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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| ET, IPSJ-CLE | 2024-06-15 09:55 |
Osaka | Kindai University, Higashi-Osaka Campus (Primary: On-site, Secondary: Online) |
Method for Identifying Logic Errors by Clustering Source Codes with a Focus on Program Structure Yuta Harada, Soichiro Sato (Tokyo Gakugei Univ.), Shoichi Nakamura (Fukushima Univ.), Youzou Miyadera (Tokyo Gakugei Univ.) |
(To be available after the conference date) [more] | |
| NLP, CCS | 2024-06-06 09:30 |
Fukuoka | West Japan General Exhibition Center AIM | Tug-of-war algorithm for collective decision making with a laser network Shun Kotoku, Takatomo Mihana, Andre Roehm, Ryoichi Horisaki (UTokyo) |
(To be available after the conference date) [more] | |
| EA | 2024-05-22 13:50 |
Online | Online | Determined BSS based on the proximal average of IVA and DNNs Kazuki Matsumoto (Waseda Univ.), Koki Yamada, Kohei Yatabe (TUAT)  EA2024-3 |
Determined BSS separates source signals by applying the separation matrices, which are estimated under some assumption o... [more] | EA2024-3 pp.14-19 |
| CQ, CS (Joint) |
2024-05-17 14:35 |
Aichi | (Primary: On-site, Secondary: Online) |
A study on a method for estimating the optimal pear pollen collection time Keita Endo (NIT), Tomotaka Kimura (Doshisha Univ.), Hiroyuki Shimizu (NIT), Tomohito Shimada (SATRC), Akane Shibasaki (SAFPC), Chisa Suzuki (SATRC), Ryota Fujinuma (DKK), Yoshihiro Takemura (Tottori Univ.), Takefumi Hiraguri (NIT) CQ2024-14 |
Pear pollination is generally done by artificial pollination, and pollen collection is necessary for artificial pollinat... [more] | CQ2024-14 pp.42-48 |
| NLP | 2024-05-10 10:30 |
Kagawa | Kagawa Prefecture Social Welfare Center | Federated Learning Algorithms based on Decentralized Spanning Tree Generation and Step-by-Step Consensus Yuki Mori, Tatsuya Kayatani, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NLP2024-11 |
A large amount of high-quality data is necessary to improve the learning accuracy of neural networks. However, there are... [more] | NLP2024-11 pp.52-57 |
| DC, CPSY, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC [detail] | 2024-03-23 11:45 |
Nagasaki | Ikinoshima Hall (Primary: On-site, Secondary: Online) |
Evaluating composition of quantum circuit and learnability in quantum neural network with NISQ devices Naoki Marumo (Waseda Univ.), Yasutaka Wada (Meisei Univ.), Kazunori Ueda, Keiji Kimura (Waseda Univ.) CPSY2023-52 DC2023-118 |
The more numbers of repeat of Ansatz and the more qubit entangling improve learnability of quantum machine learning by v... [more] | CPSY2023-52 DC2023-118 pp.82-87 |
| KBSE | 2024-03-14 15:40 |
Okinawa | Okinawa Prefectual General Welfare Center (Primary: On-site, Secondary: Online) |
An approach for improving perceived safety in autonomous driving using personalized shielding Ryotaro Abe, Jialong Li, Jinyu Cai (Waseda Univ.), Shinichi Honiden (NII), Kenji Tei (Tokyo Tech) KBSE2023-76 |
This research introduces an innovative Reinforcement Learning (RL) approach tailored for autonomous driving systems, ter... [more] | KBSE2023-76 pp.67-72 |
| RCC, ISEC, IT, WBS | 2024-03-13 - 2024-03-14 |
Osaka | Osaka Univ. (Suita Campus) | An improvement method and security evaluation for the method of protecting ownership using digital watermark Dung Ta Anh, Hidema Tanaka (NDA) IT2023-76 ISEC2023-75 WBS2023-64 RCC2023-58 |
Since developing high-performance AI models requires significant time and cost, it is common to customize publicly avail... [more] | IT2023-76 ISEC2023-75 WBS2023-64 RCC2023-58 pp.5-11 |
| IE, MVE, CQ, IMQ (Joint) [detail] |
2024-03-15 09:50 |
Okinawa | Okinawa Sangyo Shien Center (Primary: On-site, Secondary: Online) |
IMQ2023-68 IE2023-123 MVE2023-97 |
We propose a simultaneous method of multimodal graph signal denoising and graph learning. Since sensor networks distribu... [more] | IMQ2023-68 IE2023-123 MVE2023-97 pp.301-306 |
| PRMU, IBISML, IPSJ-CVIM | 2024-03-04 10:40 |
Hiroshima | Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Poisoning Attack on Fairness of Fair Classification Algorithm through Threshold Control Dai Shengtian, Akimoto Youhei (Univ. of Tsukuba/RIKEN), Jun Sakuma (Tokyo Tech./RIKEN), Fukuchi Kazuto (Univ. of Tsukuba/RIKEN) IBISML2023-47 |
The ethical issues of artificial intelligence have become more severe as machine learning is widely used in several fiel... [more] | IBISML2023-47 pp.49-56 |
| ET | 2024-03-03 13:45 |
