• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권5호
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• pp.1671-1686
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• 2022

Face manipulation tools represented by Deepfake have threatened the security of people's biological identity information. Particularly, manipulation tools with deep learning technology have brought great challenges to Deepfake detection. There are many solutions for Deepfake detection based on traditional machine learning and advanced deep learning. However, those solutions of detectors almost have problems of poor performance when evaluated on different quality datasets. In this paper, for the sake of making high-quality Deepfake datasets, we provide a preprocessing method based on the image pixel matrix feature to eliminate similar images and the residual channel attention network (RCAN) to resize the scale of images. Significantly, we also describe a Deepfake detector named Cascaded-Hop which is based on the PixelHop++ system and the successive subspace learning (SSL) model. By feeding the preprocessed datasets, Cascaded-Hop achieves a good classification result on different manipulation types and multiple quality datasets. According to the experiment on FaceForensics++ and Celeb-DF, the AUC (area under curve) results of our proposed methods are comparable to the state-of-the-art models.

• 한국과학교육학회지
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• 제23권4호
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• pp.375-385
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• 2003

In an effort to provide information about the effect of Microcomputer-Based Laboratory (MBL) use in science teaching and learning on student achievement and attitudes, a review of research analyzed studies was done between 1981 and 2001, using a meta-analysis procedure. Thirty-seven published and unpublished studies were reviewed. Use of MBL was shown to be potentially effective in the following condition of class; two students, physics teaching, more than one topic, or at the college level. Appropriate research design strategies, financial support (including hardware and software), and the use of more than one instrument for assessing the effect of the MBL instruction are crucial factors for more informative research studies. While helpful in many respects, the prior research revealed a number of problems related to the use of MBL in school science teaching and learning. The prior research does not support the desired intention described in the theory-based outcomes and reveals so little about how teachers and students use MBL, how it influences their teaching and learning, and how effectively it fits into the existing science curriculum. In order to know if the integration of MBL in the existing school science is worth it or not, more careful research design and comprehensive research should be done.

• Investigative Magnetic Resonance Imaging
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• 제23권2호
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• pp.81-99
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• 2019

Recently, deep learning methods have shown great potential in various tasks that involve handling large amounts of digital data. In the field of MR imaging research, deep learning methods are also rapidly being applied in a wide range of areas to complement or replace traditional model-based methods. Deep learning methods have shown remarkable improvements in several MR image processing areas such as image reconstruction, image quality improvement, parameter mapping, image contrast conversion, and image segmentation. With the current rapid development of deep learning technologies, the importance of the role of deep learning in MR imaging research appears to be growing. In this article, we introduce the basic concepts of deep learning and review recent studies on various MR image processing applications.

• 대한지구과학교육학회지
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• 제8권2호
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• pp.164-182
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• 2015

The purpose of this study was investigated the perceptions and expectations of science gifted elementary students in the laboratory-based science learning. For the purpose of this study, semi-structured interviews were conducted with 20 science gifted elementary students in J city. The question of the interview is constructed with perception and expectation of science gifted elementary students in divided with 4steps of understanding of lesson object, planning experiment, performing experiment and drawing conclusion in laboratory-based science learning and an attitude for science. The interview is progressed per individual and all the content of the interview is recorded. The result of this research is as follows. The science gifted elementary students have a wish for building an assumption and expectation and planning an experiment with discussion more than following the textbook and teacher present. In the step of the experiment, they wanted general more discussion of their own activities rather than teacher's instruction and they wanted teacher's instruction and they wanted teacher's mediation conflicts within small groups and comments for students' experiment results. The science gifted elementary students wish to open a science lab, which man who likes science can go and come freely and to study with friends who have a same interest to make a theme. And from top to bottom they want to test autonomous and ask to salute like a representative experiment of teacher. And they ask to have a chance to test individually and want to see a movie related to an experiment before doing an experiment. Like this, it presents that the scientifically gifted elementary students want to do an experiment what they can, want to have a class which can plan and can do an experiment by themselves through discussion with the unit more than following explanation of a teacher and a textbook without condition.

• Smart Structures and Systems
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• 제33권2호
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• pp.105-118
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• 2024

In order to realize tiny bridge crack discovery by UAV-based machine vision, a novel method combining deep learning and tensor voting is proposed. Firstly, the grid images of crack are detected and descripted based on SE-ResNet50 to generate feature points. Then, the probability significance map of crack image is calculated by tensor voting with feature points, which can define the direction and region of crack. Further, the crack detection anchor box is formed by non-maximum suppression from the probability significance map, which can improve the robustness of tiny crack detection. Finally, a case study is carried out to demonstrate the effectiveness of the proposed method in the Xiangjiang-River bridge inspection. Compared with the original tensor voting algorithm, the proposed method has higher accuracy in the situation of only 1-2 pixels width crack and the existence of edge blur, crack discontinuity, which is suitable for UAV-based bridge crack discovery.

