• International Journal of Computer Science & Network Security
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• 제22권5호
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• pp.294-302
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• 2022

Detection of fake news is a complex and a challenging task. Generation of fake news is very hard to stop, only steps to control its circulation may help in minimizing its impacts. Humans tend to believe in misleading false information. Researcher started with social media sites to categorize in terms of real or fake news. False information misleads any individual or an organization that may cause of big failure and any financial loss. Automatic system for detection of false information circulating on social media is an emerging area of research. It is gaining attention of both industry and academia since US presidential elections 2016. Fake news has negative and severe effects on individuals and organizations elongating its hostile effects on the society. Prediction of fake news in timely manner is important. This research focuses on detection of fake news spreaders. In this context, overall, 6 models are developed during this research, trained and tested with dataset of PAN 2020. Four approaches N-gram based; user statistics-based models are trained with different values of hyper parameters. Extensive grid search with cross validation is applied in each machine learning model. In N-gram based models, out of numerous machine learning models this research focused on better results yielding algorithms, assessed by deep reading of state-of-the-art related work in the field. For better accuracy, author aimed at developing models using Random Forest, Logistic Regression, SVM, and XGBoost. All four machine learning algorithms were trained with cross validated grid search hyper parameters. Advantages of this research over previous work is user statistics-based model and then ensemble learning model. Which were designed in a way to help classifying Twitter users as fake news spreader or not with highest reliability. User statistical model used 17 features, on the basis of which it categorized a Twitter user as malicious. New dataset based on predictions of machine learning models was constructed. And then Three techniques of simple mean, logistic regression and random forest in combination with ensemble model is applied. Logistic regression combined in ensemble model gave best training and testing results, achieving an accuracy of 72%.

• International Journal of Computer Science & Network Security
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• 제22권8호
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• pp.25-34
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• 2022

Today, further education of adults in the UK is one of the developing areas of continuing education. The Open University with distance learning, in the process of which innovative forms and methods based on computer and telecommunication technologies are used, is particularly successful in the organization of additional education of the adult population. The advantages of distance learning, multimedia - the latest information technologies, which provide the combination of graphic images, video, sound with the help of modern computer tools, are noted. The basic principles and forms underlying the technologies and forms of work with the elderly are defined. The international experience of implementing "Universities of the Third Age" is summarized. The most widespread approach in adult education in Great Britain is informational. The use of computer technologies motivates a new paradigm in educational methods and strategies, which requires new approaches, forms of learning, and innovative ways of delivering educational materials to adult learners. Information technologies have gained great popularity in such activities as distance learning, online learning, assistance in the education management system, development of programs and virtual textbooks in various subjects, online search for information for the educational process, computer testing of students' knowledge, creation of electronic libraries, formation of a single scientific electronic environment, publication of virtual magazines and newspapers on pedagogical topics, teleconferences, expansion of international cooperation in the field of Internet education. The information technology of synchronous distance learning "online" has gained considerable popularity in the educational process today. A promising direction is the use of multimedia technologies in educational activities to create a design of a virtual computer environment by decoding audiovisual information.

• 한국문헌정보학회지
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• 제51권2호
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• pp.95-116
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• 2017

본 연구는 문헌정보학 교육에서 프로젝트기반학습의 적용이 학습자의 협력적 자기효능감에 미치는 영향을 살펴보기 위한 연구이다. 이를 위하여 디지털도서관 시스템 평가 과목의 수강생 26명을 대상으로 협력적 자기효능감에 대한 사전설문과 사후설문을 실시하여 그 변화를 측정하였고, 대응표본 t-test를 통해 통계적 유의성을 분석하였고, 프로젝트 학습의 실제성이 협력적 자기효능감의 세부요인들과 상관관계가 있는지도 조사하였다. 분석 결과 프로젝트기반학습법은 학습자의 협력적 자기효능감의 세부하위요인들인 리더의 양상, 의견 교환, 의견 평가, 그리고 의견 통합에 대하여 모두 유의미한 효과가 있다는 것과, 학습의 실제성 중 자원의 실제성이 협력적 자기효능감과 부분적으로 상관관계가 있다는 것을 확인하였다.

• 실천공학교육논문지
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• 제5권2호
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• pp.163-168
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• 2013

국내 대학교육은 높은 진학률과 낮은 취업률이라는 과잉학력의 악순환이 이루어지고 있다. 이러한 악순환을 해소하고, 청년 실업 해소와 청년층의 노동시장 조기 진입을 지원 및 유도하기 위하여 근로자의 '선취업-후학습' 기회를 확대하고, 재직자의 직무능력 향상을 위하여 일과 학습을 병행할 수 있는 교육 훈련 시스템이 필요하다. 최근 MOOC (Massive Open On-line Course)이라는 새로운 형태의 온라인 교육 시스템이 등장 하였다. MOOC는 교육, entertainment, social networking을 결합한 교수-학생, 학생-학생간의 상호작용을 강조한 새로운 형태의 온라인 교육환경으로 강의 콘텐츠를 무료로 제공하고 있다. 이러한 변화된 온라인 교육환경을 활용하여 지식을 온라인으로 효과적으로 제공하고, 기술 및 공학교육에 꼭 필요한 실습교육을 캠퍼스에서 제공하는 멀티학습체제를 구축함으로써 재직자들의 일과 학습을 병행할 수 있도록 지원할 수 있다.

