“Signal Processing in the AI era” was the tagline of this year’s IEEE International Conference on Acoustics, Speech and Signal Processing, taking place in Rhodes, Greece.
In this context, Brent de Weerdt, Xiangyu Yang, Boris Joukovsky, Alex Stergiou and Nikos Deligiannis presented ETRO’s research during poster sessions and oral presentations, with novel ways to process and understand graph, video, and audio data. Nikos Deligiannis chaired a session on Graph Deep Learning, attended the IEEE T-IP Editorial Board Meeting, and had the opportunity to meet with collaborators from the VUB-Duke-Ugent-UCL joint lab.
Featured articles:
- De Weerdt, B. et al., “Designing Transformer Networks for Sparse Recovery of Sequential Data Using Deep Unfolding” (https://ieeexplore.ieee.org/abstract/document/10094712)
- Yang X. et al., “Relevance Propagation through Deep Conditional Random Fields” (https://ieeexplore.ieee.org/abstract/document/10095075)
- Stergiou A. et al., “Play it back: Iterative attention for audio recognition” (https://ieeexplore.ieee.org/abstract/document/10096532, developed at the University of Bristol)
Lucas Moura Santana won the 2022-2023 IEEE SSCS Predoctoral Achievement Award
The Charcot Fund Jury met on December 9, 2022.
The project “Disentangling cognitive functioning and visual scanning deficits in cognitive test scores” (Prom: Prof J. Van Schependom), has been selected by the Jury for the Charcot Fund 2023.
The Charcot Fund Ceremony will take place on 31 January 2023 at the University Foundation.
First visit to NPU- Xi’an China
Creation of research groups LAMI (Micro-and Photon-Electronics)and IRIS (Image and video processing).
On February 1 2021 at 16.00 Tien Do Huu will defend his PhD entitled “A generic source coding. Methodology and architecture of dynamic holograms”.
Everybody is invited to attend the presentation online via https://teams.microsoft.com/l/meetup-join/19%3ameeting_N2EwMTQ2ZmMtMjA2Ny00MGQwLTk2ZDctODNmMzE3NGEyNDMz%40thread.v2/0?context=%7b%22Tid%22%3a%22695b7ca8-2da8-4545-a2da-42d03784e585%22%2c%22Oid%22%3a%2237757df7-4050-47fe-8384-aded7e9e79c3%22%7d
We address the two challenges of big heterogeneous data originated from social media and smart cities, namely data quality enhancement and data exploitation. In the first challenge, we consider several subproblems commonly found in social media data and smart city data, including user location prediction, traffic data denoising, and hyperlocal air quality prediction. For the second challenge, we aim to gain insights from data, namely we focus on detecting fake news using social media data. As there exist correlations across datapoints in social media and smart city data, we propose exploiting these correlations using graph-based deep learning techniques to address the aforementioned challenges.
Our contributions are associated with the concerned subproblems. In user location prediction, we propose a novel deep multiview model combining multiple aspects of social media data. One of the inputs of the multiview model is node representation, which is learned using a graph-deep-learning-based technique. In order to denoise traffic data, we design a special graph auto-encoder with a Kron-reduction-based pooling scheme. We devise a graph variational auto-encoder in dealing with the air quality prediction problem. For fake news detection, we propose using a graph convolutional neural network, which captures the relation between articles shared by suspicious publishers. Having experienced graph neural networks on different applications, our last contribution focuses on a more fundamental problem of regularizing graph neural networks by introducing a novel regularization technique based on dropping nodes. We conduct comprehensive experiments on benchmark datasets to verify the effectiveness of the proposed methods. Finally yet importantly, although some of our methods are designed for specific problems (e.g., air pollution prediction), our formulations are general, leading to the possibility of using these methods for different applications (e.g., recommender systems) in related domains.
Pengpeng Hu has successfully defended his PhD in public on 22/09. He has obtained the “Grootste Onderscheiding”. In addition, the jury has unanimously decided to felicitate Pengpeng for his achievements in terms of scientific publications and industrial valorisation.
With this, he continues the tradition in his team of his promotor Adrian Munteanu and colleague Nikolaos Delligiannis, whom both also recieved this exceptional result for their respective PhD theses.