Earticle

Background: While efforts have been made to differentiate fall risk in older adults using wearable devices and clinical methodologies, technologies are still infancy. We applied a decision tree (DT) algorithm using inertial measurement unit (IMU) sensor data and clinical measurements to generate high performance classification models of fall risk of older adults. Objects: This study aims to develop a classification model of fall risk using IMU data and clinical measurements in older adults. Methods: Twenty-six older adults were assessed and categorized into high and low fall risk groups. IMU sensor data were obtained while walking from each group, and features were extracted to be used for a DT algorithm with the Gini index (DT1) and the Entropy index (DT2), which generated classification models to differentiate high and low fall risk groups. Model's performance was compared and presented with accuracy, sensitivity, and specificity. Results: Accuracy, sensitivity and specificity were 77.8%, 80.0%, and 66.7%, respectively, for DT1; and 72.2%, 91.7%, and 33.3%, respectively, for DT2. Conclusion: Our results suggest that the fall risk classification using IMU sensor data obtained during gait has potentials to be developed for practical use. Different machine learning techniques involving larger data set should be warranted for future research and development.

In this paper, Atanassov's intuitionistic fuzzy set theory is used to handle the uncertainty of students' knowledgeon domain concepts in an E-learning system. Their knowledge on these domain concepts has been collected from tests that were conducted during their learning phase. Atanassov's intuitionistic fuzzy user model is proposed to deal with vagueness in the user's knowledge description in domain concepts. The user model uses Atanassov's intuitionistic fuzzy sets for knowledge representation and linguistic rules for updating the user model. The scores obtained by each student were collected in this model and the decision about the students' knowledge acquisition for each concept whether completely learned, completely known, partially known or completely unknown were placed into the information table. Finally, it has been found that the proposed scheme is more appropriate than the fuzzy scheme.

Background The human cervical spine, vital for supporting head movements, is susceptible to degenerative changes, especially non-specific chronic neck pain (NSCNP). Cervical protraction and retraction, which are key components of cervical spine motion, have been studied to assess their role in NSCNP. However, the existing research lacks quantitative assessments and explores nonlinear relationships. Purpose This study explored the relationship between cervical movements during protraction and retraction and NSCNP using machine learning (ML) techniques for classification. Study design Cross sectional study. Methods This study included 277 non-NSCNP and 463 NSCNP office workers. Data were collected from the musculoskeletal screening tests. Two-dimensional video analysis was used to track markers during cervical protraction and retraction. The head tilt angle (HTA), craniovertebral angle (CVA), head excursion angle (HEA), and protraction/retraction distances were measured. Six ML algorithms (random forest, neural network, decision tree, gradient boosting, logistic regression, and support vector machine) were employed to classify individuals with and without the NSCNP. The model performance was evaluated using the area under the curve (AUC), accuracy, recall, precision, and F1 score. Results Random forest performed best, with a test AUC of 0.800, followed by decision trees (0.790), and gradient boosting (0.701). Logistic regression and support vector machine had the lowest performance. CVA during retraction, CVA and HEA during protraction were significant predictors of NSCNP in the random forest model, indicating the importance of cervical retraction and protraction kinematics. Conclusions ML models can enhance our understanding of NSCNP and the role of cervical movements. These findings offer potential targets for assessment and intervention in NSCNP cases, and suggest the clinical utility of random forests for classification. Further research is needed to explore these relationships in diverse populations and investigate the underlying mechanisms.

Since the government issued ICT priority policy for the last decade, Vietnam was reported to have impressed development of ICT infrastructure and Internet users. Until May of 2015, Vietnam has 39.7 millions of Internet users and 31 millions of social network user accounts. Facebook is the dominant website with more than 22 million Vietnamese users and 70% of those accesses Facebook via mobile phone. Several companies have utilized Facebook as the most effective interaction channels. The increasing of big text data such as posts and comments on Facebook that embed customer opinions requires method to mine sentiment text in Vietnamese language. This research applies machine learning with several algorithms such Naive-Bayes, decision trees and Support Vector Machine (SVM) for Vietnamese text data collected from fast-food industry on Facebook. The experiment results show that machine learning methods are able to classify Vietnamese sentiment text with the accuracy over 70%. Thus we proposed several recommendations for mining Vietnamese social text data.

