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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| HIP, HCS, HI-SIGCOASTER [detail] | 2024-05-13 13:20 |
Okinawa | Okinawa Industry Support Center | Strategies to encode non-speech sounds into language: A developmental study Kaede Hattori, Shoko Miyauchi, Kazuhide Hashiya (Kyushu Univ.) |
[more] | |
| 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 10:00 |
Miyazaki | Miyazaki University | Alleviating Persistence in Learning Strategies with a Model of Empathy for Others' Learning Experience
-- Designing Interaction Scenario with a Social Robot -- So Sasaki, Akihiro Kashihara (UEC) ET2023-64 |
Effective learning requires learners to properly use learning strategies according to learning phases. However, it is no... [more] | ET2023-64 pp.69-76 |
| PRMU, MVE, VRSJ-SIG-MR, IPSJ-CVIM | 2024-01-26 15:46 |
Kanagawa | Keio Univ. (Hiyoshi Campus) | PRMU2023-48 |
In the realm of autonomous driving, end-to-end models (E2EDMs) have gained prominence due to their high predictive accur... [more] | PRMU2023-48 pp.46-49 |
| IA | 2024-01-25 16:10 |
Tokyo | Kwansei Gakuin Univiversity, Marunouchi Campus (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Analyzing Cybersecurity Datasets
-- Enhancing Security throughout the Data Life Cycle -- Chidchanok Choksuchat, Sorawit Khamnaewnak, Siwakorn Kasikam, Chanin Maiprom, Suwimon Bureekarn (PSU) IA2023-63 |
Our study identifies and prevents threats in real-time, particularly focusing on the publishing stage of the data lifecy... [more] | IA2023-63 pp.37-39 |
| NS, RCS (Joint) |
2023-12-15 11:45 |
Fukuoka | Kyushu Institute of Technology Tobata campus, and Online (Primary: On-site, Secondary: Online) |
Deep Reinforcement Learning Based Computing Resource Allocation in Fog Radio Access Networks Tong Zhaowei (Kyushu Univ.), Ahmad Gendia (Al-Azhar Univ.), Osamu Muta (Kyushu Univ.) RCS2023-198 |
The integration of artificial intelligence (AI) with fog radio access networks (F-RANs) has garnered significant interes... [more] | RCS2023-198 pp.112-117 |
| HCGSYMPO (2nd) |
2023-12-11 - 2023-12-13 |
Fukuoka | Asia pacific Import Mart (Kitakyushu) (Primary: On-site, Secondary: Online) |
Transition and analysis by mutual learning within a group in incomplete information game in " Hol's der Geier" Shintaro Abe, Kazuki Takahashi, Takashi Takekawa (Kogakuin Univ) |
In perfect information games, AI learned through self-play and achieved high performance. In incomplete information game... [more] | |
| NS | 2023-10-06 15:20 |
Hokkaido | Hokkaidou University + Online (Primary: On-site, Secondary: Online) |
Incentive Mechanism Considering Heterogeneous Privacy Demand Level in Federated Learning with Differential Privacy Shota Miyagoshi, Takuji Tachibana (Univ. Fukui) NS2023-104 |
In federated learning, where multiple data owners participate as clients to perform machine learning, each client shares... [more] | NS2023-104 pp.162-167 |
| TL | 2023-06-10 13:00 |
Online | (Primary: Online, Secondary: On-site) |
How are learning strategies formed through reflection?
