• 제목/요약/키워드: text input

검색결과 358건 처리시간 0.026초

아이트래킹을 이용한 안전한 패스워드 입력 방법에 관한 연구 - 숄더 서핑 공격 대응을 중심으로 (A Study of Secure Password Input Method Based on Eye Tracking with Resistance to Shoulder-Surfing Attacks)

  • 김슬기;유상봉;장윤;권태경
    • 정보보호학회논문지
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    • 제30권4호
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    • pp.545-558
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    • 2020
  • 시선 기반 입력은 사용자가 문자를 입력하였을 때, 입력이 제대로 되었음을 확인하기 위한 피드백을 제공한다. 이미 많은 연구에서 적절한 피드백이 시선 기반 입력의 사용성을 높이는 효과가 있음을 증명하였다. 그러나 피드백을 통해 입력된 문자에 대한 정보가 노출되어 숄더 서핑 공격의 대상이 될 수 있다. 적절한 피드백을 활용하여 기존의 사용성을 유지하며 보안성을 향상시킬 필요가 있다. 본 연구에서는 숄더 서핑 공격에 대응하기 위한 새로운 시선 기반 입력 방법인 FFI(Fake Flickering Interface)를 제안한다. 또한, 실험 및 설문을 통해 기존의 피드백을 활용한 시선 기반 입력과 비교하여 FFI의 사용성과 보안성을 평가한다.

A CTR Prediction Approach for Text Advertising Based on the SAE-LR Deep Neural Network

  • Jiang, Zilong;Gao, Shu;Dai, Wei
    • Journal of Information Processing Systems
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    • 제13권5호
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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.

텍스트로서의 조경드로잉 - 읽기의 틀과 실제 - (Landscape Drawing as a Text: Practical and Theoretical Approach)

  • 이광빈;조정송
    • 한국조경학회지
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    • 제27권1호
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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.

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Text Categorization with Improved Deep Learning Methods

  • Wang, Xingfeng;Kim, Hee-Cheol
    • Journal of information and communication convergence engineering
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    • 제16권2호
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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.

RESEARCH ON SENTIMENT ANALYSIS METHOD BASED ON WEIBO COMMENTS

  • Li, Zhong-Shi;He, Lin;Guo, Wei-Jie;Jin, Zhe-Zhi
    • East Asian mathematical journal
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    • 제37권5호
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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.

A Symmetric Key Cryptography Algorithm by Using 3-Dimensional Matrix of Magic Squares

  • 이상호;김시호;정광호
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2013년도 추계학술발표대회
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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.

다중 스케일 그라디언트 조건부 적대적 생성 신경망을 활용한 문장 기반 영상 생성 기법 (Text-to-Face Generation Using Multi-Scale Gradients Conditional Generative Adversarial Networks)

  • ;;추현승
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2021년도 추계학술발표대회
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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.