Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
IA, ICSS |
2022-06-24 10:00 |
Nagasaki |
Univ. of Nagasaki (Primary: On-site, Secondary: Online) |
Anonymity and Usefulness of combining Random sampling and Patient characteristics format statistics Kenta Kitamura, Irvan Mhd, Rie Shigetomi Yamaguchi (UTokyo) IA2022-10 ICSS2022-10 |
Data utilization has privacy invasion risks. Considering such risks, there is a trade-off between anonymity and usefulne... [more] |
IA2022-10 ICSS2022-10 pp.55-60 |
R |
2021-07-17 14:25 |
Online |
Virtual |
Refined Ensemble Learning Algorithms for Software Bug Prediction
-- Metaheuristic Approach -- Keisuke Fukuda, Tadashi Dohi, Hiroyuki Okamura (Hiroshima Univ.) R2021-19 |
In this paper, we propose to apply three metaheuristic algorithms; latin hypercube sampling, ABC (artificial
bee colon... [more] |
R2021-19 pp.18-23 |
EA, ASJ-H |
2021-07-15 09:55 |
Online |
Online |
Practical considerations on resampling and sampling rate conversion in audio media processing
-- Application of simultaneous measurement of multiple paths based on extended time stretch pulses -- Hideki Kawahara (Wakayama Univ.), Shigeaki Amano (Aichi Shukutoku Univ.) EA2021-2 |
Modern digital audio systems automatically convert the sampling frequency of audio media. It makes the system user-frien... [more] |
EA2021-2 pp.6-9 |
CPSY, RECONF, VLD, IPSJ-ARC, IPSJ-SLDM [detail] |
2021-01-25 17:10 |
Online |
Online |
Low Power EEG Measurement Using Compressed Sensing Consideration of the Sampling Interval Yuki Okabe, Daisuke Kanemoto (Osaka Univ.), Tomoya Mochizuki (Yamanashi Univ.), Osamu Maida, Tetsuya Hirose (Osaka Univ.) VLD2020-53 CPSY2020-36 RECONF2020-72 |
In recent years, wireless EEG measurement devices that cause less discomfort to the subject have attracted much attentio... [more] |
VLD2020-53 CPSY2020-36 RECONF2020-72 pp.80-84 |
QIT (2nd) |
2020-12-10 16:50 |
Online |
Online |
Classical hardness of quantum random circuit sampling with larger error Yasuhiro Kondo, Ryuhei Mori (Tokyo Tech) |
Recently, Google's research team has succeeded in sampling the output of a randomly given 53-qubit quantum circuit 1 mil... [more] |
|
NC, MBE |
2019-12-06 14:40 |
Aichi |
Toyohashi Tech |
Implementation of an FPGA-based energy-efficient MCMC method for 2D Lenz-Ising model Patrick Tchicali, Hayaru Shouno (UEC) MBE2019-54 NC2019-45 |
MCMC methods are arguably one of the most useful sampling methods. MCMC while being very useful and practical remains a ... [more] |
MBE2019-54 NC2019-45 pp.55-60 |
COMP |
2018-09-18 15:20 |
Fukuoka |
Kyusyu Institute of Technology |
Enumeration and Random Sampling of Nonisomorphic Two-Terminal Series-Parallel Graphs Shuhei Denzumi (UTokyo), Takashi Horiyama (Saitama Univ.), Kazuhiro Kurita (Hokudai), Yu Nakahata (NAIST), Hirofumi Suzuki (Hokudai), Kunihiro Wasa (NII), Kazuaki Yamazaki (JAIST) COMP2018-17 |
A graph $G$ is a two-terminal series-parallel graph if (1) $G$ consists of two vertices and an edge between them or (2) ... [more] |
COMP2018-17 pp.55-62 |
ITS, WBS, RCC |
2017-12-15 14:20 |
Okinawa |
Tiruru/Okinawa Jichikaikan |
Same Range Target Detection with Large RCS Difference by Signal Descending Order Subtraction Target Detection Method for Multiple Frequency Random Stepped CPC Radar Takashi Shiba, Daisuke Hirose, Yuya Ota, Manabu Akita, Takayuki Inaba (UEC) WBS2017-76 ITS2017-53 RCC2017-92 |
Random step frequency had been already studied to enlarge maximum detection velocity for multiple frequency stepped rada... [more] |
WBS2017-76 ITS2017-53 RCC2017-92 pp.233-238 |
IBISML |
2017-11-10 13:00 |
Tokyo |
Univ. of Tokyo |
Approximated hyperparameter distribution estimation using Gaussian process and Bayesian optimization Shun Katakami, Hirotaka Sakamoto, Masato Okada (UTokyo) IBISML2017-81 |
In order to reduce the computational cost of Bayesian inference, we propose a method to estimate the Bayesian posterior ... [more] |
IBISML2017-81 pp.333-338 |
VLD, DC, CPSY, RECONF, CPM, ICD, IE, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC (Joint) [detail] |
2017-11-07 09:50 |
Kumamoto |
Kumamoto-Kenminkouryukan Parea |
