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Ken-ichiro Yoshino

Also published under: K. Yoshino, Ken-Ichiro Yoshino, Ken'ichiro Yoshino

Publications

Citations

151

Publications by Year

20082018

Co-Authors:

A. AllacherT. AsaiShione AsamiBurm BaekA. Dixon

Show All Co-Authors (63)

Ken-ichiro Yoshino

Also published under: K. Yoshino, Ken-Ichiro Yoshino, Ken'ichiro Yoshino

Affiliation

IoT Devices Research Labs.

NEC Corporation

Kawasaki, Japan

Publication Topics

Quantum Key Distribution,
Key Security,
Wavelength Division Multiplexing,
Avalanche Photodiode,
Bit Error Rate,
Key Generation,
Key Rate,
Quantum Key,
Secret Key,
Achievable Rate,
Analog-to-digital Converter,
Bit Error

Biography

Ken-Ichiro Yoshino received the B.E. and M.E. degrees in applied physics from the University of Tokyo, Tokyo, Japan, in 2003 and 2005, respectively. Since 2005, he has been working on research and development of quantum communication/cryptographic system in NEC Corporation, Kawasaki, Japan.(Based on document published on 12 November 2014).

Publications

Citations

151

Publications by Year

20082018

Co-Authors:

A. Allacher
T. Asai
Shione Asami
Burm Baek
A. Dixon

Show All Co-Authors (63)

Author's Published Works

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Ruixin Wang(3)
Ruwen Zhang(2)
Tong Yang(2)
Steve Uhlig(1)
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Peng Cheng Laboratory, Shenzhen, China(2)
National Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China(2)
Electrical and Computer Engineering Department, and the Department of Computer Science, Boston University, Boston, MA, USA(1)
Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA(1)
Guangdong Key Laboratory for Big Data Analysis, Simulation of Public Opinion(1)
2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC)(1)
IEEE Transactions on Knowledge and Data Engineering(1)
IEEE/ACM Transactions on Networking(1)
IEEE(3)
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Kaohsiung, Taiwan(1)
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OneSketch: A Generic and Accurate Sketch for Data Streams

Zhuochen Fan;Ruixin Wang;Yalun Cai;Ruwen Zhang;Tong Yang;Yuhan Wu;Bin Cui;Steve Uhlig

IEEE Transactions on Knowledge and Data Engineering

Year: 2023 | Volume: 35, Issue: 12 | Journal Article |

Cited by: Papers (4)

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In this paper, we propose a generic sketch algorithm capable of achieving more accuracy in the following five tasks: finding top-

$k$ k frequent items, finding heavy hitters, per-item frequency estimation, and heavy changes in the time and spatial dimension. The state-of-the-art (SOTA) sketch solution for multiple measurement tasks is ElasticSketch (ES). However, the accuracy of its frequency estima...Show More

OneSketch: A Generic and Accurate Sketch for Data Streams

Zhuochen Fan;Ruixin Wang;Yalun Cai;Ruwen Zhang;Tong Yang;Yuhan Wu;Bin Cui;Steve Uhlig

IEEE Transactions on Knowledge and Data Engineering

Year: 2023 | Volume: 35, Issue: 12 | Journal Article |

CocoSketch: High-Performance Sketch-Based Measurement Over Arbitrary Partial Key Query

Ruijie Miao;Yinda Zhang;Zihao Zheng;Ruixin Wang;Ruwen Zhang;Tong Yang;Zaoxing Liu;Junchen Jiang

IEEE/ACM Transactions on Networking

Year: 2023 | Volume: 31, Issue: 6 | Journal Article |

Cited by: Papers (2)

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Sketch-based measurement has emerged as a promising solutions due to its high accuracy and resource efficiency. Prior sketches focus on measuring single flow keys and cannot support measurement on multiple keys. This work takes a significant step towards supporting arbitrary partial key queries, which aims to provide information for any key in the predefined range of possible flow keys. The design...Show More

CocoSketch: High-Performance Sketch-Based Measurement Over Arbitrary Partial Key Query

Ruijie Miao;Yinda Zhang;Zihao Zheng;Ruixin Wang;Ruwen Zhang;Tong Yang;Zaoxing Liu;Junchen Jiang

IEEE/ACM Transactions on Networking

Year: 2023 | Volume: 31, Issue: 6 | Journal Article |

Attributed Network Embedding with Data Distribution Adaptation

Jiaxing Chen;Yanghui Rao;Runxuan Chen;Qi Dai;Xiaofeng Li;Ruixin Wang

2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC)

Year: 2018 | Conference Paper |

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Network embedding aims to learn the low-dimensional representations of nodes in networks and preserve the features of networks simultaneously. In this paper, we propose a novel approach of network embedding, named Attributed Network Embedding with Data Distribution Adaptation (ANEDDA). In our model, we consider network structure and node attributes as two kinds of data which have different probabi...Show More

Attributed Network Embedding with Data Distribution Adaptation

Jiaxing Chen;Yanghui Rao;Runxuan Chen;Qi Dai;Xiaofeng Li;Ruixin Wang

2018 5th International Conference on Behavioral, Economic, and Socio-Cultural Computing (BESC)

Year: 2018 | Conference Paper |

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