인터넷은 전통적인 미디어를 대체하고 주요 뉴스 미디어 플랫폼 중 하나가 되었습니다. 인터넷 소스의 뉴스는 접근성이 좋고 편리하기 때문에 기존 뉴스 소스에 비해 빠르고 간단하게 이동할 수 있습니다. 그러나 가짜 뉴 스가 대량으로 발생하고 정치적, 상업적 이유로 온라인 커뮤니티에 퍼지면서 확인되지 않은 소식통으로부터 입수한 모든 언론 보도가 진짜인 것은 아니다. 가짜 뉴스는 이론적으로나 의도적으로 독자들을 속이거나 잘못 알릴 수 있다. 왜냐하면 사람들은 오프라인 커뮤니티에 영향을 미칠 수 있는 어떤 정보에도 쉽게 얽히게 되기 때문이다. 일부 수동 웹사이트는 정보가 사실인지 확인하도록 설계되어 있지만 온라인, 특히 웹에서 빠르게 확 산되는 정보의 양은 확장되지 않습니다. 이 문제를 해결하기 위해 자동 팩트체크 어플리케이션은 확장성과 자 동화의 요건에 대응하도록 설계되었습니다. 그러나 현재 애플리케이션 방법에는 기계 학습 분류 모델 성능을 개선하기 위해 가짜 뉴스 특징을 식별하는 포괄적인 다차원 데이터 세트가 없다. 이 문제를 해결하기 위해 본 연구논문에서는 사용자가 기사의 제목을 입력하면 데이터를 분류하는 Formb 챗봇을 제안했다. 이 연구 작업 에서 데이터 집합의 분류는 반복 신경망(RNN)과 장기 단기 기억(LSTM) 모델을 사용하여 수행되었다. 가짜 및 실제 뉴스 데이터 세트는 사전 처리되어 모델을 교육하는 데 사용됩니다. 저장된 모델은 지정된 입력 텍스트 의 신뢰성을 확인하기 위해 불일치 서버에 배포됩니다. Disconsid API는 python 파일을 chatbot으로 실행할 수 있는 액세스를 제공합니다. 분석 측면에서, 제안된 모델은 96.77%의 정확도로 CNN와 같은 기존 뉴럴 네트 워크 모델을 능가한다.

Internet has replaced traditional media and become one of the major news media platforms. News from internet sources tend, since they are accessible and convenient, to travel quicker and simpler than conventional news sources. However, not all of the media reports obtained from unverified sources are authentic as fake news arises in large numbers and is prevalent in online communities for both political and commercial reasons. Fake news can deceive or misinform readers theoretically or intentionally because people will easily get tangled by any of this information which may impact on the offline community. Although some manual websites are designed to check if the piece of information is true, the volume of quick-spread information online, notably on the web, does not scale. In order to solve this issue automatic fact-checking applications were designed to tackle the requirement of scalability and automation. However, current application methods lack an inclusive multi-dimensional data set to identify fake news features to improve machine learning classification model performance. To overcome this problem, this research paper proposed the Hoax chatbot which classifies the data when user enters an article headline into it. In this research work, the classification of dataset has been done using recurrent neural network (RNN) and long short-term memory (LSTM) model. The fake and true news dataset are preprocessed and used to train the model. Saved model is deployed on the discord server in order to check the credibility of the given input text. Discord API gives an access to run python files into their chatbot. In terms of analysis, the proposed model outperforms already existed neural network model such as convolutional neural network (CNN) with an accuracy of 96.77%.