• Genomics & Informatics
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• 제2권2호
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• pp.99-106
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• 2004

In this paper we introduce PubMiner, an intelligent machine learning based text mining system for mining biological information from the literature. PubMiner employs natural language processing techniques and machine learning based data mining techniques for mining useful biological information such as protein­protein interaction from the massive literature. The system recognizes biological terms such as gene, protein, and enzymes and extracts their interactions described in the document through natural language processing. The extracted interactions are further analyzed with a set of features of each entity that were collected from the related public databases to infer more interactions from the original interactions. An inferred interaction from the interaction analysis and native interaction are provided to the user with the link of literature sources. The performance of entity and interaction extraction was tested with selected MEDLINE abstracts. The evaluation of inference proceeded using the protein interaction data of S. cerevisiae (bakers yeast) from MIPS and SGD.

• ETRI Journal
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• 제38권6호
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• pp.1207-1217
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• 2016

The general method of machine learning has encountered disadvantages in terms of the significant amount of time and effort required for feature extraction and engineering in natural language processing. However, in recent years, these disadvantages have been solved using deep learning. In this paper, we propose a mention pair (MP) model using deep learning, and a system that combines both rule-based and deep learning-based systems using a guided MP as a coreference resolution, which is an information extraction technique. Our experiment results confirm that the proposed deep-learning based coreference resolution system achieves a better level of performance than rule- and statistics-based systems applied separately

• Journal of Information Processing Systems
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• 제18권4호
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• pp.535-548
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• 2022

Using a rich resource language to classify sentiments in a language with few resources is a popular subject of research in natural language processing. Burmese is a low-resource language. In light of the scarcity of labeled training data for sentiment classification in Burmese, in this study, we propose a method of transfer learning for sentiment analysis of a language that uses the feature transfer technique on sentiments in English. This method generates a cross-language word-embedding representation of Burmese vocabulary to map Burmese text to the semantic space of English text. A model to classify sentiments in English is then pre-trained using a convolutional neural network and an attention mechanism, where the network shares the model for sentiment analysis of English. The parameters of the network layer are used to learn the cross-language features of the sentiments, which are then transferred to the model to classify sentiments in Burmese. Finally, the model was tuned using the labeled Burmese data. The results of the experiments show that the proposed method can significantly improve the classification of sentiments in Burmese compared to a model trained using only a Burmese corpus.

• 대한화학회지
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• 제54권6호
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• pp.771-780
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• 2010

본 연구에서는 서울시 소재 대학의 "무기화학실험" 수강생을 대상으로 문제 중심 학습(Problem-based Learning; PBL) 전략을 적용한 실험 수업을 한 학기 동안 진행한 후, PBL 전략이 '자기 주도 학습 능력'과 '과학에 대한 태도'에 미치는 영향을 알아보았다. 이와 더불어 실험 수업에 적용한 PBL 문제와 PBL 실험 수업 과정에 대한 학생들의 인식을 조사하였다. 연구 결과는 다음과 같다. 첫째, PBL 전략을 적용한 "무기화학실험" 수업 후, 학생들의 '자기 주도 학습 능력'과 '과학에 대한 태도'가 통계적으로 유의미한 차이를 나타냈다(p < .05). 특히, '자기 주도 학습 능력'은 7개의 하위 영역 중 '학습자적 신념'을 제외한 6개의 영역에서, '과학에 대한 태도'는 5개의 하위 영역 중 '과학의 유용성'을 제외한 4개의 영역에서 유의미한 차이를 보였다(p < .05). 둘째, 학생들은 PBL 문제가 '자기 주도 학습'을 가능하게 하며 책임감을 가지고 학습할 수 있도록 하는 기회를 제공한다고 응답하였다. 하지만 스스로 문제를 정의하면서 학습 과제를 선정해 나가는 수업 과정에 대해서는 어렵다고 응답하였다. 셋째, 학생들은 PBL 실험 수업을 통해 효과적인 학습을 할 수 있었다고 생각하였으며, PBL이 실험 교과에 적합하고 자기 주도적으로 학습을 할 수 있도록 하는 수업 방식이라고 응답하였다.

• Journal of Computing Science and Engineering
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• 제7권2호
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• pp.112-121
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• 2013

The last years have seen a rise of interest in using electroencephalography-based brain computer interfacing methodology for investigating non-medical questions, beyond the purpose of communication and control. One of these novel applications is to examine how signal quality is being processed neurally, which is of particular interest for industry, besides providing neuroscientific insights. As for most behavioral experiments in the neurosciences, the assessment of a given stimulus by a subject is required. Based on an EEG study on speech quality of phonemes, we will first discuss the information contained in the neural correlate of this judgement. Typically, this is done by analyzing the data along behavioral responses/labels. However, participants in such complex experiments often guess at the threshold of perception. This leads to labels that are only partly correct, and oftentimes random, which is a problematic scenario for using supervised learning. Therefore, we propose a novel supervised-unsupervised learning scheme, which aims to differentiate true labels from random ones in a data-driven way. We show that this approach provides a more crisp view of the brain states that experimenters are looking for, besides discovering additional brain states to which the classical analysis is blind.