Event Category: In-house Presentation

Investigating Online Attacks on Mobile Network Forecasters under Temporal Constraints

Machine Learning approaches for traffic forecasting are increasingly being explored to improve resource allocation in mobile networks. However, these models...

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Navigating Taxes in Spain: A Survival Guide for International Researchers

Welcome to sunny Spain! Now that you’ve discovered flamenco, tapas, paella, and late-night dinners, it’s time to meet another essential...

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Breaking the Flat Horizon: Unlocking the Sixth Sense of Wireless Networks

For centuries, humanity lived under the illusion of a flat Earth. It wasn’t a trick of the mind, but a...

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TikZ and PGFPlots for Research: an Introduction

The talk introduces TikZ and PGFPlots, two LaTeX-based packages for producing high-quality vector graphics and scientific plots. It explains their...

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Automated Heuristic Design for Network Operations

Network operation relies on heuristics to solve many tasks rapidly and efficiently across the protocol stack. These heuristics are the...

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RELiQ: Scalable Entanglement Routing via Reinforcement Learning in Quantum Networks

Disculpa, pero esta entrada está disponible sólo en English. Quantum networks are becoming increasingly important because of advancements in quantum...

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Enhancing Cellular Network Positioning Through 5G Technologies for Hybrid Systems and Emergency Scenarios

Fifth-generation cellular networks are introducing new capabilities to significantly enhance positioning accuracy through wideband reference signals specifically designed for time-based...

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The Secret Life of Research Projects

Research projects have a secret life that most PhD students and postdoctoral researchers are not always aware of. Beyond scientific...

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Beyond QKD: Emulating and Orchestrating General Entanglement Networks

Until now, most quantum network deployments have focused primarily on QKD, one of the most mature quantum technologies, where the...

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Model-Aware BitTorrent for Robust and Bandwidth-Efficient Decentralized Federated Learning

Federated Learning (FL) enables training over sensitive, distributed data without centralizing it, but the canonical server-based architecture introduces a single...

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