본 연구의 목적은 현행 양도소득세제에 대한 이론적 고찰과 현행 양도소득세 제도에 대한 검토를 통하여 우리나라 양도소득세제에 내재하는 문제점을 도출하고 이에 대한 개선방향을 제시하였다. 개인에 대한 양도소득은 소득세법상의 양도소득세로, 법인에 대한 양도소득은 법인세법상의 토지 등 양도소득에 대한 법인세로 과세하고 있으며, 개인 양도소득세는 토지와 건물이 양도소득세 징수세목의 대부분을 차지하고 있다. 본 연구에서는 현행 양도소득세제에 개선방향을 제시하였는데, 첫째로 선진국처럼 세부담의 형평성 측면에서 접근할 필요가 있으며, 둘째로 부동산투기억제 대책을 조세정책만 가지고 접근할 것이 아니라 금융정책과 주택정책 위주로 접근해야 실효를 거둘 수 있을 것이며, 셋째로 비과세의 조건을 주택거주기간 5년～10년이상으로 세법을 개정하여야 한다고 제안하고 있다. 본 연구의 범위와 연구방법으로는 개인에 부과되는 양도소득세 중에서도 주된 과세대상인 토지와 건물에 대한 양도소득세제로 연구범위를 한정해 다루고 있으며, 연구방법은 법률의 공정성과 객관성을 위해 주로 현행 세법을 분석하고 파악하여 향후 세법개정에 제안하는데 비중을 두었다. 본 연구결과를 통하여 향후 양도소득세제가 조세의 세법원칙에 맞춰 개정이 이루어 질 수 있기를 기대한다.

The purpose of this study tried theoretical review on the current Transfer Income Tax system, and review on current Korean Transfer Income Tax system, to derive the inherent problems in Korean Transfer Income Tax system. This study presents the improving measures thereto.The transfer income earned by any individual person is taxed as the Transfer Income Tax pursuant to the Income Tax Act, and the transfer income earned by any legal person is taxed as the transfer income on transfer gain on land etc, pursuant to the Corporate Tax Act. In case of the Transfer Income Taxes earned by individual persons, land and buildings comprise most of the taxable items of the Transfer Income Tax. This study limits the scope of study to the Transfer Income Tax on land and building as the major taxable item, rather than all the Transfer Income Tax taxed to individual taxpayers. The outcomes of this are expected to rationly improvement the real estate taxation in accordance with the principle of tax law.

Social Commerce recently attracted the attention of academic and industry researchers. Social Commerce aims to make a transactional environment which is beneficial to three parties-social commerce service provider, buyer and seller by way of using the platform of SNS. As Social Commerce is a new technology issue, there is no existing conceptual framework, e.g. appropriate classification the business types, that help to understand the nature of Social Commerce. This study suggests one classification framework and tries to verify whether it works.

스트레스 때문에 일상생활에서 부정적인 행동이 나타나는 것 중 하나가 불면증이다. 불면증의 대처가 늦어지면 경도인지장애, 우울증 등 다양한 부정적 영향을 초래하게 된다. 본 논문은 심리학 그림인 WZT(Wartegg Zeichen Test)를 사용하여 다양한 디바이스에 직접 그린 데이터 이미지를 심리학 기반과 에듀테크 융합 방법으로 인공지능 자동분석을 통하여 불면증을 분류 및 예측하는 방법이다. 불면증을 동반한 사람의 그림 70개와 비불면증의 그림 70개를 데이터로 사용하여 인공지능으로 자동 분석하였다. 그 결과 84-87%의 정확성 결과를 나타낸다. WZT 이미지 데이터에서 나타나는 불면증을 인공지능으로 특징을 추출하여 분석하는 것은 심리치료의 기술 융합으로 더 많은 기회를 창출하는 논문으로 의의가 있다.

One of the negative behaviors that occur in daily life due to stress is insomnia. If the treatment of insomnia is delayed, it can lead to various negative effects such as mild cognitive impairment and depression. This paper presents a method for classifying and predicting insomnia through artificial intelligence automatic analysis of data images drawn directly on various devices using the psychological graph WZT (Wartegg Zeichen Test) and a fusion method of psychology and edutech. Using 70 drawings of people with insomnia and 70 drawings of people without insomnia as data, the data is auto-matically analyzed by artificial intelligence with the data increased. As a result, the accuracy results are 84-87%. The pa-per is significant in that it creates more opportunities by integrating psychotherapy technologies by extracting features of insomnia appearing in WZT image data using artificial intelligence.

