• Information Systems Review
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• v.20 no.2
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• pp.39-62
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• 2018

Thanks to the growth of computing power and the recent development of data analytics, researchers have started to work on the data produced by users through the Internet or social media. This study is in line with these recent research trends and attempts to adopt data analytical techniques. We focus on the impact of "internal marketing" factors on firm performance, which is typically studied through survey methodologies. We looked into the job review platform Jobplanet (www.jobplanet.co.kr), which is a website where employees and former employees anonymously review companies and their management. With web crawling processes, we collected over 40K data points and performed morphological analysis to classify employees' reviews for internal marketing data. We then implemented econometric analysis to see the relationship between internal marketing and market capitalization. Contrary to the findings of extant survey studies, internal marketing is positively related to a firm's market capitalization only within a limited area. In most of the areas, the relationships are negative. Particularly, female-friendly environment and human resource development (HRD) are the areas exhibiting positive relations with market capitalization in the manufacturing industry. In the service industry, most of the areas, such as employ welfare and work-life balance, are negatively related with market capitalization. When firm size is small (or the history is short), female-friendly environment positively affect firm performance. On the contrary, when firm size is big (or the history is long), most of the internal marketing factors are either negative or insignificant. We explain the theoretical contributions and managerial implications with these results.

• Proceedings of The KACE
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• 2017.08a
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• pp.201-204
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• 2017

빅 데이터의 시대가 도래하면서 이전보다 데이터로부터 유의미한 정보를 추출하는 것에 대한 연구가 활발하게 진행되고 있다. 딥러닝은 텍스트, 이미지, 동영상 등 다양한 데이터에 대한 학습을 가능하게 할 뿐만 아니라 높은 학습 정확도를 보임으로써 차세대 머선러닝 기술로 각광 받고 있다. 그러나 딥러닝은 일반적으로 학습해야하는 데이터가 많을 뿐만 아니라 학습에 요구되는 시간이 매우 길다. 또한 데이터의 전처리 수준과 학습 모델 튜닝에 의해 학습정확도가 크게 영향을 받기 때문에 활용이 어렵다. 딥러닝에서 학습에 요구되는 데이터의 양과 연산량이 많아지면서 분산 처리 프레임워크 기반 분산 학습을 통해 학습 정확도는 유지하면서 학습시간을 단축시키는 사례가 많아지고 있다. 본 연구에서는 범용 분산 처리 프레임워크인 아파치 스파크에서 데이터 병렬화 기반 분산 학습 모델을 활용하여 모바일 빅 데이터 분석을 위한 딥러닝을 구현한다. 딥러닝을 구현할 때 분산학습을 통해 학습 속도를 높이면서도 학습 정확도를 높이기 위한 모델 튜닝 방법을 연구한다. 또한 스파크의 분산 병렬처리 효율을 최대한 끌어올리기 위해 파티션 병렬 최적화 기법을 적용하여 딥러닝의 학습속도를 향상시킨다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.27 no.4
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• pp.843-853
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• 2017

In the case of electronic payment, the obligation to use the certificate-based authentication was abolished. As Fin-tech service providers gain autonomy, various authentication methods are provided. SMS, ARS, PIN, Text-passwords, Fingerprints are popular authentication methods in the mobile Fin-tech services. In this study evaluate the usability and security of authentication methods in a unified mobile environment. We evaluate the usability through SUS and interview. Also we evaluate the security level of authentication methods through NIST guideline. At the result of the usability evaluation, Fingerprint authentication method had been determined as the highest usability, also Fingerprint authentication method had been determined as the safest authentication method by obtaining Security Level 4.

• Proceedings of the Korea Contents Association Conference
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• 2006.11a
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• pp.351-354
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• 2006

Because of the geographical characteristics of Jeju Island, the degree of usage of dialect in Jeju is higher than those of other cities. In addition, Jeju dialect is one of the important research topics in Korean Linguistics due to archaic words before 15th century. However, the research works about Jeju dialect related to tourism are rare unfortunately. In other words, since the content about Jeju dialect for tourists are rare, in order to activate and inform the tourists about Jeju dialect, the development of mobile content about Jeju dialect is necessary. In this paper, we develop the mobile content about Jeju dialect which are divided into several situations to help the tourists' understanding.

• Archives of design research
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• v.12 no.3
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• pp.117-126
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• 1999

The purpose of this thesis is to research the applicatuion of moving typography whcih was analyzed and introduced in Opening Credit of Movies. There are four proposals to apply this plan which was derived from the movement of image transmission and enlargement of the main part. First; By providing a motion to a keyword from a text-based screen, it can enhance the importance of the main part. Second; Use a differentiated CUI (Character User Interface) as a button in order to form the flow data in data sort. Third; The meaining of unknown letters can be conveyed by adding a motion to letter. Fourth; The conveyance of meaning and the visual image transmission method can be used.

