Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
CCS |
2024-03-27 14:50 |
Hokkaido |
RUSUTSU RESORT |
Dependence on Complexity of Nonlinear Functions for Augmented DFA Method in Deep Learning Based on Optoelectronic Delay System Yuto Nagatsuka, Rin Nogami (Saitama Univ.), Tomoaki Niiyama, Satoshi Sunada (Kanazawa Univ.), Atsushi Uchida, Kazutaka Kanno (Saitama Univ.) CCS2023-46 |
Increasing energy costs in deep learning have led to proposals for fast and efficient optical deep learning hardware. Th... [more] |
CCS2023-46 pp.42-47 |
WIT, IPSJ-AAC |
2024-03-19 12:10 |
Ibaraki |
Tsukuba University of Technology (Primary: On-site, Secondary: Online) |
A Study on the Relationship Between the Linguistic Features of Sign Language and the Accuracy of Imitation
-- Sign Language Imitation Experiment using Illustrations, Video, and HMD Learning Materials -- Tomoya Sato, Takeaki Shionome (Teikyo Univ.) WIT2023-54 |
In sign language learning using a head-mounted display (HMD), the hand-tracking function enables users to learn from a f... [more] |
WIT2023-54 pp.63-68 |
AP |
2024-03-15 10:25 |
Fukui |
UNIVERSITY OF FUKUI (Primary: On-site, Secondary: Online) |
A Study on Path loss characteristics estimation methods considering geographical conditions for designing narrowband DR-IoT communication system Takato Ikegame, Naoki Ikeda, Motonari Imai, Tetsushi Ikegami (Meiji Univ.), Mineo Takai (Osaka Univ.), Susumu Ishihara (Shizuoka Univ.), Arata Kato, Shugo Kajita (STE) AP2023-212 |
A versatile variable-range IoT communication system using the VHF-High band, Diversified-Range IoT (DR-IoT) is being con... [more] |
AP2023-212 pp.63-67 |
SIS |
2024-03-14 15:10 |
Kanagawa |
Kanagawa Institute of Technology (Primary: On-site, Secondary: Online) |
Diagnosis Support System for Oral Mucosal Diseases Development Which Using Tendency of User's Select a Diseases Area Taiyo Sato (NIT), Taishi Ohtani, Manabu Habu, Kazuhiro Tominaga (KDU), Keiichi Horio (Kyutech), Nanto Ozaki (NIT) SIS2023-51 |
The Diagnosis Support System for Oral Mucosal Diseases is a system designed to assist dentists. The input to the system ... [more] |
SIS2023-51 pp.33-38 |
CAS, CS |
2024-03-15 14:45 |
Okinawa |
|
Exploring Uniform Convergence in Neural Networks and its Implication on Generalization Error Zong Xianzhe, Hiroshi Tamura (CHUO Univ.) CAS2023-135 CS2023-128 |
Uniform Convergence, a well-established framework for evaluating generalization in traditional Machine Learning, frequen... [more] |
CAS2023-135 CS2023-128 pp.134-139 |
SIS |
2024-03-15 10:40 |
Kanagawa |
Kanagawa Institute of Technology (Primary: On-site, Secondary: Online) |
A study of low-level Gaussian noise estimating by using machine learning Takashi Suzuki (MicroTechnica), Tomoaki Kimura (Kanagawa institute of technology) SIS2023-57 |
In this study, we investigate a noise estimation method for low-level Gaussian noise with a standard deviation of less t... [more] |
SIS2023-57 pp.67-72 |
RCS, SR, SRW (Joint) |
2024-03-13 16:40 |
Tokyo |
The University of Tokyo (Hongo Campus), and online (Primary: On-site, Secondary: Online) |
A Plug-and-Play Module for Enhancing Fault-Tolerant Distributed Inference Based on Gaussian Dropout Hou Zhangcheng, Ohtsuki Tomoaki (KU) RCS2023-267 |
Distributed inference (DI) in the Internet of Things (IoT) is becoming increasingly important as the demand for AI appli... [more] |
RCS2023-267 pp.77-82 |
IE, MVE, CQ, IMQ (Joint) [detail] |
2024-03-15 13:50 |
Okinawa |
Okinawa Sangyo Shien Center (Primary: On-site, Secondary: Online) |
IMQ2023-87 IE2023-142 MVE2023-116 |
This paper introduces physics-inspired synthesized underwater image dataset (PHISWID).
Deep learning approaches to unde... [more] |
IMQ2023-87 IE2023-142 MVE2023-116 pp.396-401 |
IA, SITE, IPSJ-IOT [detail] |
2024-03-12 16:00 |
Okinawa |
Miyakojima City Future Creation Center (Primary: On-site, Secondary: Online) |
Improvement of Unknown Malicious Domain Detection Based on DNS Query History Analysis Hiroto Yamada, Daiki Nobayashi, Takeshi Ikenaga (kyutech) SITE2023-83 IA2023-89 |
Network users are increasing and there is concern about the malware infection.