What are some of the popular subjects among Korean researchers with respect to translation? What are unique characteristics involving Korea's Translation Studies? This paper is a preliminary study to answer those questions and aims to explore the ways on how to classify researches of Translation Studies. This paper consists of the following three parts: In the first part, three scholars who suggested frameworks for classifying Translation Studies are selected, and their classification methods are reviewed. To be specific, significance and limitations of Holmes/Toury map, which was first developed by Holmes and improved by Toury, are examined. Then, this paper examines four types of translation discourses, i.e., prescriptive/normative Translation Studies(TS), descriptive TS, inductive/scientific TS, and productive TS, proposed by Ladmiral, a French philosopher of translation. Lastly, twelve research areas of TS proposed by Williams and Chesterman are reviewed. The second part classifies a total of 193 papers published in the Interpreting and Translation Studies, an academic magazine dedicated to Translation Studies, from the first issue through 2010 issues, according to the above twelve research areas of Williams & Chesterman. The last part of this paper analyzes the classification results, and presents ways to complement the above-mentioned classification frameworks, along with their implications.

배우자 보험살인 범죄 현장은 대부분 우연한 사고로 인한 사망으로 위장되어 있기 때문에, 살인범죄 관점보다는 자연스러운 사망 현장으로 바라보기 쉽다. 배우자 보험살인 범죄는 계획적이고 장기적인 범죄임으로 타 살인범죄와 달리 범죄현장 보다는 범행동기 및 범행단계별로 나타나는 특성을 종합적으로 파악하여 피해자 보호방안을 수립하여야 한다. 이러한 관점에서 본 연구는 배우자 보험살인 사례에서 나타나는 범행동기 및 범행단계별 특성에 근거하여 배우자 보험살인 범죄자의 유형분류를 시도하였다. 본 연구의 목적은 첫째, 한국 배우자 보험살인 범죄자에게 적합한 유형을 탐색하는 것이다. 둘째, 범행특성과 범죄자 유형의 변화추세를 분석하는 것이다. 이를 위하여 2000년∼2017년 한국에서 배우자 보험살인 범죄로 적발된 51사건에 대한 언론보도, 수사기관 보도자료, 형사판결문 등을 수집하여 108개 특성변인에 대한 동질성분석(HOMALS)을 수행하였다. 연구결과, 한국 배우자 보험살인 범죄자는 외도형, 포식형, 빈곤형의 3가지 유형으로 분류되었다. 외도형 범죄자는 주로 내연관계 공범과 함께 범행 후 뺑소니 교통사고로 위장하는 범행패턴이 관찰되었다. 포식형 범죄자는 주로 사회적 약자인 피해자를 물색 후 위장혼인과 동시에 보험에 가입하고 독극물로 살해 후 사고사로 위장하는 범행패턴이 관찰되었다. 빈곤형 범죄자는 주로 생계형 궁핍에 좌절을 느껴 배우자와 자녀들을 연속살인하는 범행패턴이 관찰되었다. 범죄자 유형 중 외도형 범죄자는 감소한 반면, 포식형 범죄자는 증가 추세에 있음이 관찰되었다. 배우자 보험살인 범죄 예방을 위하여 증가추세에 있는 포식형 범죄자의 위험성에 주목하여야 한다. 포식형 범죄자는 사냥하듯이 피해자를 물색하여 위장 혼인관계를 맺은 후 보험금과 재산을 함께 노리고 살해하는 특성이 관찰된다. 이러한 연구결과를 바탕으로 포식형 범죄자에 대한 후속 연구방향을 제시하였다.

The murder crime scene related to spousal insurance is often mistaken for casual accident death, so it is easier to look at it as a natural death scene rather than a murder crime. As the spousal insurance related murder is a planned and long-term crime unlike other murder, it is necessary to establish a protection plan for the victim by comprehensively understanding the characteristics of the motive and crime stages rather than the crime scene. From this point of view, this study attempted to classified the types of homicide offenders based on the characteristics of the crime motive and crime stage in the case of spousal insurance homicide. The purpose of this study is to find out the type that is suitable for Korean murder criminals related to spousal insurance. Secondly, it is to analyze trends of characteristics of crimes and types of criminals. For this purpose, we conducted homogeneity analysis(HOMALS) on 108 characteristic variables after we’d collected media reports, investigative agency press releases and criminal judgment sentences of 51 cases accused of homicide criminals related to spousal insurance in Korea from 2000 to 2017. As a result, Korean homicide criminals related to spousal insurance were classified into three different types according to their motive and crime stage characteristics: affair, predator, and poverty. In the affair type, the crime mainly was disguised as a hit-and-run traffic accident and both of them having an affair were related to the crime. The predator type offender mainly disguised the crime as an accident after the offender’d poisoned the victim. The victim was selected for the marriage and insurance application as the victim was socially weak. The offender from the poverty type mainly killed both the spouse and children after frustrated from the financial difficulties for daily living. Among the crime types, the predator type increased while the affair type decreased. It is important to pay attention to the risk of predator type increasing for the prevention of crime related to the spouse insurance. The perpetrator searches for the victim like hunting and kills the victim for the insurance death benefits and property after fake marriage. Based on these findings, future research directions for predators were suggested.

