• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.545-558
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• 2020

The gaze-based input provides feedback to confirm that the typing is correct when the user types the text. Many studies have already demonstrated that feedback can increase the usability of gaze-based inputs. However, because the information of the typed text is revealed through feedback, it can be a target for shoulder-surfing attacks. Appropriate feedback needs to be used to improve security without compromising the usability of the gaze-based input using the original feedback. In this paper, we propose a new gaze-based input method, FFI(Fake Flickering Interface), to resist shoulder-surfing attacks. Through experiments and questionnaires, we evaluated the usability and security of the FFI compared to the gaze-based input using the original feedback.

• Journal of Information Processing Systems
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• v.13 no.5
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• pp.1052-1070
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• 2017

For the autoencoder (AE) implemented as a construction component, this paper uses the method of greedy layer-by-layer pre-training without supervision to construct the stacked autoencoder (SAE) to extract the abstract features of the original input data, which is regarded as the input of the logistic regression (LR) model, after which the click-through rate (CTR) of the user to the advertisement under the contextual environment can be obtained. These experiments show that, compared with the usual logistic regression model and support vector regression model used in the field of predicting the advertising CTR in the industry, the SAE-LR model has a relatively large promotion in the AUC value. Based on the improvement of accuracy of advertising CTR prediction, the enterprises can accurately understand and have cognition for the needs of their customers, which promotes the multi-path development with high efficiency and low cost under the condition of internet finance.

• Journal of the Korean Institute of Landscape Architecture
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• v.27 no.1
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• pp.54-63
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• 1999

The Landscape drawing is used as main media in landscape design process like the language in daily life for human. Designers input many intentions and meaningful words in design process through landscape drawing. The common purpose of landscape drawing is to represent reality effectively, even though it has variable visual forms and materiality. The representation in landscape drawing in metaphorical as well as visual and functional. But current tendency is inclined to use landscape drawing in a functional aspect for visual representation and the landscape drawing is utilized straight-forwardly rather than metaphorically for clear communication. Such recognition on landscape drawing results from the difficulty to accept the symbolic aspect of the drawing. The difficulty makes the utilization and the interpretation of landscape drawing stay at conventional level in following visible factors. For the sake of solving the difficulty this study considers landscape drawing as the text that contains readable objects and symbolic words. This study presents layer-methods for reading a landscape drawing as a text; situational and contextural reading, iconological reading and reading the subject of drawing.

• Journal of information and communication convergence engineering
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• v.16 no.2
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• pp.106-113
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• 2018

Although deep learning methods of convolutional neural networks (CNNs) and long-/short-term memory (LSTM) are widely used for text categorization, they still have certain shortcomings. CNNs require that the text retain some order, that the pooling lengths be identical, and that collateral analysis is impossible; In case of LSTM, it requires the unidirectional operation and the inputs/outputs are very complex. Against these problems, we thus improved these traditional deep learning methods in the following ways: We created collateral CNNs accepting disorder and variable-length pooling, and we removed the input/output gates when creating bidirectional LSTMs. We have used four benchmark datasets for topic and sentiment classification using the new methods that we propose. The best results were obtained by combining LTSM regional embeddings with data convolution. Our method is better than all previous methods (including deep learning methods) in terms of topic and sentiment classification.

• East Asian mathematical journal
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• v.37 no.5
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• pp.599-612
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• 2021

In China, Weibo is one of the social platforms with more users. It has the characteristics of fast information transmission and wide coverage. People can comment on a certain event on Weibo to express their emotions and attitudes. Judging the emotional tendency of users' comments is not only beneficial to the monitoring of the management department, but also has very high application value for rumor suppression, public opinion guidance, and marketing. This paper proposes a two-input Adaboost model based on TextCNN and BiLSTM. Use the TextCNN model that can perform local feature extraction and the BiLSTM model that can perform global feature extraction to process comment data in parallel. Finally, the classification results of the two models are fused through the improved Adaboost algorithm to improve the accuracy of text classification.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.768-770
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• 2013

We propose a symmetric key based cryptography algorithm to encode and decode the text data with limited length using 3-dimensional magic square matrix. To encode the plain text message, input text will be translated into an index of the number stored in the key matrix. Then, Caesar's shift with pre-defined constant value is fabricated to finalize an encryption algorithm. In decode process, Caesar's shift is applied first, and the generated key matrix is used with 2D magic squares to replace the index numbers in ciphertext to restore an original text.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.764-767
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• 2021

While Generative Adversarial Networks (GANs) have seen huge success in image synthesis tasks, synthesizing high-quality images from text descriptions is a challenging problem in computer vision. This paper proposes a method named Text-to-Face Generation Using Multi-Scale Gradients for Conditional Generative Adversarial Networks (T2F-MSGGANs) that combines GANs and a natural language processing model to create human faces has features found in the input text. The proposed method addresses two problems of GANs: model collapse and training instability by investigating how gradients at multiple scales can be used to generate high-resolution images. We show that T2F-MSGGANs converge stably and generate good-quality images.

• Bulletin of the Korean Space Science Society
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• 2000.10a
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• pp.62-62
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• 2000

No Abstract, See Full Text

• Proceedings of the Zoological Society Korea Conference
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• 1999.10b
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• pp.96.1-96
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• 1999

No Abstract, See Full Text

• Proceedings of the Zoological Society Korea Conference
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• 1999.10b
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• pp.97.1-97
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• 1999

No Abstract, See Full Text