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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| PN | 2022-03-02 11:00 |
Online | Online | Expected Effective Rate Guaranteed Distributed Processing Allocation Method Based on Resource Availability for Real-Time Video Processing Koki Muramatsu, Masaki Murakami, Yoshihiko Uematsu, Satoru Okamoto, Naoaki Yamanaka (Keio Univ.)  PN2021-73 |
Our laboratory is working on the implementation of AMec (Access Metro Edge Computing), an edge computing technology that... [more] | PN2021-73 pp.110-116 |
| RCS, NS (Joint) |
2021-12-17 11:25 |
Nara | Nara-ken Bunka Kaikan and Online (Primary: On-site, Secondary: Online) |
A Study on User Scheduling for Overloaded Environment in Distributed Antenna Network Using Quantum Computing Keishi Hanakago, Ryo Takahashi, Takahiro Ohyama (PSNRD), Fumiyuki Adachi (Tohoku Univ.) RCS2021-189 |
In a distributed antenna network, a large number of antennas are distributed over a base station coverage area (cell). H... [more] | RCS2021-189 pp.75-80 |
| COMP | 2021-12-03 14:50 |
Ishikawa | Kanazawa Chamber of Commerce and Industry (Primary: On-site, Secondary: Online) |
On a Self-Stabilizing Algorithm for k-Flag Problem with Crash Failures Yuta Yokoyama, Yonghwan Kim, Yoshiaki Katayama (Nitech) COMP2021-27 |
In developmental biology, the French Flag model is used as a cell positioning model for regeneration of defective cells.... [more] | COMP2021-27 pp.30-37 |
| NS | 2021-10-08 09:15 |
Online | Online | A study of privacy-preserving distributed machine learning using Rich Clients Saki Takano (Ochanomizu Univ.), Akihiro Nakao (The Univ. of Tokyo), Saneyasu Yamaguchi (Kogakuin Univ.), Masato Oguchi (Ochanomizu Univ.) NS2021-76 |
In recent years, edge computing has attracted much attention because of its advantages such as low latency and the abili... [more] | NS2021-76 pp.45-50 |
| SWIM, SC | 2021-08-27 15:05 |
Online | Online | Design and implementation of a programming model for distributed sharing function as a service Takao Nakaguchi (KCGI) SWIM2021-23 SC2021-21 |
To develop real-time collaboration tools, the drawing canvas, 3D objects or other elements on the screen must be designe... [more] | SWIM2021-23 SC2021-21 pp.64-68 |
| COMP, IPSJ-AL | 2021-08-25 11:00 |
Online | Online | Lower Bounds for Induced Cycle Detection in Distributed Computing Francois Le Gall, Masayuki Miyamoto (Nagoya Univ.) COMP2021-9 |
The distributed subgraph detection asks, for a fixed graph $H$, whether the $n$-node input graph contains $H$ as a subgr... [more] | COMP2021-9 pp.1-8 |
| RCS, SR, NS, SeMI, RCC (Joint) |
2021-07-14 09:00 |
Online | Online | A Study on Inter-Cluster Interference Coordination for Distributed Antenna Network Using Quantum Computing Keishi Hanakago, Ryo Takahashi, Takahiro Ohyama (PSNRD), Fumiyuki Adachi (Tohoku Univ.) RCS2021-77 |
In a large-scale distributed antenna network, where a large number of antennas are deployed over a base station coverage... [more] | RCS2021-77 pp.1-6 |
| NLP, MSS (Joint) |
2021-03-15 13:00 |
Online | Online | Proposal of Novel Distributed Learning Algorithms for Multi-Neural Networks Kazuaki Harada, Tsuyoshi Migita, Norikazu Takahashi (Okayama Univ.) NLP2020-58 |
A method for multiple neural networks (NNs) with the same structure to learn multiple sets of training data collected at... [more] | NLP2020-58 pp.17-22 |
| COMP | 2021-03-08 10:45 |
Online | Online | Team Assembling Problem by Kilobots Tang Run, Yamauchi Yukiko (Kyushu Univ.), Sebastien Tixeuil (Sorbonne Univ.) COMP2020-30 |
We consider the team assembling problem for a swarm of simple robots called Kilobots. The team assembling problem requir... [more] | COMP2020-30 pp.17-23 |
| COMP | 2021-03-08 11:30 |
Online | Online | [Invited Talk]
Tight Distributed Listing of Cliques Keren Censor-Hillel (Technion), Yi-Jun Chang (ETH), François Le Gall (Nagoya Univ.), Dean Leitersdorf (Technion) COMP2020-31 |
Much progress has recently been made in understanding the complexity landscape of subgraph finding problems in the CONGE... [more] | COMP2020-31 p.24 |
| IN, NS (Joint) |
