Search Results for author: Daniel Aloise

Found 9 papers, 2 papers with code

Language Models for Novelty Detection in System Call Traces

no code implementations5 Sep 2023 Quentin Fournier, Daniel Aloise, Leandro R. Costa

Due to the complexity of modern computer systems, novel and unexpected behaviors frequently occur.

Language Modelling Novelty Detection

Soft Attention: Does it Actually Help to Learn Social Interactions in Pedestrian Trajectory Prediction?

no code implementations16 Jun 2021 Laurent Boucaud, Daniel Aloise, Nicolas Saunier

In this paper, we focus on the deep-learning models with a soft-attention mechanism for social interaction modeling and study whether they use social information at prediction time.

Pedestrian Trajectory Prediction Trajectory Prediction

Exploring dual information in distance metric learning for clustering

no code implementations26 May 2021 Rodrigo Randel, Daniel Aloise, Alain Hertz

To address these issues, we propose to exploit the dual information associated with the pairwise constraints of the semi-supervised clustering problem.

Clustering Metric Learning

On Improving Deep Learning Trace Analysis with System Call Arguments

no code implementations11 Mar 2021 Quentin Fournier, Daniel Aloise, Seyed Vahid Azhari, François Tetreault

Such tasks may be used to detect anomalies, pre-train neural networks to improve their performance, and extract a contextual representation of the events.

Language Modelling

Automatic Cause Detection of Performance Problems in Web Applications

no code implementations8 Mar 2021 Quentin Fournier, Naser Ezzati-Jivan, Daniel Aloise, Michel R. Dagenais

In this paper, we propose a method of extracting the internal behavior of web requests as well as introduce a pipeline that detects performance issues in web requests and provides insights into their root causes.

Clustering

Empirical comparison between autoencoders and traditional dimensionality reduction methods

no code implementations8 Mar 2021 Quentin Fournier, Daniel Aloise

The four different dimensionality reduction techniques were separately employed on each dataset to project data into a low-dimensional space.

Dimensionality Reduction

The Covering-Assignment Problem for Swarm-powered Ad-hoc Clouds: A Distributed 3D Mapping Use-case

1 code implementation21 Apr 2020 Leandro R. Costa, Daniel Aloise, Luca G. Gianoli, Andrea Lodi

Besides automating field operations, a drone swarm can serve as an ad-hoc cloud infrastructure built on top of computing and storage resources available across the swarm members and other connected elements.

3D Reconstruction

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