Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Hidehito Gomi"'
Publikováno v:
網際網路技術學刊. 22:1169-1181
We propose an effective classification algorithm for machine learning to achieve higher performance for multi-modal behavioral authentication systems. Our algorithm uses a multiclass classification scheme that has a smaller number of classes than the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8bc298779a57fac6d2036f6c1f580b88
https://doi.org/10.53106/160792642021092205019
https://doi.org/10.53106/160792642021092205019
Publikováno v:
Proceedings of the Twentieth ACM Conference on Embedded Networked Sensor Systems.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4811d013d03bc369ef207c36fb1ad4ea
https://doi.org/10.1145/3560905.3568048
https://doi.org/10.1145/3560905.3568048
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031106835
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5be5da8711772de5ae011188815bbd8c
https://doi.org/10.1007/978-3-031-10684-2_5
https://doi.org/10.1007/978-3-031-10684-2_5
Publikováno v:
2021 IEEE 21st International Conference on Software Quality, Reliability and Security Companion (QRS-C).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0a20a4cf28eb96ac02fc92a866bb1313
https://doi.org/10.1109/qrs-c55045.2021.00125
https://doi.org/10.1109/qrs-c55045.2021.00125
Autor:
Shuji Yamaguchi, Hidehito Gomi, Mhd Irvan, Ryosuke Kobayashi, Rie Shigetomi Yamaguchi, Tran Phuong Thao
Publikováno v:
AsiaJCIS
We propose an effective classification algorithm for machine learning to achieve higher performance for multi-modal behavioral authentication systems. Our algorithm uses a multiclass classification scheme that has a smaller number of classes than the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6be7c7d5b2468f4447c30fd8bd8c6bc1
https://doi.org/10.1109/asiajcis50894.2020.00027
https://doi.org/10.1109/asiajcis50894.2020.00027
Publikováno v:
PerCom
People often turn off location logging when the batteries of their smartphones get low, to reduce the phone’s power consumption and prolong its operation. Here, we propose an innovative data sharing scheme called as the Parasitic Location Logging (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0d647fa6cb97c0f5c1b0f58802465d2e
https://doi.org/10.1109/percom45495.2020.9127381
https://doi.org/10.1109/percom45495.2020.9127381
Autor:
Hidehito Gomi, Wataru Oogami
Publikováno v:
IEICE ESS Fundamentals Review. 12:115-125
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::084af7d4b509f9d1a5c1dedc173b0722
https://doi.org/10.1587/essfr.12.2_115
https://doi.org/10.1587/essfr.12.2_115
Autor:
Hidehito Gomi
Publikováno v:
The Journal of The Institute of Image Information and Television Engineers. 70:481-484
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e6885861c7eee85bbd855019e3e78729
https://doi.org/10.3169/itej.70.481
https://doi.org/10.3169/itej.70.481
Publikováno v:
UbiComp/ISWC Adjunct
Sensing our daily activities is essential for ubiquitous computing. Although smartphones and wearable devices can easily collect their owner's data, continuous sensing causes battery drain and consumes CPU power of those devices. This paper proposes
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::98c919e5bcf8b2665d0c5d7b833b80e3
https://doi.org/10.1145/3267305.3267581
https://doi.org/10.1145/3267305.3267581
Publikováno v:
TrustCom/BigDataSE
This paper poses the question, "Is it possible to identify users with just a set of Web activity logs?" The answer is yes: we can provide a continuous authentication system that does not require explicit actions by the users while monitoring their in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dcb115648ca794faf5b5d3901d90f59f
https://doi.org/10.1109/trustcom/bigdatase.2018.00080
https://doi.org/10.1109/trustcom/bigdatase.2018.00080