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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| 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.) |
(To be available after the conference date) [more] | |
| RCC, ISEC, IT, WBS | 2024-03-13 15:05 |
Osaka | Osaka Univ. (Suita Campus) | Efficient Replay Data Selection in Continual Federated Learning Model Yuto Kitano (Kobe Univ), Lihua Wang (NICT), Seiichi Ozawa (Kobe Univ)  IT2023-96 ISEC2023-95 WBS2023-84 RCC2023-78 |
In this study, we propose a continual federated learning that can continuously learn distributed data generated daily by... [more] | IT2023-96 ISEC2023-95 WBS2023-84 RCC2023-78 pp.135-141 |
| IE, MVE, CQ, IMQ (Joint) [detail] |
2024-03-13 11:50 |
Okinawa | Okinawa Sangyo Shien Center (Primary: On-site, Secondary: Online) |
Effects of road width changes by street parking on driving in a driving simulator Sora Iida, Masahiro Fukui, Takumi Takaku, Satoshi Nakamura (Meiji Univ.), Shota Yamanaka (LY) IMQ2023-18 IE2023-73 MVE2023-47 |
For proper car navigation, it is necessary to model driving difficulty according to road geometry. In our previous studi... [more] | IMQ2023-18 IE2023-73 MVE2023-47 pp.29-34 |
| NLP, MSS | 2024-03-14 10:00 |
Misc. | Kikai-Shinko-Kaikan Bldg. | Extraction of Traffic Accident High-Risk Areas Using Deep Learning of Map Images and Grad-CAM Kaito Arase, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) MSS2023-86 NLP2023-138 |
An attempt has been made to predict the traffic accident risk of each map tile image at zoom level 17 using a Convolutio... [more] | MSS2023-86 NLP2023-138 pp.71-76 |
| SS | 2023-03-15 09:55 |
Okinawa | (Primary: On-site, Secondary: Online) |
Regularity Preservation Property of Data Tree Rewrite Systems
-- A Subclass Decomposable into Monadic Normal Form -- Yuto Sakao, Hiroyuki Seki (Nagoya Univ.) SS2022-62 |
Let $T$ be a transformation over a class $mathcal{L}$ of languages. If for any regular language $L in mathcal{L}$, $T^*(... [more] | SS2022-62 pp.91-96 |
| LOIS | 2023-03-13 10:25 |
Okinawa | (Primary: On-site, Secondary: Online) |
Estimation method of difficulty of cases and proficiency of persons in charge of cases using amount of work until completion of each case Akinori Miyamoto, Susumu Ogata, Akira Karasudani (Fujitsu) LOIS2022-45 |
The automation of business using information technology is being developed. However, in business that cannot be fully au... [more] | LOIS2022-45 pp.5-10 |
| RISING (3rd) |
2022-10-31 10:30 |
Kyoto | Kyoto Terrsa (Day 1), and Online (Day 2, 3) | [Poster Presentation]
Delay Analysis Model of Service Chain Configuration for Fat-Tree Topology Tomonori Yokono (Univ. Fukui), Yuhei Hayashi (NTT), Shohei Kamamura (Seikei Univ.), Takuji Tachibana (Univ. Fukui) |
When a service chain is constructed in a data center network, the total transmission delay is expected to be reduced by ... [more] | |
| IBISML | 2022-09-15 14:00 |
Kanagawa | Keio Univ. (Yagami Campus) (Primary: On-site, Secondary: Online) |
Interpretable Model Combining statements and DNN Ryo Okuda, Yuya Yoshikawa (STAIR) IBISML2022-36 |
In this study, we propose a method that achieves both interpretability of Decision Tree and the prediction accuracy of D... [more] | IBISML2022-36 pp.25-30 |
| IT | 2022-07-22 14:40 |
Okayama | Okayama University of Science (Primary: On-site, Secondary: Online) |
Meta-Tree Set Construction for Approximate Bayes Optimal Prediction on Decision Tree Model Keito Tajima, Naoki Ichijo, Koshi Shimada, Toshiyasu Matsushima (Waseda Univ.) IT2022-27 |
Decision trees are generally used as a predictive function, but some studies use decision trees as data-generative model... [more] | IT2022-27 pp.61-66 |
| IT | 2022-07-22 15:05 |
Okayama | Okayama University of Science (Primary: On-site, Secondary: Online) |
Bayes Optimal Approximation Algorithm by Boosting-like Construction of Meta-Tree Sets in Classification on Decision Tree Model Ryota Maniwa, Naoki Ichijo, Koshi Shimada, Toshiyasu Matsushima (Waseda Univ.) IT2022-28 |
