Archivos: Events

Caught Between Feeling Lucky and Abusing Free Returns: How to Bootstrap Data Marketplaces

Data is the fuel of AI systems, meant to have a profound impact on society and the economy. However, bootstrapping...

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Improving resource utilization in nowadays data centers

Effective scheduling in modern data centers is crucial for optimizing resource allocation, minimizing energy consumption, and ensuring high availability, which...

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Foundations of Reliable Cooperation under Asynchrony, Byzantine Faults and Message Adversaries

Reliable Broadcast (RB) is a fundamental primitive for building fault-tolerant distributed systems, with applications in critical infrastructures, financial systems, and...

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AIoT for Rural and Extreme Environments

The Artificial Intelligence of Things (AIoT) offers great potential for real-time data collection from various devices, benefiting numerous applications. However,...

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A Study of Malicious Source Code Reuse Among GitHub, StackOverflow and Underground Forums

To date, most analysis of collaboration between malware authors has been performed on meta-data and compiled binaries, while ignoring artifacts...

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1st IMDEA Networks Alumni Workshop

IMDEA Networks Institute is delighted to host its 1st Alumni Workshop, bringing together alumni from diverse career paths in industry,...

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I love pinapple on pizza! = I hate pinapple on pizza: Stance-Aware Sentence Transformers for Opinion Mining

Sentence transformers’ job is to convert pieces of text into embedded vectors, where similar (dissimilar) pieces of text have spatially...

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Semana de la Ciencia y la Innovación 2024

A través de una práctica guiada, crearemos paso a paso un asistente virtual con el que controlaremos la iluminación y...

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Characterizing, Modeling and Exploiting the Mobile Demand Footprint of Large Public Protests

Smartphones and mobile applications are staple tools in the operation of current-age public demonstrations, where they support organizers and participants...

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FedQV: Leveraging Quadratic Voting in Federated Learning

Federated Learning (FL) permits different parties to collaboratively train a global model without disclosing their respective local labels. A crucial...

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