• Journal of Science Education
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• v.35 no.2
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• pp.159-172
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• 2011

The purpose of this study is to specify the quality of I-R-E pattern and question-answer affiliation between students and teacher, depending on teacher's career. This study analyzes 6 classroom discourse texts of 6th grade science class. The results of this study are as follows. First, in the case of a newly appointed teacher, I-R-E pattern is appeared repeatedly. Second, in the case of experienced teachers, expended I-R-E pattern is appeared compare with a newly appointed teacher. Third, in the case of a newly appointed teacher, independence relational structure is appeared more repeatedly than other structures. But, in the case of experienced teachers, all kinds of question-answer structures - independence, parallel, insertion, and reorganization relational structure - are appeared more evenly.

• Journal of Educational Research in Mathematics
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• v.25 no.4
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• pp.549-569
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• 2015

Reflecting on own beliefs about teaching and learning, developed during "the apprenticeship of observation", is a central task for pre-service years. This case study analysed a lesson observation course which could identify, challenge pre-service teachers' folk pedagogy about classroom communications and induce to change of beliefs about teaching and learning. Our analysis shows that targeting and refuting pre-service teachers' specific belief may be an effective strategy for teacher educators to foster new teaching practice.

• Journal of Korea Multimedia Society
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• v.17 no.2
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• pp.232-239
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• 2014

As online texts have been rapidly growing, their automatic classification gains more interest with machine learning methods. Nevertheless, comparatively few research could be found, aiming for Korean texts. Evaluating them with statistical methods are also rare. This study took a sample of tweets and used machine learning methods to classify emotions with features of morphemes and n-grams. As a result, about 76% of emotions contained in tweets was correctly classified. Of the two methods compared in this study, Support Vector Machines were found more accurate than Na$\ddot{i}$ve Bayes. The linear model of SVM was not inferior to the non-linear one. Morphological features did not contribute to accuracy more than did the n-grams.

• Annual Conference of KIPS
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• 2017.04a
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• pp.873-876
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• 2017

최근 정보검색의 효율성을 위해 데이터를 분석하여 해당 데이터를 가장 잘 나타내는 연관단어를 추출 및 추천하는 연구가 활발히 이루어지고 있다. 현재 관련 연구들은 출현 빈도수를 사용하는 방법이나 LDA와 같은 기계학습 기법을 활용해 데이터를 분석하여 연관단어를 생성하는 방법을 제안하고 있다. 기계학습 기법은 결과 값을 찾는데 사용되는 특징들을 전문가가 직접 설계해야 하며 좋은 결과를 내는 적절한 특징을 찾을 때까지 많은 시간이 필요하다. 또한, 파라미터들을 직접 설정해야 하므로 많은 시간과 노력을 필요로 한다는 단점을 지닌다. 이러한 기계학습 기법의 단점을 극복하기 위해 인공신경망을 다층구조로 배치하여 데이터를 분석하는 딥러닝이 최근 각광받고 있다. 본 논문에서는 기존 기계학습 기법을 사용하는 연관단어 추출연구의 한계점을 극복하기 위해 딥러닝을 활용한다. 먼저, 인공신경망 기반 단어 벡터 생성기인 Word2Vec를 사용하여 다양한 텍스트 데이터들을 학습하고 룩업 테이블을 생성한다. 그 후, 생성된 룩업 테이블을 바탕으로 인공신경망의 한 종류인 합성곱 신경망을 활용하여 사용자가 입력한 주제어와 관련된 최근 뉴스데이터를 분석한 후, 주제별 최신 연관단어를 추출하는 시스템을 제안한다. 또한 제안한 시스템을 통해 생성된 연관단어의 정확률을 측정하여 성능을 평가하였다.

• Journal of Internet Computing and Services
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• v.5 no.5
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• pp.129-138
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• 2004

In the telecommunications network, multicasting is widely used recently. Multicast tree problem is modeled as the NP-complete Steiner problem in the networks. In this paper, we study algorithms for finding efficient multicast trees with hop and node degree constraints. Multimedia service is an application of multicasting and it is required to transfer a large volume of multimedia data with QoS(Quality of Service). Though heuristics for solving the multicast tree problems with one constraint have been studied. however, there is no optimum algorithm that finds an optimum multicast tree with hop and node degree constraints up to now. In this paper, an approach for finding an efficient multicast tree that satisfies hop and node degree constraints is presented and the experimental results explain how the hop and node degree constraints affect to the total cost of a multicast tree.