Toshiyuki Amagasa

most citedRecommending Extract Method Refactoring Based on Confidence of Predicted Method Name

5 citations · 9 across the 2 of their papers we have counts for

collaborators

3 papers

cs.SE2021★ 5 cited

Recommending Extract Method Refactoring Based on Confidence of Predicted Method Name

Jinto Yamanaka, Yasuhiro Hayase, Toshiyuki Amagasa

Refactoring is an important activity that is frequently performed in software development, and among them, Extract Method is known to be one of the most frequently performed refact…

astro-ph.GA2021

SILVERRUSH X: Machine Learning-Aided Selection of $9318$ LAEs at $z = 2.2$ , $3.3$ , $4.9$ , $5.7$ , $6.6$ , and $7.0$ from the HSC SSP and CHORUS Survey Data

Yoshiaki Ono, Ryohei Itoh, Takatoshi Shibuya +25

We present a new catalog of $9318$ Ly $α$ emitter (LAE) candidates at $z = 2.2$ , $3.3$ , $4.9$ , $5.7$ , $6.6$ , and $7.0$ that are photometrically selected by the SILVERRUSH program wi…

cs.SI2019★ 4 cited

Scaling Fine-grained Modularity Clustering for Massive Graphs

Hiroaki Shiokawa, Toshiyuki Amagasa, Hiroyuki Kitagawa

Modularity clustering is an essential tool to understand complicated graphs. However, existing methods are not applicable to massive graphs due to two serious weaknesses. (1) It is…

Recommending Extract Method Refactoring Based on Confidence of Predicted Method Name

SILVERRUSH X: Machine Learning-Aided Selection of 9318 LAEs at z=2.2, 3.3, 4.9, 5.7, 6.6, and 7.0 from the HSC SSP and CHORUS Survey Data

Scaling Fine-grained Modularity Clustering for Massive Graphs

SILVERRUSH X: Machine Learning-Aided Selection of $9318$ LAEs at $z = 2.2$ , $3.3$ , $4.9$ , $5.7$ , $6.6$ , and $7.0$ from the HSC SSP and CHORUS Survey Data