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 Conference Papers (Available on Advance Programs)  (Sort by: Date Descending)
 Results 1 - 20 of 37  /  [Next]  
Committee Date Time Place Paper Title / Authors Abstract Paper #
NS, IN
(Joint)
2024-02-29
10:10
Okinawa Okinawa Convention Center Analysis of Wireless Link for Mobile Communication Devices Using a Machine Learning Model
Yuki Kanto, Kohei Watabe (Nagaoka Univ. of Tech.) IN2023-67
In recent years, mobile communication devices have been providing various services via high-speed and high-capacity wire... [more] IN2023-67
pp.13-18
MIKA
(3rd)
2023-10-10
15:35
Okinawa Okinawa Jichikaikan
(Primary: On-site, Secondary: Online)
[Poster Presentation] A Study on the Information Diffusion via Reposts without Explicit Diffusion Paths on Social Media
Yuto Tamura, Sho Tsugawa (Tsukuba Univ.), Kohei Watabe (NUT)
The spread of information on social media can be understood as a process where one receives information from those they ... [more]
CQ, MIKA
(Joint)
(2nd)
2023-08-30
16:20
Fukushima Tenjin-Misaki Sports Park [Poster Presentation] Analysis of occurrence patterns in retweets without explicit diffusion paths focusing on the distance between users
Yuto Tamura, Sho Tsugawa (Tsukuba Univ.), Kohei Watabe (NUT)
On social media, it is conceivable that information diffusion is facilitated by the recurrent actions of users who engag... [more]
IMQ, IE, MVE, CQ
(Joint) [detail]
2023-03-15
10:35
Okinawa Okinawaken Seinenkaikan (Naha-shi)
(Primary: On-site, Secondary: Online)
Evaluation of the effectiveness of link deletion based on diffusion prediction for suppressing information diffusion on social media
Shiori Furukawa, Sho Tsugawa (Tsukuba Univ.), Kohei Watabe (Nagaoka Univ. of Tech) CQ2022-82
 [more] CQ2022-82
pp.13-18
IN 2023-01-19
10:00
Aichi Aichi Industry & Labor Center
(Primary: On-site, Secondary: Online)
A Learning-Based Accurate Graph Reconstruction Model with Deep Learning
Takahiro Yokoyama, Yoshiki Sato (Nagaoka Univ. of Tech.), Sho Tsugawa (Univ. Tsukuba), Kohei Watabe (Nagaoka Univ. of Tech.) IN2022-52
In recent years, there has been an increasing interest in developing models for learning and generating graphs using dee... [more] IN2022-52
pp.1-6
IN 2023-01-19
10:25
Aichi Aichi Industry & Labor Center
(Primary: On-site, Secondary: Online)
Flow Classification Using Flow Collections and Deep Learning
Loc Gia Nguyen, Kohei Watabe (Nagaoka Univ. of Tech.) IN2022-53
This study aims to explore the possibility of using collections of flows to improve classification accuracy of network f... [more] IN2022-53
pp.7-12
IN, CCS
(Joint)
2022-08-05
09:40
Hokkaido Hokkaido University(Centennial Hall)
(Primary: On-site, Secondary: Online)
Machine Learning-Based Network Traffic Prediction with Tunable Parameters
Kaito Kuriyama, Kohei Watabe (Nagaoka Univ. of Tech.) IN2022-20
Network evaluation has become increasingly important in recent years.
Network evaluation requires large amounts of traf... [more]
IN2022-20
pp.27-32
SAT, SANE
(Joint)
2022-02-25
09:30
Online Online Bandwidth and Power Allocation for a Multi-beam Satellite Communication System in the Japanese Islands
Shun Okuhama (ONCT), Kohei Watabe (NUT), Katsuya Nakahira (ONCT) SAT2021-62
Multi-beam systems have become the mainstream for satellite communications because they can make effective use of freque... [more] SAT2021-62
pp.57-61
CQ, CBE
(Joint)
2022-01-28
14:40
Ishikawa Kanazawa(Ishikawa Pref.)