On low power oriented test pattern compaction using SAT solver Yusuke Matsunaga (Kyushu Univ.) VLD2017-43 DC2017-49 |
This paper proposes a test pattern compaction method under power
consumption constraint, which uses SAT solver based ... [more] |
VLD2017-43 DC2017-49 pp.95-99 |
PRMU |
2017-10-12 11:00 |
Kumamoto |
|
Face identification based on shape spaces randomly generated from facial minute feature points Kazuki Takasaka, Kazuhiro Fukui (Tsukuba Univ.) PRMU2017-66 |
In this paper, we propose a face identification method based on the 3-d structure of facial minute feature points. The p... [more] |
PRMU2017-66 pp.19-24 |
PRMU, IBISML, IPSJ-CVIM [detail] |
2017-09-15 11:00 |
Tokyo |
|
Alternating Circulant Random Features for Semigroup Kernels Yusuke Mukuta, Yoshitaka Ushiku, Tatsuya Harada (UTokyo) PRMU2017-42 IBISML2017-14 |
In this paper, we propose novel random features termed ``alternating circulant random features,'' which are comprised of... [more] |
PRMU2017-42 IBISML2017-14 pp.27-34 |
SIP, CAS, MSS, VLD |
2017-06-20 14:50 |
Niigata |
Niigata University, Ikarashi Campus |
SAT model sampling for test pattern generation considering signal transition activities Yusuke Matsunaga (Kyushu Univ.) CAS2017-21 VLD2017-24 SIP2017-45 MSS2017-21 |
This paper presents a test pattern generation method with considering
signal transition activities using a SAT solver... [more] |
CAS2017-21 VLD2017-24 SIP2017-45 MSS2017-21 pp.107-112 |
VLD, DC, CPSY, RECONF, CPM, ICD, IE (Joint) [detail] |
2016-11-30 09:50 |
Osaka |
Ritsumeikan University, Osaka Ibaraki Campus |
On SAT based test pattern generation for transition faults considering signal activities Yusuke Matsunaga (Kyushu Univ.) VLD2016-63 DC2016-57 |
This paper presents a test pattern generation method with considering
signal transition activities using a SAT solver... [more] |
VLD2016-63 DC2016-57 pp.111-115 |
BioX |
2016-08-19 09:25 |
Miyagi |
|
Generalized Combined Segmentation-Verification for Multi-Script Signatures using Random-Impostor Training Keigo Matsuda, Wataru Ohyama, Tetsushi Wakabayashi (Mie Univ.) BioX2016-14 |
In this paper, we propose an improvement to the method of combined segmentation verification for multi-script signature ... [more] |
BioX2016-14 pp.39-43 |
CCS |
2015-08-07 10:00 |
Hokkaido |
Dai-ichi Takimotokan (Noboribetsu, Hokkaido) |
Randomness and Complexity in "Nano-chaos" Song-Ju Kim (NIMS), Makoto Naruse (NICT), Masashi Aono (TITECH), Hirokazu Hori (U. Yamanashi) CCS2015-41 |
Previously, we performed numerical simulations to explore how optical energy (exciton) transfers in a network of quantum... [more] |
CCS2015-41 pp.67-72 |
NLP, CCS |
2015-06-12 14:00 |
Tokyo |
Waseda Univerisity |
Binary chaotic cryptography using augmented Lorenz equations Kenichiro Cho, Takaya Miyano (Rits Univ) NLP2015-62 CCS2015-24 |
Augmented Lorenz equations are expressed as a star network of N Lorenz subsystems sharing the scalar variable X as the c... [more] |
NLP2015-62 CCS2015-24 pp.135-137 |
NC, MBE |
2015-03-17 13:25 |
Tokyo |
Tamagawa University |
Effects of downsampling on hyperparameter estimation for Markov random field model Hirotaka Sakamoto (Univ. Tokyo), Yoshinori Nakanishi-Ohno (Univ. Tokyo/JSPS), Masato Okada (Univ. Tokyo/RIKEN) MBE2014-174 NC2014-125 |
We investigate effects which downsampling has on latent-variable estimation from image data. Downsampling is essential f... [more] |
MBE2014-174 NC2014-125 pp.325-330 |
VLD, DC, IPSJ-SLDM, CPSY, RECONF, ICD, CPM (Joint) [detail] |
2014-11-26 15:35 |
Oita |
B-ConPlaza |
An efficient calculation of RTN-induced SRAM failure probability Hiromitsu Awano, Masayuki Hiromoto, Takashi Sato (Kyoto Univ.) VLD2014-74 DC2014-28 |
Failure rate degradation of an SRAM cell due to random telegraph noise (RTN) is calculated for the first time. An effic... [more] |
VLD2014-74 DC2014-28 pp.15-20 |
MBE, NC (Joint) |
2014-11-21 12:15 |
Miyagi |
Tohoku University |
Dynamics of compressed sensing at zero temperature Jun-ichi Inoue (Hokkaido Univ.) NC2014-29 |
We discuss dynamics of signal restoring process in compressed
sensing based on the $L_{r}$-minimizer. In particular, ... [more] |
NC2014-29 pp.21-26 |