In some cases, malware-infected terminal... [more] |
SITE2023-83 IA2023-89 pp.92-97 |
NC, MBE (Joint) |
2024-03-12 14:45 |
Tokyo |
The Univ. of Tokyo (Primary: On-site, Secondary: Online) |
Visualization of the learning process of ResNet revealing its learning dynamics Ryodo Yuge, Takashi Shinozaki (Kindai Univ.) NC2023-59 |
We visualize the impact of skip connections, a key element in residual networks (ResNet), and visualize its impact on th... [more] |
NC2023-59 p.94 |
NC, MBE (Joint) |
2024-03-11 11:40 |
Tokyo |
The Univ. of Tokyo (Primary: On-site, Secondary: Online) |
Basic Consideration for the Effect of Gait Data Measured by a Simple Accelerometer on the Performance of Frailty Symptom Classifiers Takumi Chino (Shinshu Grad school), Mizue Kayama (Shinshu Univ.), Masaki Tachibana (Shinshu Grad school), Taishi Wakitani, Nobuyuki Tachi (Shinshu Univ.), Takashi Nagai (Inst of Tech) MBE2023-71 |
The purpose of this study is to explore the possibility of preventing frailty symptoms through gait characteristics comp... [more] |
MBE2023-71 pp.13-18 |
SS |
2024-03-07 17:45 |
Okinawa |
(Primary: On-site, Secondary: Online) |
For evaluating the effectiveness of CodeT5 transfer learning in refactoring recommendations. Yuto Nakajima, Kenji Fujiwara (Tokyo City University) SS2023-62 |
Refactoring is "the process of restructuring the internal architecture of software to make it easier to understand and m... [more] |
SS2023-62 pp.79-84 |
MI |
2024-03-03 16:54 |
Okinawa |
OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Domain generalization with WSI feature Yuki Shigeyasu (Kyushu Univ.), Shota Harada (Hiroshima City Univ.), Mariyo Kurata, Kazuhiro Terada, Naoki Nakazima (Kyoto Univ.), Akihiko Yoshizawa (Nara Medical Univ.), Hiroyuki Abe, Tetsuo Ushiku (Tokyo Univ.), Ryoma Bise (Kyushu Univ.) MI2023-58 |
In this study, we propose a domain generalization method for pathological images (WSI). Domain shifts in pathological im... [more] |
MI2023-58 pp.81-84 |
MI |
2024-03-04 09:00 |
Okinawa |
OKINAWAKEN SEINENKAIKAN (Primary: On-site, Secondary: Online) |
Distance-informed adversarial learning for metal artifact reduction Daisuke Shigemori, Megumi Nakao (Kyoto Univ.) MI2023-62 |
In this study, we propose an adversarial learning framework that utilises distance information from metal to reduce CT m... [more] |
MI2023-62 pp.95-98 |
PRMU, IBISML, IPSJ-CVIM |
2024-03-04 15:54 |
Hiroshima |
Hiroshima Univ. Higashi-Hiroshima campus (Primary: On-site, Secondary: Online) |
Conversion Prediction in Internet Advertising Using the Law of Diminishing Marginal Utility Keiya Unno (Waseda Univ.), Daichi Iwata, Hiroaki Tanaka (OPT), Masato Uchida (Waseda Univ.) PRMU2023-80 |
The importance of Internet advertising in marketing is increasing every year. An advertising agency is using machine lea... [more] |
PRMU2023-80 pp.168-173 |
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 |
SIP, SP, EA, IPSJ-SLP [detail] |
2024-02-29 10:30 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Multi-task learning with age information model for highly accurate elderly speech recognition. Shine Takumi, Kinouchi Takahiro, Wakabayashi Yukoh, Kitaoka Norihide (TUT) EA2023-64 SIP2023-111 SP2023-46 |
The speech recognition of the elderly is less accurate, especially in smart speaker speech recognition, due to aging-rel... [more] |
EA2023-64 SIP2023-111 SP2023-46 pp.19-24 |
SIP, SP, EA, IPSJ-SLP [detail] |
2024-02-29 17:20 |
Okinawa |
(Primary: On-site, Secondary: Online) |
An Enhanced Privacy-Preserving Scheme for Federated Learning of Vision Transformer without Model Performance Degradation Rei Aso, Sayaka Shiota, Hitoshi Kiya (Tokyo Metropolitan Univ.) EA2023-80 SIP2023-127 SP2023-62 |
Federated learning is a learning method for training models over multiple participants without directly sharing their ra... [more] |
EA2023-80 SIP2023-127 SP2023-62 pp.115-120 |
SIP, SP, EA, IPSJ-SLP [detail] |
2024-02-29 15:10 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Byzantine attack detection via similarity of local updates in federated learning Kenta Ohno, Masao Yamagishi (Hosei Univ.) EA2023-86 SIP2023-133 SP2023-68 |
We propose a method to detect Byzantine attacks in federated learning, as well as a method for identifying clients repea... [more] |
EA2023-86 SIP2023-133 SP2023-68 pp.150-155 |
SIP, SP, EA, IPSJ-SLP [detail] |
2024-03-01 09:30 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Improving training recipe of Remixed2Remixed for speech enhancement Li Li, Shogo Seki (CyberAgent) EA2023-95 SIP2023-142 SP2023-77 |
In the use of deep learning for speech enhancement, supervised learning models that use pairs of clean speech and artifi... [more] |
EA2023-95 SIP2023-142 SP2023-77 pp.202-207 |