본 연구의 목적은 노동시장 진입과정 유형화를 통하여 빈곤의 세대간 이전이 진행되는 과정을 분석하는 것이다. 이를 위하여 사건배열분석을 통하여 빈곤가구에서 성장한 자녀를 대상으로 청년기 노동시장 진입과정을 유형화하였고, 이러한 결과를 고소득가구 자녀와 비교하였다. 종사상지위와 직업을 중심으로 이행과정을 분석한 결과, 우선 종사상 지위 측면에서 빈곤가구 자녀는 정규직 중심형, 정규직으로의 이동형, 미취업형, 진입과 이탈 반복형 등 4개 유형이 도출되었다. 그에 비해 고소득가구의 자녀는 정규직 중심형, 정규직으로의 이동형, 미취업형 등 3개 유형이 도출되었다. 한편, 직업을 중심으로 살펴본 결과, 빈곤가구는 기능장치형, 빈번한 이직형, 다양한 직종형, 미취업형 등 4개의 유형이 도출되었다. 그리고 고소득가구는 전문･사무직형, 전문･사무직으로의 이동형, 미취업형 등 3개의 유형이 도출되었다. 고소득가구 자녀의 경우 청년기에 이미 전문직이나 사무직 정규근로자로의 이행이 고정되고 있는 반면, 빈곤가구 자녀는 노동시장의 진입과 이탈뿐만 아니라 직업의 이동도 빈번한 특성을 보이고 있었다. 이를 통해 서로 다른 청소년기를 보낸 두 개의 집단이 청년기에 노동시장 진입과정에서도 상이한 경험을 하고, 이것이 결과적으로 생애소득에 영향을 미칠 것임을 유추할 수 있었다.

The main purpose of this paper is to analyze intergenerational transmission process of poverty through classification of entry process in labour market. In order to find out an answer to this question, this study classifies entry process in labour market of youth among poverty and compares the result with 5th quintile. To analyze entry process, this study used optimal matching of sequence analysis, cluster analysis, and multinomial analysis from Korea Labour Panel data(1-11waves). Findings of this study are as follows. First, poor youth were divided into 4 clusters (regular worker, change to regular worker, unemployment, and repeat of entry and exit) from employment status. And rich youth were classified 3 clusters (regular worker, change to regular worker, and unemployment). Second, in terms of job, poor youth were divided to 4 clusters (manual worker, frequent change job, various jobs, and unemployment). And rich youth were classified 3 clusters (professional, change to professional, and unemployment). These finding means that poor youth already experience unstable labour from entry process in labour market. And it is able to affect to the intergenerational transmission of poverty.

