no code implementations • 10 Jan 2018 • Mohammadreza Esfandiari, Senjuti Basu Roy, Sihem Amer-Yahia
In this work, we believe that asking workers to indicate their preferences explicitly improve their experience in task completion and hence, the quality of their contributions.
no code implementations • 10 Sep 2019 • Jose F. Rodrigues-Jr, Gabriel Spadon, Bruno Brandoli, Sihem Amer-Yahia
Our results demonstrate significant improvements in automated medical prognosis, as measured with Recall@k. We summarize our experience as a set of relevant insights for the design of Deep Learning architectures.
no code implementations • 31 Aug 2020 • Bruno Brandoli, Gabriel Spadon, Travis Esau, Patrick Hennessy, Andre C. P. L. Carvalho, Jose F. Rodrigues-Jr, Sihem Amer-Yahia
Pesticide application has been heavily used in the cultivation of major crops, contributing to the increase of crop production over the past decades.
no code implementations • 9 Apr 2021 • Sihem Amer-Yahia, Georgia Koutrika, Frederic Bastian, Theofilos Belmpas, Martin Braschler, Ursin Brunner, Diego Calvanese, Maximilian Fabricius, Orest Gkini, Catherine Kosten, Davide Lanti, Antonis Litke, Hendrik Lücke-Tieke, Francesco Alessandro Massucci, Tarcisio Mendes de Farias, Alessandro Mosca, Francesco Multari, Nikolaos Papadakis, Dimitris Papadopoulos, Yogendra Patil, Aurélien Personnaz, Guillem Rull, Ana Sima, Ellery Smith, Dimitrios Skoutas, Srividya Subramanian, Guohui Xiao, Kurt Stockinger
We demonstrate that our system is uniquely accessible to a wide range of users from larger scientific communities to the public.
no code implementations • 27 May 2022 • Brit Youngmann, Sihem Amer-Yahia, Aurélien Personnaz
We examine the applicability of Exploratory Data Analysis (EDA) to data summarization and formalize Eda4Sum, the problem of guided exploration of data summaries that seeks to sequentially produce connected summaries with the goal of maximizing their cumulative utility.
1 code implementation • 6 Jun 2022 • Dujian Ding, Sihem Amer-Yahia, Laks VS Lakshmanan
Alternatively, under the Core Set Closure assumption, we develop two algorithms: CSC that efficiently returns high quality answers with high probability and minimal oracle usage, and CSE, which extends it to more general settings.
1 code implementation • 20 Mar 2024 • Yun Wang, Chrysanthi Kosyfaki, Sihem Amer-Yahia, Reynold Cheng
Experiments on real datasets demonstrate the ability of our framework to leverage common graph sampling methods for hypothesis testing, and the superiority of hypothesis-aware sampling in terms of accuracy and time efficiency.