• 한국언어정보학회지:언어와정보
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• 제14권2호
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• pp.159-181
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• 2010

This paper presents a hybrid approach to the sentiment analysis of online texts. The sentiment of a text refers to the feelings that the author of a text has towards a certain topic. Many existing approaches employ either a pattern-based approach or a machine learning based approach. The former shows relatively high precision in classifying the sentiments, but suffers from the data sparseness problem, i.e. the lack of patterns. The latter approach shows relatively lower precision, but 100% recall. The approach presented in the current work adopts the merits of both approaches. It combines the pattern-based approach with the machine learning based approach, so that the relatively high precision and high recall can be maintained. Our experiment shows that the hybrid approach improves the F-measure score for more than 50% in comparison with the pattern-based approach and for around 1% comparing with the machine learning based approach. The numerical improvement from the machine learning based approach might not seem to be quite encouraging, but the fact that in the current approach not only the sentiment or the polarity information of sentences but also the additional information such as target of sentiments can be classified makes the current approach promising.

• 한국콘텐츠학회논문지
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• 제8권5호
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• pp.268-275
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• 2008

학습자간의 협력 작업은 교육용 저작 시스템의 효율성을 결정짓는 중요한 요소 중의 하나이다. 기존의 저작 시스템에서도 다양한 저작 기능이 채용되고 있지만 초보자들이 동료 학습자로부터 도움을 받거나 저작과 학습을 병행하는 것에는 관심이 부족하다. 이에 따라 본 연구에서는 수학적 언어와 학습자간 협력 작업을 이용한 수학교과용 언어기반 저작도구를 설계 및 개발하고, 그 타당성을 검증하였다. 본 연구는 언어기반 저작을 통한 자료 제작의 편리성, 수학적 언어 적용에 따른 저작과 학습의 병행, 네트워크 협력 작업을 통한 학습자간 공조를 주된 내용으로 하고 있다.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제10권9호
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• pp.359-366
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• 2021

대한민국 건설사들은 아파트 하자 정보를 축적하고 보수작업을 관리하기 위한 시스템을 운영하는데 상당한 인력과 비용을 투자하고 있다. 본 연구에서는 하자 접수 상세내용 텍스트 데이터를 이용하여 하자 보수 시설공사에 따른 세부공종을 분류하는 머신러닝 모델을 제안한다. 두 가지 단어 임베딩(Bag-of-words, Term Frequency-Inverse Document Frequency (TF-IDF))과 두 가지 분류기(Support Vector Machine, Random Forest)를 통해 한국어로 작성된 65만건 이상의 하자 접수데이터로부터 하자보수 시설공사 세부공종을 분류했다. 특히, 이번 연구에서는 특정 시설공사(마감공사)의 9개 세부공종(가전제품, 도배공사, 도장공사, 미장공사, 석공사, 수장공사, 옥내가구공사, 주방기구공사, 타일공사)을 분류하는 이진분류 모델과 다중 분류 모델을 연구했다. 그 결과, TF-IDF와 Random Forest를 사용한 두가지 분류 모델에서 90%이상의 정확도, 정밀도, 재현율 및 F1점수를 확인했다.

• 로봇학회논문지
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• 제14권1호
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• pp.1-7
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• 2019

Recently, smart factories have attracted much attention as a result of the 4th Industrial Revolution. Existing factory automation technologies are generally designed for simple repetition without using vision sensors. Even small object assemblies are still dependent on manual work. To satisfy the needs for replacing the existing system with new technology such as bin picking and visual servoing, precision and real-time application should be core. Therefore in our work we focused on the core elements by using deep learning algorithm to detect and classify the target object for real-time and analyzing the object features. We chose YOLO CNN which is capable of real-time working and combining the two tasks as mentioned above though there are lots of good deep learning algorithms such as Mask R-CNN and Fast R-CNN. Then through the line and inside features extracted from target object, we can obtain final outline and estimate object posture.

• 공학교육연구
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• 제25권5호
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• pp.55-67
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• 2022

Distance learning has become an efficient tool and is being widely used at work and in school. This research presents the results of a project-oriented entrepreneurship course taught both in classroom and online for a period of 3 years before and after the pandemic caused by COVID-19. Despite the various challenges, the outcome demonstrated that the students were able to attain required knowledge and capabilities in the online learning environment. As such, this paper discusses effective ways to blend in distance learning components so that both instructors and students can benefit from Internet-based education technologies. In the future, both face-to-face and virtual project work and study are likely to get integrated into a 'hyflex' class, which is flexible, on-offline education.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권3호
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• pp.1228-1248
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• 2020

Steganalysis & steganography have witnessed immense progress over the past few years by the advancement of deep convolutional neural networks (DCNN). In this paper, we analyzed current research states from the latest image steganography and steganalysis frameworks based on deep learning. Our objective is to provide for future researchers the work being done on deep learning-based image steganography & steganalysis and highlights the strengths and weakness of existing up-to-date techniques. The result of this study opens new approaches for upcoming research and may serve as source of hypothesis for further significant research on deep learning-based image steganography and steganalysis. Finally, technical challenges of current methods and several promising directions on deep learning steganography and steganalysis are suggested to illustrate how these challenges can be transferred into prolific future research avenues.