본 연구의 목적은 우리나라 중고령자의 은퇴과정을 유형화 하고 다양성과 경제적 불평등에 초점을 두어 각 유형의 특성을 살펴보는 데 있다. 유형화 분석을 위해 한국노동패널 제 2~6차년도 자료를 활용하여 탐색적 분석방법인 최적일치법(optimal matching)과 군집분석(cluster analysis)을 적용하였다. 은퇴과정의 유형화 분석결과, 주변적경제활동형, 사적이전의존형, 재진입제약형, 안정적퇴장형, 퇴장후재진입형 5개의 유형을 도출하였다. 각 유형별로 다양한 유형적 특성을 보였는데, 우선 주변적경제활동형은 주변적 근로자에게서 발견되는 전형적인 고용 불안정 특성을 보였다. 둘째, 사적이전의존형은 은퇴과정에서 고용활동 중단 시 사적이전에 의한 소득보전 가능성이 높은 유형이다. 셋째, 재진입제약형은 경제적 지원없이‘비경활-비수급’상태의 분포가 가장 높은 유형이다. 넷째, 안정적퇴장형은 비경활-연금수급 상태로 이행한 비중이 높은 유형이다. 끝으로, 퇴장후재진입형은 은퇴과정에서 재취업을 하여 늦은 연령기까지 고용상태를 유지할 가능성이 높은 특징을 보였다. 또한 상태별 지속기간 분석을 통해 유형 간 경제적 불평등 상태를 비교해 본 결과, 공적연금 수급 가능성과 수급의 지속성이 가장 안정적으로 이루어지는 유형은 안정적퇴장형이었다. 사적이전의존형은 사적이전의 경제적 지원 가능성이 가장 높고 지속기간도 가장 긴 것으로 나타났다. 그리고 은퇴과정에서 경제적 지원이 없는 상태에서 고용상태에 따른 상태별 지속기간을 살펴 본 결과, 정규직 지속기간이 가장 긴 유형은 안정적퇴장형이었다. 주변적경제활동형은 은퇴과정에서 비정규직 고용상태의 지속기간이 가장 길게 나타났다. 비임금근로는 퇴장후재진입형에서 지속기간이 가장 높게 나타났다. 본 연구를 통해 얻을 수 있는 정책적 함의는 생애과정의 초기에 형성된 기회구조와 노동시장에서 불리한 지위로 인해서 은퇴 이후에 불이익이 더 증가되지 않도록 완화하는 생애과정 관점의 재분배정책이 강화되어야 한다는 점이다.

The purpose of this study is to classify the type of retirement process among the mature-aged in Korea. The study used the panel data from Korean Labor Panel (year2~6) for the classification of retirement process through Optimal Matching and Cluster Analysis. Classification is made in 5 categories as 'peripheral-economically active', 'private-transfer dependent', 're-entering limited', 'securely exiting', 'exit-and-reentering'. First, "peripheral-economically active" is a group which frequently experienced job status change and work insecurity. Second, "private-transfer dependent" is a group in which private transfer is likely to be supplements income in the incidence of unemployment. Third, "re-entering limited" is a group in which the proportion of no financial support combined with the absence of any economic activity is the largest. Fourth, the type "Securely exit" is th group whose members switches over to non-economically active status with pension receipt. The last type is "exit-and-reenter" that the member are highly possible to reenter in the labor market and stay in long time regardless of with or without pension plan. To examine the inequality among the types of retirement process, the duration of each status is analyzed. First, in the situation of being non-economically active, the duration of status is maintain public pension receiving and duration stabile in "securely exit" group. For "private-transfer dependent" type, members are mostly dependent on private financial support and that duration of it is longest. Through the analysis of retirement process without under other financial supports, it is "securely-exiting" type for which the duration of full-time employment is longest. It appears that the duration of part-time employment is longest in "peripheral-economically active" type. And for the case of non-waged employment it is "exit-and-reenter" type. Finally, the redistribution policy based on life course perspective is necessary to prevent that the opportunity in the structure before retirement stage and the unfavorable position in labor market make worse disadvantage in retirement process and after that.

본 연구에서는 Canter 등(2003)의 연구 결과를 우리나라의 연쇄 성범죄자를 대상으로 재검증하고, 이를 토대로 한국의 연쇄 성범죄자 유형분류 모형을 탐색하고자 하였다. 분석을 위하여 2006년 1월부터 2010년 5월까지 서울 지역에서 2건 이상의 강간범죄를 저지른 35명의 연쇄 강간범죄자의 수사 자료를 수집하였다. 수집된 자료를 통한 범죄현장 행동의 분석 결과 Canter 등(2003)이 제시한 모형은 한국의 연쇄 강간범죄자에게 적합하지 않은 것으로 나타났다. 따라서 한국의 연쇄 강간범죄자들의 유형을 분석한 결과 폭력적 상호작용, 도구적 상호작용, 언어적 상호작용 유형으로 분류되었다. 연구 결과는 두 가지 측면에서 프로파일링을 위한 분석 근거로 활용 가능할 것이다. 첫째로 각 유형에 해당하는 추가 행동 변인을 탐색하기 위한 기초 자료로서 활용가능할 것이며, 둘째로 유형별 범죄자의 특징, 전과여부 및 내용, 도주 후 행동 등의 분석 근거로서 사용될 수 있을 것이다.