Decision trees are used for classification and regression such as predicting the objective variable corresponding to the... [more] | IT2022-28 pp.67-72 |
| CCS, NLP | 2022-06-09 17:15 |
Osaka | (Primary: On-site, Secondary: Online) |
Visualization of decisions from CNN models trained on OpenStreetMap images labeled based on traffic accident data Kaito Arase, Zhijian Wu, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NLP2022-10 CCS2022-10 |
The authors have recently conducted training of Convolutional Neural Networks (CNNs) on OpenStreetMap images each of whi... [more] | NLP2022-10 CCS2022-10 pp.46-51 |
| COMP, IPSJ-AL | 2022-05-19 15:40 |
Online | Online | On Time Complexity of Distributed Minimum Spanning Tree Construction in the broadcast-CONGEST model for Restricted Graph Classes Narumi Shigekiyo, Toshimitsu Masuzawa, Taisuke Izumi (Osaka Univ.) COMP2022-5 |
Broadcast-CONGEST is a variant of CONGEST, the standard computational model for distributed graph algorithms, with the r... [more] | COMP2022-5 pp.33-38 |
| IT, EMM | 2022-05-18 12:40 |
Gifu | Gifu University (Primary: On-site, Secondary: Online) |
On Bayesian Approach for Classification of Context Tree Model Shota Saito (Gunma Univ.) IT2022-11 EMM2022-11 |
This study deals with the Bayesian classification problem, which was investigated by Merhav and Ziv [IEEE Trans. Inf. Th... [more] | IT2022-11 EMM2022-11 pp.56-60 |
| IT, ISEC, RCC, WBS | 2022-03-11 15:35 |
Online | Online | The explicit formula for the distributions of nonoverlapping words Hayato Takahashi (Random Data Lab.) IT2021-123 ISEC2021-88 WBS2021-91 RCC2021-98 |
Generating functions of the distributions of number of occurences of words and approximation of these distributions ar... [more] |
IT2021-123 ISEC2021-88 WBS2021-91 RCC2021-98 pp.234-236 |
| SS | 2022-03-07 14:30 |
Online | Online | Learning Assumptions for Compositional Verification of Timed Systems with Tree Queries Kotaro Niimi, Shoji Yuen (Nagoya Univ) SS2021-49 |
This paper presents an automatic assumption-learning for compositional verification of timed systems. We focus on Assume... [more] | SS2021-49 pp.43-48 |
| MI | 2022-01-26 10:00 |
Online | Online | MI2021-52 |
In this report, we propose a method for constructing a statistical intensity model of blood vessels in thoracic CT image... [more] | MI2021-52 pp.39-40 |
| SeMI | 2022-01-21 15:20 |
Nagano | (Primary: On-site, Secondary: Online) |
[Short Paper]
Asynchronous Gradient-Boosted Decision Trees for Distributed Sensing Devices Yui Yamashita, Akihito Taya, Yoshito Tobe (Aoyama Gakuin Univ.) SeMI2021-64 |
Recently, wearable devices that install multiple sensors have been widely used. Although sensor data from these devices ... [more] | SeMI2021-64 pp.45-47 |
| WBS, IT, ISEC | 2021-03-04 10:55 |
Online | Online | An Efficient Bayes Coding Algorithm for the Source Based on Context Tree Models that Vary from Section to Section Koshi Shimada, Shota Saito, Toshiyasu Matsushima (Waseda Univ.) IT2020-115 ISEC2020-45 WBS2020-34 |
In this paper, we present an efficient coding algorithm for a non-stationary source based on context tree models that ve... [more] | IT2020-115 ISEC2020-45 WBS2020-34 pp.19-24 |
| SS | 2021-03-04 15:50 |
Online | Online | Untangling Composite Changes Using Tree-based Convolution Neural Network Cong Li, Takashi Kobayashi (Tokyo Tech) SS2020-46 |
Developers often bundle unrelated changes in a single commit, thus creating a so-called composite commit. Composite comm... [more] | SS2020-46 pp.108-113 |
| MSS, SS | 2021-01-27 15:25 |
Online | Online | Pumping Lemmas for Languages Expressed by Computational Models with Registers Rindo Nakanishi, Ryoma Senda (Nagoya Univ.), Yoshiaki Takata (KUT), Hiroyuki Seki (Nagoya Univ.) MSS2020-41 SS2020-26 |
Register automaton (RA), register context-free grammar (RCFG), and register tree automaton (RTA) are computational model... [more] | MSS2020-41 SS2020-26 pp.72-77 |
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