2021-03-04 11:00 |
Online | Online | Application Offloading Mechanism based on Distributed Reinforcement Learning in MEC Environment Soh Takamura, Takao Kondo, Fumio Teraoka (Keio Univ.) IN2020-67 |
This paper proposes a mechanism for determining the offloading strategy of an application running on a User Equipment (U... [more] | IN2020-67 pp.79-84 |
| SIP, IT, RCS | 2021-01-21 13:10 |
Online | Online | A Study on the Joint Source-Channel Coding for Computing Functions
-- An Approach from a Dichotomy of Functions -- Naruki Joki, Shigeaki Kuzuoka (Wakayama Univ.) IT2020-82 SIP2020-60 RCS2020-173 |
In this paper，a problem of joint source-channel coding for computing functions of outputs from correlated sources is stu... [more] | IT2020-82 SIP2020-60 RCS2020-173 pp.101-106 |
| NS, RCS (Joint) |
2020-12-17 12:15 |
Online | Online | Processing allocation algorithm for distributed deep learning in wireless sensor networks Karin Umeda, Takashi Nishitsuji, Takuya Asaka (Tokyo Metropolitan Univ.), Takumi Miyoshi (Shibaura Inst. of Tech.) NS2020-90 |
In wireless sensor networks, distributed processing technology for deep learning that utilizes edge computing and mobile... [more] | NS2020-90 pp.17-22 |
| KBSE, SC | 2020-11-14 11:40 |
Online | Online + Kikai-Shinko-Kaikan Bldg. (Primary: Online, Secondary: On-site) |
An Object Sharing Service for Real-time Collaboration Tools Takao Nakaguchi (KCGI) KBSE2020-28 SC2020-32 |
With the spread of remote work and online lectures, the demands for a real-time online system to perform collaborative w... [more] | KBSE2020-28 SC2020-32 pp.70-73 |
| RECONF | 2020-09-10 13:30 |
Online | Online | With GPU-FPGA Heterogeneous computing, Highly Effective Communication for Distributed Deep Learning Kenji Tanaka, Yuki Arikawa, Tsuyoshi Ito, Kazutaka Morita, Naru Nemoto, Fumiaki Miura, Kazuhiko Terada, Junji Teramoto, Takashi Sakamoto (NTT) RECONF2020-19 |
In distributed deep learning (DL), collective communication (Allreduce) used to share training results between GPUs is a... [more] | RECONF2020-19 pp.1-6 |
| CPSY, DC, IPSJ-ARC [detail] | 2020-07-30 14:30 |
Online | Online | Distributed Runtime Environment with Julia Language Hidemoto Nakada (AIST) CPSY2020-2 DC2020-2 |
Julia-lang is a relatively new scripting language aiming at high-performance computing powered by powerful LLVM JIT comp... [more] | CPSY2020-2 DC2020-2 pp.9-14 |
| IT, EMM | 2020-05-29 09:30 |
Online | Online | A Study on the Achievable Region of Distributed Coding for Computing Three-Input Functions Naruki Joki, Shigeaki Kuzuoka (Wakayama Univ) IT2020-6 EMM2020-6 |
In this paper，a problem of distributed data compression for computing functions of outputs from correlated sources is st... [more] | IT2020-6 EMM2020-6 pp.31-36 |
| LOIS | 2020-03-12 11:05 |
Okinawa | Nobumoto Ohama Memorial Hall (Cancelled but technical report was issued) |
Distributed active learning achieving both of monitoring and efficient time-series data sampling for edge computing Osamu Saisho, Keiichiro Kashiwagi, Yui Saito, Tomoyuki Fujino (NTT) LOIS2019-73 |
For edge computing, there is still a great demand to upload only meaningful data to cloud,. However there is no practica... [more] | LOIS2019-73 pp.97-101 |
| MSS, NLP (Joint) |
2020-03-10 09:25 |
Aichi | (Cancelled but technical report was issued) |
Optimal Resource Allocation for Distributed Computing Environments based on Integer Programming Generation Shoya Kyan, Tomoya Uchihara, Morikazu Nakamura (Univ. of the Ryukyus) MSS2019-64 |
This paper considers resource assignment problems in distributed computing environments based on the automatic generatio... [more] | MSS2019-64 pp.37-42 |
| RCS, SR, SRW (Joint) |
2020-03-05 15:30 |
Tokyo | Tokyo Institute of Technology (Cancelled but technical report was issued) |
[Invited Lecture]
Wi-SUN FAN Technology Enabling Smart Utility and Smart Cities to Grow Takeo Osawa (Itron) SRW2019-75 |
Title: Wi-SUN FAN Technology Enabling Smart Utility and Smart Cities to Grow Abstract: There has been a significant p... [more] |
SRW2019-75 pp.81-86 |
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