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Your search keyword '"Byun, Hayoung"' showing total 188 results
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188 results on '"Byun, Hayoung"'

22. Set Reconciliation Using Ternary and Invertible Bloom Filters

Author

Lee, Seungeun, Byun, Hayoung, and Lim, Hyesook

Abstract

Set reconciliation between different hosts to hold the same dataset is an important prerequisite in numerous distributed applications. Sending the entire dataset to achieve set reconciliation is inefficient if the majority of the data owned by each host is the same. Each host is required to send exclusive elements uniquely included in its set to the other to minimize communication complexity. This paper proposes an efficient algorithm for a host to identify its exclusive elements using the recursive comparison of ternary Bloom filters, each representing the signature of a subset of elements and used for filtering out subsets with identical elements. Hence, subsets with exclusive elements are identified, and elements included in the identified subsets are programmed to an invertible Bloom filter (IBF) to be sent to the other host. Thus, the number of elements programmed in the IBF is significantly reduced. Simulation results show that the proposed algorithm provides excellent performance compared with existing set reconciliation algorithms under the constraint of the same amount of data communication.

Published
2023
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27. Position Classification of the Endotracheal Tube with Automatic Segmentation of the Trachea and the Tube on Plain Chest Radiography Using Deep Convolutional Neural Network

Author

Jung, Heui Chul, primary, Kim, Changjin, additional, Oh, Jaehoon, additional, Kim, Tae Hyun, additional, Kim, Beomgyu, additional, Lee, Juncheol, additional, Chung, Jae Ho, additional, Byun, Hayoung, additional, Yoon, Myeong Seong, additional, and Lee, Dong Keon, additional

Published
2022
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38. Learned FBF: Learning-Based Functional Bloom Filter for Key–Value Storage.

Author

Byun, Hayoung and Lim, Hyesook

Subjects
*CONVOLUTIONAL neural networks, *CRANES (Birds), *DATA structures
Abstract

As a challenging attempt to replace a traditional data structure with a learned model, this paper proposes a learned functional Bloom filter (L-FBF) for a key–value storage. The learned model in the proposed L-FBF learns the characteristics and the distribution of given data and classifies each input. It is shown through theoretical analysis that the L-FBF provides a lower search failure rate than a single FBF in the same memory size, while providing the same semantic guarantees. For model training, character-level neural networks are used with pretrained embeddings. In experiments, four types of different character-level neural networks are trained: a single gated recurrent unit (GRU), two GRUs, a single long short-term memory (LSTM), and a single one-dimensional convolutional neural network (1D-CNN). Experimental results prove the validity of theoretical results, and show that the L-FBF reduces the search failures by 82.8% to 83.9% when compared with a single FBF under the same amount of memory used. [ABSTRACT FROM AUTHOR]

Published
2022
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