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Author |
Patricia Súarez, Henry Velesaca, Dario Carpio & Angel Sappa |
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Title |
Corn Kernel Classification From Few Training Samples |
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Journal Article |
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Year |
2023 |
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In journal Artificial Intelligence in Agriculture |
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Vol. 9 |
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pp. 89-99 |
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25897217 |
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cidis @ cidis @ |
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223 |
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Author |
Rafael E. Rivadeneira, Henry O. Velesaca, Angel D. Sappa |
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Title |
Object Detection in Very Low-Resolution Thermal Images through a Guided-Based Super-Resolution Approach |
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Conference Article |
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Year |
2023 |
Publication |
17th International Conference On Signal Image Technology & Internet Based System |
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no |
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cidis @ cidis @ |
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224 |
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Author |
Patricia L. Suarez, Dario Carpio, Angel Sappa |
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Title |
Boosting Guided Super-Resolution Performance with Synthesized Images |
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Conference Article |
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Year |
2023 |
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17th International Conference On Signal Image Technology & Internet Based Systems |
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no |
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cidis @ cidis @ |
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225 |
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Author |
Patricia L. Suarez, Dario Carpio, Angel Sappa |
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Title |
Depth Map Estimation from a Single 2D Image |
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Conference Article |
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Year |
2023 |
Publication |
17th International Conference On Signal Image Technology & Internet Based Systems |
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no |
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cidis @ cidis @ |
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226 |
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Author |
Armin Mehri, Parichehr Behjati, Dario Carpio, and Angel D. Sappa |
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Title |
SRFormer: Efficient Yet Powerful Transformer Network For Single Image Super Resolution |
Type |
Journal Article |
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Year |
2023 |
Publication |
IEEE access |
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Volume |
Vol. 11 |
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Pages |
121457 - 121469 |
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21693536 |
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no |
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cidis @ cidis @ |
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227 |
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Sara Nieto, Evelyn Mejia, Ricardo Villacis, Fernanda Calderon, Hector Villegas, Jonathan Paillacho and Miguel Realpe |
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Title |
A Practical Study on Banana (Musa spp.) Plant Counting and Coverage Percentage Using Remote Sensing and Deep Learning |
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Conference Article |
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Year |
2023 |
Publication |
International Conference on Geospatial Information Sciences, iGISc 2023 |
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no |
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Call Number |
cidis @ cidis @ |
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228 |
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Author |
Xavier Soria, Yachuan Li, Mohammad Rouhani & Angel D. Sappa |
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Title |
Tiny and Efficient Model for the Edge Detection Generalization |
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Conference Article |
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Year |
2023 |
Publication |
Proceedings – 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023 |
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1356 - 1365 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
229 |
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Author |
Rubio Abel; Agila Wilton; González Leandro; Aviles Jonathan |
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Title |
A Numerical Model for the Transport of Reactants in Proton Exchange Fuel Cells |
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Conference Article |
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Year |
2023 |
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12th IEEE International Conference on Renewable Energy Research and Applications, ICRERA 2023 Oshawa 29 August – 1 September 2023 |
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273 - 278 |
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979-835033793-8 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
230 |
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Author |
Suarez Patricia; Carpio Dario; Sappa Angel D. |
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Title |
A Deep Learning Based Approach for Synthesizing Realistic Depth Maps |
Type |
Conference Article |
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Year |
2023 |
Publication |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics 22nd International Conference on Image Analysis and Processing, ICIAP 2023 Udine 11 – 15 September 2023 |
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14234 LNCS |
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369 - 380 |
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03029743 |
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978-303143152-4 |
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no |
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Call Number |
cidis @ cidis @ |
Serial |
231 |
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Author |
Cristina L. Abad; Yi Lu; Roy H. Campbell |
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Title |
DARE: Adaptive Data Replication for Efficient Cluster Scheduling |
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Conference Article |
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Year |
2011 |
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IEEE International Conference on Cluster Computing, 2011 |
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159 - 168 |
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MapReduce, replication, scheduling, locality |
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Abstract |
Placing data as close as possible to computation is a common practice of data intensive systems, commonly referred to as the data locality problem. By analyzing existing production systems, we confirm the benefit of data locality and find that data have different popularity and varying correlation of accesses. We propose DARE, a distributed adaptive data replication algorithm that aids the scheduler to achieve better data locality. DARE solves two problems, how many replicas to allocate for each file and where to place them, using probabilistic sampling and a competitive aging algorithm independently at each node. It takes advantage of existing remote data accesses in the system and incurs no extra network usage. Using two mixed workload traces from Facebook, we show that DARE improves data locality by more than 7 times with the FIFO scheduler in Hadoop and achieves more than 85% data locality for the FAIR scheduler with delay scheduling. Turnaround time and job slowdown are reduced by 19% and 25%, respectively. |
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English |
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English |
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yes |
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Call Number |
cidis @ cidis @ |
Serial |
21 |
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