This study aims to revarify the result of Canter(2003)'s research on serial sex offenders in the context of Korean society, and to explore for typological model of serial sex offenders in Korea. As a result of this research, the interactions between serial offenders and victims were classified into hostile, instrumental, and verbal. This result corresponds with the earlier one of Canter's study and shows that this typology can be considered as a useful base to categorize serial sex offenders. The result of this study can be used as an analytical base on two counts. To begin with, it will be useful as basic material to probe additional behavioral factor that fits to each type and, moreover, to analyze characteristics, criminal record, and behavior after flee of offender according to type. Although the result alone should not be presented as final analysis, it can be used as objective basis to minimize the possible errors of individual analyst.

강력범죄자의 수사를 지원하기 위해 범죄자 프로파일링 기법이 개발된 이후, 대부분의 연구들은 범죄자의 유형 분류를 위한 기준으로 범죄자의 동기에 초점을 두어왔다. 그러나 범행 동기는 사건 발생 직후 수집된 정보만으로는 확인되기 어려우며 범인이 검거된 이후에 범인조차도 자신의 동기를 명확하게 진술하지 못하는 경우가 많다는 점 등의 한계가 지적되면서 최근의 연구들은 사건발생 직후 파악할 수 있는 정보에 근거한 범죄자의 행동을 통해 범죄자의 유형을 분류하고자 시도해왔다. 이러한 관점에서 본 연구는 방화범죄자의 현장 행동에 근거하여 방화범죄의 유형을 분류하였다. 본 연구의 목적은 첫째, 외국의 연구에서 제시된 방화범죄 유형이 한국의 방화범죄 자료에 의해서도 지지되는가를 검증하고, 둘째로 한국의 방화범죄에 적합한 유형을 분석하는 것이다. 이를 위하여 2006년～2009년 검거된 방화범죄자 127명의 사건 자료를 수집하였으며, 선행연구에서 제시된 41개의 행동변인에 대한 다차원척도 분석을 수행하였다. 연구 결과, 선행연구에서 제시된 유형은 한국의 방화범죄 유형 모델로 적합하지 않은 것으로 나타났으며, 분석 결과 5개의 유형이 산출되었다.

Recent studies for the criminal profiling have focused on the relation between offenders' behaviors at a crime scene and offender characteristics. For Korean offenders, a study has been performed to search for the model to differentiate the homicide and rape offenders based on criminal acts at the crime scenes, but there were no research attempts to profile the characteristics of Korean arsons. The purpose of this study is finding the framework to understand Korean arsons based on the characteristics of crime scenes and criminal acts. To achieve this goal, actions at the crime scenes drawn from police data were analysed using MDS(N=127). The result did not support the model of the thematic classification proposed by earlier western research but revealed the existence of five themes among Korean arsons. Among these, two themes was dominant; expressive/instrumental actions. Finally, comparison between this model and earlier one was made.

Purpose: This study aimed to validate instruments to classify the frailty of Korean elderly people in community. Methods: For this study, 632 elders were selected from community?based elderly houses and home visiting registries, and data on frailty were collected using three instruments during November, 2008. The Korean Frail Scale(KFS) was composed of 10 domains with the maximum score of 20. The Edmonton Frail Scale(EFS) had 10 domains with the maximum score of 17. The 25_Japan Frail Scale(25_JFS) was composed of 6 domains with the maximum score of 25. Internal consistency was measured with Cronbach's ?. Sensitivity, specificity and area under the curve(AUC) of ROC were measured to see validity with long?term care insurance grade as a gold standard. Results: The Cronbach's ? was .72 for KFS, .55 for EFS, and .80 for 25_JFS. Sensitivity, specificity, and AUC were 70.0%, 83.2%, and .83, respectively, at cutting point 10.5 for the KFS, 50.0%, 80.9%, and .66, respectively, at 8.5 for EFS, and 80.0%, 85.9%, and .86, respectively, at 12.5 for 25_JFS. Conclusion: KFS and three JFS showed favorable internal consistency and predictive validity. Further longitudinal studies are recommended to confirm predictive validity.

