| 25-06-2024 |
Modelamiento de las vocalizaciones de preescolares entre 36 y 60 meses
Los humanos vocalizan desde el nacimiento. Llantos, gorjeos, balbuceo y otras vocalizaciones que preceden a la llamada explosión del habla observada entre los 18 y los 24 meses en niñas y niños con desarrollo típico. La explosión del habla se caracteriza por un aumento súbito del número de palabras que los preescolares dicen. Actualmente poco se sabe del curso del desarrollo de las vocalizaciones durante los primeros años de vida y cómo este desarrollo puede darnos señales de logros y dificultades en la adquisición del lenguaje. Esta investigación se propone generar curvas de desarrollo típico y atípico del desarrollo de las vocalizaciones, comenzando por caracterizar las vocalizaciones de los preescolares. En particular, proponemos modelar las vocalizaciones como grafos y extraer mediante herramientas computacionales información de estos que nos permitan caracterizar las vocalizaciones estudiadas.
Keywords:
modelamiento
grafos
Prerequisitos:
IIC1103
Tiene un método de evaluación Nota 1-7, con 10 créditos y tiene 2/2 vacantes disponibles |
Mentor(es): Ver en la plataforma |
| 25-06-2024 |
Modeling Vocalizations of Preschoolers Between 36 and 60 Months
Humans vocalize from birth. Cries, coos, babbling, and other vocalizations precede the so-called explosion of speech observed between 18 and 24 months in typically developing girls and boys. The speech explosion is characterized by a sudden increase in the number of words that preschoolers say. Currently, little is known about the course of vocalization development during the first years of life and how this development can give us clues about achievements and difficulties in language acquisition. This research aims to generate developmental curves for typical and atypical vocalization development, beginning with the characterization of preschoolers' vocalizations. Specifically, we propose to model the vocalizations as graphs and use computational tools to extract information from these graphs that will allow us to characterize the studied vocalizations.
Keywords:
modelamiento
grafos
Prerequisites:
IIC1103
Evaluation method: Nota 1-7, with 2/2 available vacants |
Mentor(s): Open in the plataform |
| 08-01-2024 |
Keywords:
Prerequisites:
None.
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 06-01-2022 |
Liquid Democracy: Modelling, Analysis, and Computational Implementation
Liquid democracy is a new paradigm of democratic decision-making, based on the idea that voters can delegate their votes transitively to people they consider most qualified for the particular election that is being held. The study of liquid democracy from an algorithmic and computational point of view, which is a branch of the area of "computational social choice", has recently started through a couple of very influential articles; for example, https://ojs.aaai.org/index.php/AAAI/article/view/11468. Despite how interesting these works are, we believe that they have not yet managed to capture all the complexities that these types of dynamics have in reality. The idea is to develop extensions of this model that better fit the requirements of current political science, and from that to analyze its mathematical and computational properties. The work will be interdisciplinary with components of mathematics, computing, and political science.
Prerequisites:
IIC1253
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |
| 21-01-2021 |
Reasoning over knowledge graphs using vector embeddings
Knowledge graphs are one of the hottest topics in artificial intelligence today as they allow increasing the reasoning capabilities of neural networks. Extracting information from these graphs is complex, but this year new techniques have been presented to do it efficiently through machine learning techniques using vector embeddings. We want to study algorithmically robust and optimal ways to carry out this process based on the state of the art of data structures and algorithms.
Prerequisites:
IIC2133
Evaluation method: Nota 1-7, with 0/1 available vacants |
Mentor(s): Open in the plataform |