An Embedding-based approach for Motor Imagery classification by Quentin Langlois, Phd at UCLouvain/ICTEAM/INGI Electroencephalography (EEG) is an electrical signal captured by electrodes, representing the brain activity of a subject. In recent years, Deep Learning techniques have been extensively studied for analyzing such signals in various applications, including Motor Imagery (MI) d

The ability to scale up is an essential ability for large computer systems that handle numerous simultaneous tasks reliably. It follows that these systems must support parallelism for performance and redundancy for fault tolerance.

Machine learning, specifically neural networks, have seen significant advancements in classification tasks across various fields.

By Pr. Antonio Paolillo from VUB Antonio's talk will outline his journey in real-time and low-level software systems research, beginning with his Ph.D. at ULB.

By Vianney Coppé and Xavier Gillard, UCLouvain Are you looking to enhance your skills in tackling complex problems for the advent of code? Then this seminar is for you!

An important problem in network science is finding an optimal placement of sensors in nodes in order to uniquely detect failures in the network. This problem can be modelled as an identifying code set (ICS) problem, introduced by Karpovsky et al. in 1998.

By Thomas Alfroy, Thomas Holterbach (University of Strasbourg); Thomas Krenc, kc Claffy (UC San Diego / CAIDA); Cristel Pelsser (UCLouvain) Dramatic growth in Internet connectivity poses a challenge for the resource-constrained data collection efforts that support scientific and operational analysis of interdomain routing.

By Pr. Eric Piette, UCLouvain/ICTEAM/INGI This talk will introduce Ludii (https://ludii.games), a General Game System developed under the Digital Ludeme Project funded by the European Research Council (ERC). Ludii provides a unified framework capable of modelling a diverse array of "strategy" board games, encompassing over 1,300 games.

Guillaume Rosinosky is a post-doc researcher who will present StreamBed: capacity planning for stream processing Distributed Stream Processing (DSP) supports long-lived processing jobs over continuous data. DSP engines scale out to support parallel processing of incoming data, until they reach the necessary volume of resources.

By Prof. Keiichi Yasumoto &  Prof. Yugo Nakamura 10:30-11:30 Prof. Keiichi Yasumoto will present the activites of his research group. Prof. Yasumoto is the head of the research group for "Ubiquitous Computing Systems" at the Nara Institute of Science and Technology in Japan. The laboratory aims to make our society smarter through cyber physical system technologies.