(Primary: On-site, Secondary: Online)
A Study on Finding Intermediate Spreaders in Social Networks
Sho Tsugawa (Univ. of Tsukuba), Kohei Watabe (Nagaoka Univ. of Technology) CQ2021-94
Identifying influencers in a given social network has been an important research problem. In the literature, several me... [more] CQ2021-94
pp.100-105
IN, NS
(Joint)
2021-03-05
14:20
Online Online A study of a tunable generative model for graph data using machine learning
Shohei Nakazawa, Yoshiki Sato, Kenji Nakagawa (Nagaoka Univ. of Tech.), Sho Tsugawa (Tsukuba Univ.), Kohei Watabe (Nagaoka Univ. of Tech.) NS2020-159
In recent years, applications and simulations using graphs are becoming more important. The graph has various features, ... [more] NS2020-159
pp.214-219
CQ, CBE
(Joint)
2021-01-20
16:05
Online Online [Invited Talk] A machine learning approach to data generation in networks
Kohei Watabe (Nagaoka Univ. of Tech.) CQ2020-71
When we evaluate communication networks and protocols/applications running on them, it is important to demonstrate their... [more] CQ2020-71
p.57
NS, RCS
(Joint)
2020-12-18
15:50
Online Online Improvement of Fairness and Throughput in Multi-Hop Wireless Ad Hoc Networks
Truong Van Nhat Minh, Kenji Nakagawa, Kohei Watabe (Nagaoka Univ. of Tech) NS2020-106
 [more] NS2020-106
pp.103-107
IN, IA
(Joint)
2019-12-20
10:45
Hiroshima Satellite Campus Hiroshima Packet loss rate estimation and path selection method by minimizing L1 norm in multicast communication
Taiji Kichikawa, Kenji Nakagawa, Kohei Watabe (Nagaoka University of Univ.) IN2019-54
 [more] IN2019-54
pp.51-55
IN, IA
(Joint)
2019-12-20
11:10
Hiroshima Satellite Campus Hiroshima Available bandwidth estimation method using MACD index
Ryota Saito, Kenji Nakagawa, Kohei Watabe (Nagaoka Univ. of Tech) IN2019-55
In recent years, services that consume bandwidth on the Internet have become popular. In order to provide these services... [more] IN2019-55
pp.57-61
MIKA
(2nd)
2019-10-04
10:15
Hokkaido Hokkaido Univ. [Poster Presentation] A study of similar network generative model using machine learning
Shohei Nakazawa, Kohei Watabe, Kenji Nakagawa (Nagaoka Univ. of Tech.)
A real topology data are required when we simulate assuming an environment close to a real situation. The real data of t... [more]
CQ 2019-07-18
13:00
Niigata Niigata Univ. [Poster Presentation] A Study on Pseudo Traffic Generation Using Machine Learning
Tetsuya Yamagiwa, Kohei Watabe, Kenji Nakagawa (Nagaoka Univ. of Tech.) CQ2019-38
When we constructing a network, it is important to use a traffic generator that generates realistic traffic and perform load... [more] CQ2019-38
pp.27-29
IN, NS
(Joint)
2019-03-05
13:50
Okinawa Okinawa Convention Center On Accurate Packet Loss Estimation Method for Networks without Traffic Models
Masahiro Terauchi, Kohei Watabe, Kenji Nakagawa (Nagaoka Univ. Tech.) IN2018-131
It is important to accurately model network traffic when we evaluate Quality of Service~(QoS) of networks through simula... [more] IN2018-131
pp.283-288
IN, NS
(Joint)
2019-03-05
14:10
Okinawa Okinawa Convention Center Estimation of packet drop rate by parallel path active measurement
Norinosuke Murai, Kohei Watabe, Kenji Nakagawa (Nagaoka Univ. of Technology) IN2018-132
In this paper, we propose a method to estimate packet drop rate by performing end-to-end multi-path active measurement o... [more] IN2018-132
pp.289-294
NS, IN
(Joint)
2018-03-02
10:00
Miyazaki Phoenix Seagaia Resort Estimation of Failure Locations by Path Integration/partition in Network Failure
Yohsuke Tsutsumi, Kohei Watabe, Kenji Nakagawa (Nagaoka Univ. of Technology) NS2017-198
 [more] NS2017-198
pp.175-180
CQ
(2nd)
2018-01-20
12:50
Tokyo Nishiwaseda Campus, Waseda Univ. [Poster Presentation] A Study of a Packet Loss Estimation Using Importance Sampling in Networks with Unknown Traffic
Masahiro Terauchi, Kohei Watabe, Kenji Nakagawa (Nagaoka Univ. of Technology)
Techniques for measuring the packet loss probability with high accuracy are important to ensure Service Level Agreements... [more]
 Results 1 - 20 of 37  /  [Next]  
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