Miyazaki | Miyazaki University | Examination on Adaptive Questions in Braille Learning using Multi-Armed Bandits Algorithm Yasuhisa Okazaki, Jevri Tri Ardiansah (Saga Univ.) ET2023-70 |
In adaptive learning, it is desirable to appropriately present the next topic for each learner to learn. A typical metho... [more] | ET2023-70 pp.110-115 |
| AI | 2024-03-01 13:40 |
Aichi | Room0221, Bldg.2-C, Nagoya Institute of Technology | Applying Graph Neural Networks and Reinforcement Learning to the Multiple Depot-Multiple Traveling Salesman Problem Dongyeop Kim, Toshihiro Matsui (NITech) AI2023-39 |
In this study, we introduce a method combining Graph Neural Networks (GNN) and reinforcement learning for the Multiple D... [more] | AI2023-39 pp.13-18 |
| SIP, SP, EA, IPSJ-SLP [detail] | 2024-02-29 16:20 |
Okinawa | (Primary: On-site, Secondary: Online) |
Comparison of DNN architectures for determined BSS by proximal average of IVA and DNN Kazuki Matsumoto (Waseda Univ.), Koki Yamada, Kohei Yatabe (TUAT) EA2023-88 SIP2023-135 SP2023-70 |
We have proposed a framework called PA-BSS for high-performance separation matrix estimation using deep denoisers based ... [more] | EA2023-88 SIP2023-135 SP2023-70 pp.162-167 |
| NS, IN (Joint) |
2024-03-01 11:35 |
Okinawa | Okinawa Convention Center | Application of a Deep Reinforcement Learning Algorithm to Virtual Machine Migration Control in Multi-Stage Information Processing Systems Yuki Kojitani (Okayama Univ.), Kazutoshi Nakane (Nagoya Univ.), Yuya Tarutani (Okayama Univ.), Celimuge Wu (UEC), Yusheng Ji (NII), Tokumi Yokohira (Okayama Univ.), Tutomu Murase (Nagoya Univ.), Yukinobu Fukushima (Okayama Univ.) IN2023-87 |
This paper tackles a virtual machine (VM) migration control problem to maximize the progress (accuracy) of information p... [more] | IN2023-87 pp.130-135 |
| CQ, CBE (Joint) |
2024-01-26 13:15 |
Kumamoto | Kurokawa-Onsen (Primary: On-site, Secondary: Online) |
Estimating the best time to collect pear pollen using deep learning Keita Endo (NIT), Tomotaka Kimura (Doshisha Univ.), Hiroyuki Shimizu (NIT), Tomohito Shimada (SATRC), Akane Shibasaki (SAFPC), Ryota Fujinuma (DKK), Yoshihiro Takemura (Tottori Univ.), Takefumi Hiraguri (NIT) CQ2023-65 |
Pear pollination is generally done by artificial pollination, and pollen collection is necessary for artificial pollinat... [more] | CQ2023-65 pp.68-75 |
| SIP, IT, RCS | 2024-01-18 11:45 |
Miyagi | (Primary: On-site, Secondary: Online) |
A Study on Massive MIMO Channel Estimation Based on Sparse Bayesian Learning Using Hierarchical Model Kengo Furuta, Takumi Takahashi, Kenta Ito (Osaka Univ.), Shinsuke Ibi (Doshisha Uni.) IT2023-34 SIP2023-67 RCS2023-209 |
Massive multi-input multi-output (MIMO) channels are known to have pseudo-sparsity in the angular (beam) domain, and it ... [more] | IT2023-34 SIP2023-67 RCS2023-209 pp.25-30 |
| SIP, IT, RCS | 2024-01-19 14:30 |
Miyagi | (Primary: On-site, Secondary: Online) |
Infant Detection in Passenger Vehicles Using Millimeter Wave FMCW-MIMO Radar and CFAR Algorithm Kotone Sato, Steven Wandale, Koichi Ichige (Yokohama National Univ.), Kazuya Kimura, Ryo Sugiura (Murata Manufacturing) IT2023-71 SIP2023-104 RCS2023-246 |
This paper implements several proposed features using the CFAR algorithm, then constructs a concise decision tree model ... [more] | IT2023-71 SIP2023-104 RCS2023-246 pp.223-228 |
| SS, MSS | 2024-01-17 14:30 |
Ishikawa | (Primary: On-site, Secondary: Online) |
Extrinsicaly Rewarded Soft Q Imitation Learning with Discriminator Ryoma Furuyama, Daiki Kuyoshi, Yamane Satoshi (Kanazawa Univ.) MSS2023-55 SS2023-34 |
Imitation learning is often used in addition to reinforcement learning in environments where reward design is difficult ... [more] | MSS2023-55 SS2023-34 pp.19-24 |
| AP, WPT (Joint) |
2024-01-18 14:00 |
Niigata | Tokimate, Niigata University (Primary: On-site, Secondary: Online) |
[Tutorial Lecture]
Reinforcement learning and its computer simulation Hitoshi Kono (Tokyo Denki Univ.) AP2023-170 |
Reinforcement learning is a learning algorithm in which an agent selects actions through trial and error and explores fo... [more] | AP2023-170 pp.58-61 |
| SS, MSS | 2024-01-18 11:30 |
Ishikawa | (Primary: On-site, Secondary: Online) |
Deep Reinforcement Learning Using LMM's Studying Papers and Intrinsic Rewards Sota Nagano, Satoshi Yamane (Kanazawa Univ.) MSS2023-64 SS2023-43 |
Research combining deep reinforcement learning with a large language model (LLM) produced high scores even for open-worl... [more] | MSS2023-64 SS2023-43 pp.70-75 |
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