-- As an example of adult learning -- Takeshi Sato (Globis) TL2023-4 |
We conducted a survey of participants in actual training programs to find out what strategies working adults use when le... [more] | TL2023-4 pp.13-16 |
| PRMU, IBISML, IPSJ-CVIM [detail] | 2023-03-02 09:00 |
Hokkaido | Future University Hakodate (Primary: On-site, Secondary: Online) |
Toward Regularizing Neural Networks with Meta-Learning Generative Models Shin'ya Yamaguchi (NTT/Kyoto Univ.), Daiki Chijiwa, Sekitoshi Kanai, Atsutoshi Kumagai (NTT), Hisashi Kashima (Kyoto Univ.) PRMU2022-58 IBISML2022-65 |
This paper investigates methods for improving generative data augmentation for deep learning. Generative data augmentati... [more] | PRMU2022-58 IBISML2022-65 pp.1-6 |
| SeMI, IPSJ-UBI, IPSJ-MBL | 2023-03-01 16:40 |
Aichi | (Primary: On-site, Secondary: Online) |
Study of Deep Reinforcement Learning for Wireless Multihop Networks Cui Zhihan, Khun Aung thura phyo, Lim Yuto, Tan Yasuo (JAIST) SeMI2022-113 |
In beyond 5G network, the device-to-device communications has been actively studied. These devices are wirelessly connec... [more] | SeMI2022-113 pp.37-42 |
| IE, ITS, ITE-MMS, ITE-ME, ITE-AIT [detail] | 2023-02-22 10:00 |
Hokkaido | Hokkaido Univ. | ITS2022-60 IE2022-77 |
Unsupervised domain adaptation (UDA) is extremely effective for transferring knowledge from a label-rich source domain t... [more] | ITS2022-60 IE2022-77 pp.101-106 |
| IA | 2023-01-25 15:45 |
Osaka | Osaka Umeda Campus, Kwansei Gakuin University (Osaka) (Primary: On-site, Secondary: Online) |
Predicting the drop out of Prince of Songkla University students using machine learning methods Theerayuth Prasompong, Suwimon Bureekarn, Chidchanok Choksuchat (PSU) IA2022-73 |
In point of students, ‘dropout’ problem in higher education wastes their time and tuition fees. In contrast, universitie... [more] | IA2022-73 pp.36-42 |
| IBISML | 2022-12-22 15:30 |
Kyoto | Kyoto University (Primary: On-site, Secondary: Online) |
[Short Paper]
Semi supervised image classification using unreliable pseudo label Jihong Hu, Yinhao Li, Yen-Wei Chen (Ritsumeikan Univ.) IBISML2022-47 |
Semi-supervised learning (SSL), which automatically annotates unlabeled data with pseudo labels during training, has ach... [more] | IBISML2022-47 pp.24-29 |
| DC | 2022-12-16 15:00 |
Yamaguchi | (Primary: On-site, Secondary: Online) |
Learning of train control measures by means of Deep Q-Network
-- Preliminary study with a single train control -- Shogo Igarashi, Takumi Fukuda, Sei Takahashi, Hideo Nakamura (Nihon Univ), Tetsuya Takata (Kyosan Electric Manufacturing) DC2022-77 |
Although the predictive fuzzy control technique has been put to practical use as a train control strategy for automatic ... [more] | DC2022-77 pp.26-29 |
| PRMU | 2022-12-16 14:10 |
Toyama | Toyama International Conference Center (Primary: On-site, Secondary: Online) |
Sampling Strategies in Data Pruning Ryota Higashi, Toshikazu Wada (Wakayama Univ.) PRMU2022-48 |
Data Pruning is a method of selecting the training data out of an entire training dataset so as to keep the accuracy aft... [more] | PRMU2022-48 pp.85-90 |
| ET | 2022-11-05 13:25 |
Online | Online | Strategy-based Code Sharing Method in a Code Sharing Platform for Encouraging Refinement Activities Shintaro Maeda, Kento Koike, Takahito Tomoto (Tokyo Polytechnic Univ.) ET2022-34 |
Refinement activities are important in learning programming to make codes better. To promote refinement activities, we h... [more] | ET2022-34 pp.25-28 |
| IA, CQ, MIKA (Joint) |
2022-09-15 14:55 |
Hokkaido | Hokkaido Citizens Actives Center (Primary: On-site, Secondary: Online) |
[Invited Talk]
Artificial Intelligence Approaches for Curling Masahito Yamamoto (Hokkaido Univ.) CQ2022-32 |
Curling is a sport in which players compete for points by delivering stones on the ice, and is one of the official sport... [more] | CQ2022-32 p.49 |
| NS, SR, RCS, SeMI, RCC (Joint) |
2022-07-14 13:25 |
Ishikawa | The Kanazawa Theatre + Online (Primary: On-site, Secondary: Online) |
Deep Reinforcement Learning-based IRS-aided Wireless Communication without Channel State Information Hashida Hiroaki, Kawamoto Yuichi, Kato Nei (Tohoku Univ.), Iwabuchi Masashi, Murakami Tomoki (NTT) RCS2022-85 |
Intelligent reflecting surfaces (IRSs) have attracted attention as devices that enable radio propagation, which has been... [more] | RCS2022-85 pp.84-89 |
| KBSE | 2022-03-09 16:50 |
Online | Online (Zoom) | Fairness Testing of Machine Learning Software through a Combinatorial Approach Daniel Perez Morales (AIST/Keio Univ.), Takashi Kitamura (AIST), Shingo Takada (Keio Univ.) KBSE2021-50 |
Machine learning (ML) can be used in decision-making algorithms or classifiers. These classifiers must be tested looking... [more] | KBSE2021-50 pp.54-59 |
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