instrumenting CTD (Conductivity-Temperature-Depth) sensors on them for recording vertical profiles of ocean variables such as temperature and salinity during animal diving. It is a novel data collecting platform that significantly improves our abilities in observing extreme environments such as the Southern Ocean with low cost compared to the other conventional methods. Furthermore, the system continues to create valuable information until sensors are detached, expanding data coverage in both space and time. Owing to these practical advantages, the marine mammals-based observations become popular to investigate ocean circulation changes in the Southern Ocean. Although these merits may bring us more opportunities to understand ocean changes, the data should be carefully qualified before we interpret it incorporating shipboard/autonomous vehicles/moored CTD data. In particular, we need to pay more attention to salinity correction due to the usage of an unpumped-CTD sensor tagged on marine mammals. In this article, we introduce quality control methods for the marine mammals-based CTD profiles that have been developed in recent studies. In addition, we discuss strategies of quality control specifically for the seal-tagging CTD profiles, successfully having been obtained near Terra Nova Bay, Ross Sea, Antarctica since February 2021. It is the Korea Polar Research Institute's research initiative of animal-borne instruments monitoring in the region. We anticipate that this initiative would facilitate collaborative efforts among Polar physical oceanographers and even marine mammal behavior researchers to understand better rapid changes in marine environments in the warming world.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.36 no.1
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• pp.105-114
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• 2016

With approximately 20 million transportation card data entries of the metropolitan districts being generated per day, application of the data to management and policy interventions is becoming an issue of interest. The research herein attempts a model of the possibility of dynamic demand change predictions and its purpose is thereby to construct a Dynamic Passengers Trip Assignment Model. The model and algorithm created are targeted at city rail lines operated by seven different transport facilities with the exclusion of travel by bus, as passenger movements by this mode can be minutely disaggregated through card tagging. The model created has been constructed in continuous time as is fitting to the big data characteristic of transport card data, while passenger path choice behavior is effectively represented using a perception parameter as a function of increasing number of transfers. Running the model on 800 pairs of metropolitan city rail data has proven its capability in determining dynamic demand at any moment in time, in line with the typical advantages expected of a continuous time-based model. Comparison against data measured by the eye of existing rail operating facilities to assess changes in congestion intensity shows that the model closely approximates the values and trends of the existing data with high levels of confidence. Future research efforts should be directed toward continued examination into construction of an integrated bus-city rail system model.

• Journal of Internet Computing and Services
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• v.17 no.3
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• pp.95-106
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• 2016

The purpose of this study was to examine how college students use Facebook and the ways in which they feel of commitment while using Facebook. The Big Five Personality Model has been considerably used in the psychology fields, and the researchers have started to explore the role of characteristic factors in influencing an individual's use of social media, such as Facebook which has become one of the most popular social networking site in the world. Therefore, the current study aims to specify the links between The Big Five Personality Model and usage patterns as well as commitment of Facebook. Two hundreds thirty five college students participated in a survey and the results are as follows: First, participants who were high in extraversion and agreeableness were more likely to do information sharing activities such as sharing posts to their friends, writing comments on the other's posts. In addition, participants who were high in openness to experience, conscientiousness, and neuroticism were more likely to do information producing activities including offering events, group, or public pages to meet people both on and offline. Second, in terms of the relationship between personality traits and commitment to the Facebook, the study found that extraversion and neuroticism were related to users' commitment to Facebook. These findings are consistent with the existing literature regarding extraversion and neuroticism were representative personality factors when it comes to commitment of media. Specifically, the study found that those who were high in neuroticism were more likely to produce information such as posting photos repeatedly or tagging their friends on posts, and also more likely to feel commitment on Facebook. These findings confirm that personality is a highly relevant factor in determining individual's behavior and the degree of commitment on Facebook. Based on these findings implications and limitations of the study are discussed.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.69-92
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• 2015

The explosion of social media data has led to apply text-mining techniques to analyze big social media data in a more rigorous manner. Even if social media text analysis algorithms were improved, previous approaches to social media text analysis have some limitations. In the field of sentiment analysis of social media written in Korean, there are two typical approaches. One is the linguistic approach using machine learning, which is the most common approach. Some studies have been conducted by adding grammatical factors to feature sets for training classification model. The other approach adopts the semantic analysis method to sentiment analysis, but this approach is mainly applied to English texts. To overcome these limitations, this study applies the Word2Vec algorithm which is an extension of the neural network algorithms to deal with more extensive semantic features that were underestimated in existing sentiment analysis. The result from adopting the Word2Vec algorithm is compared to the result from co-occurrence analysis to identify the difference between two approaches. The results show that the distribution related word extracted by Word2Vec algorithm in that the words represent some emotion about the keyword used are three times more than extracted by co-occurrence analysis. The reason of the difference between two results comes from Word2Vec's semantic features vectorization. Therefore, it is possible to say that Word2Vec algorithm is able to catch the hidden related words which have not been found in traditional analysis. In addition, Part Of Speech (POS) tagging for Korean is used to detect adjective as "emotional word" in Korean. In addition, the emotion words extracted from the text are converted into word vector by the Word2Vec algorithm to find related words. Among these related words, noun words are selected because each word of them would have causal relationship with "emotional word" in the sentence. The process of extracting these trigger factor of emotional word is named "Emotion Trigger" in this study. As a case study, the datasets used in the study are collected by searching using three keywords: professor, prosecutor, and doctor in that these keywords contain rich public emotion and opinion. Advanced data collecting was conducted to select secondary keywords for data gathering. The secondary keywords for each keyword used to gather the data to be used in actual analysis are followed: Professor (sexual assault, misappropriation of research money, recruitment irregularities, polifessor), Doctor (Shin hae-chul sky hospital, drinking and plastic surgery, rebate) Prosecutor (lewd behavior, sponsor). The size of the text data is about to 100,000(Professor: 25720, Doctor: 35110, Prosecutor: 43225) and the data are gathered from news, blog, and twitter to reflect various level of public emotion into text data analysis. As a visualization method, Gephi (http://gephi.github.io) was used and every program used in text processing and analysis are java coding. The contributions of this study are as follows: First, different approaches for sentiment analysis are integrated to overcome the limitations of existing approaches. Secondly, finding Emotion Trigger can detect the hidden connections to public emotion which existing method cannot detect. Finally, the approach used in this study could be generalized regardless of types of text data. The limitation of this study is that it is hard to say the word extracted by Emotion Trigger processing has significantly causal relationship with emotional word in a sentence. The future study will be conducted to clarify the causal relationship between emotional words and the words extracted by Emotion Trigger by comparing with the relationships manually tagged. Furthermore, the text data used in Emotion Trigger are twitter, so the data have a number of distinct features which we did not deal with in this study. These features will be considered in further study.