본 연구는 노인학대 가해자의 유형을 잠재계층분석을 통해 분류함으로써 노인학대 가해자의 이질성을 살펴보고자 한다. 분석을 위해 총 69개의 사례들을 중앙노인보호기관이 발간한 사례집에서 추출했다. 노인학대 가해자의 학대유발요인에 따라 집단으로 분류하기 위해 총 11개의 학대유발요인에 기반해 가해자에 대해 잠재계층분석을 실시했다. 그 결과 정신장애형 가해자, 스트레스표출형 가해자, 의존형 가해자, 부양부담형 가해자 등 네 가지 유형으로 학대행위자를 유형화할 수 있었다. 선행연구에서는 학대행위나 피해자에 초점을 맞추었기 때문에 살펴볼 수 없었던 노인학대 가해자들의 학대유발요인에 따른 이질성을 확인할 수 있었다. 연구의 한계, 정책적 함의, 그리고 향후 연구 방향에 대해 제안하였다.

This study classifies elder abuser into subtypes based on their profiles. 69 cases with abusers’ demographics, harmful characteristics, and abuse behaviors were retrieved from case books published by government agencies. Latent class analysis was used to categorize abusers based on 11 variables. Result showed that abusers fall into 4 profiles descriptively labeled “Mental disordered”, “stress explosive”, “dependent”, “economical burden” types. This result revealed abuser subtypes which are not able to be identified by analysis of abusing behaviors. Discussion and future research direction are discussed.

인공지능 인력을 양성하기 위해 주요국에서는 초등학교에서부터 인공지능 교육을 제공하고자 하는 노력을 기 울이고 있다. 초등학교에서 인공지능 교육을 도입하기 위해서는 초등학생 수준을 고려한 교육과정과 내용이 필 요하다. 본 연구는 초등학생의 인공지능 교육을 목적으로 언플러그드 수준의 조작을 통해 인공지능이 학습하는 원리를 체험하는 교육 콘텐츠를 개발하였다. 개발한 교육 콘텐츠는 문장의 정서를 판단하는 인공지능으로 주제 를 선정하였고, 문제를 해결하기 위해 데이터 속성을 도출하여 수집하고 인공지능이 학습하는 과정을 시뮬레이 션하여 문제를 해결하는 과정으로 구성하였다. 연구결과, 인공지능에 대한 태도가 사전보다 사후에 증가하였고, 과제 수행률이 평균 85%로 나타나 제안하는 인공지능 교육 콘텐츠가 교육적 의의가 있음을 보여주었다.

In order to cultivate AI(artificial intelligence) manpower, major countries are making efforts to apply AI education from elementary school. In order to introduce AI education in elementary school, it is necessary to have a curriculum and educational content for elementary school level. This study developed educational contents to experience the principle of AI learning at the unplugged level for the purpose of AI education for elementary school students. The educational content developed was selected as an AI that evaluates the emotion of sentences. In addition, to solve the problem, data attributes were derived and collected, and the process of AI learning was simulated to solve the problem. As a result of the study, the attitude of elementary school students to AI increased post than before. In addition, the task performance rate was averaged at 85%, showing that the proposed AI education content has educational significance.

Real-time monitoring the spread of disease and taking a rapid response is necessary. The traditional public health report is accurate, but requires a lot of manpower and resources. The main drawback is the lag of time. Notifiable infectious diseases report generally lags behind medical diagnosis about 4-5 weeks. In this paper, social network data are used to detect disease and track its rapidly changing trends. We take flu data in Sina weibo as an example and analyze flu related weibos from temporal and spatial dimensions. Compared with the previous work, most studies filter out the non-infection weibo noises directly, however, the noises have close association with flu activities. We are not simply discard these data but use them to capture the public nuanced attitude changes. Flu related weibos are divided into four categories which represent four states of public concern. The four states, gradually upgrading from concern about news to anxiety of illness, help to capture the public nuanced attitude changes toward flu trends. Flu weibos concern distributed map and influenza activities curve are drawn to show the analyze result. Multiple classification systems’ accuracy are investigated. The proposed method twice iterative classification makes the system accuracy up to 89.50%.

This study describes a theory-based instructional approach that aimed to overcome the gap between the geometrical level of thinking (van Hiele) expected in given assignments and students' level of thinking. The research formulated a method that enabled students who did not reach van Hiele’s level 3 (Order) to classify special parallelograms via conceptual behavior. Korean ninth-grade low achievers were introduced to parallelograms in a process involving dynamic and continuous shape changes of multi-named-objects in order to enlarge the concept image. The results of the study seem promising.