Jos van der Velde

ORCID: 0000-0003-0430-1532
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About
Contact & Profiles
Research Areas
  • Advanced Data Storage Technologies
  • Machine Learning and Data Classification
  • Scientific Computing and Data Management
  • Mobile Crowdsensing and Crowdsourcing
  • Network Security and Intrusion Detection
  • Maritime Navigation and Safety
  • Target Tracking and Data Fusion in Sensor Networks
  • Information Retrieval and Search Behavior
  • Expert finding and Q&A systems

Netherlands Organisation for Applied Scientific Research
2019

Amsterdam University of the Arts
2015

Data is a critical resource for Machine Learning (ML), yet working with data remains key friction point. This paper introduces Croissant, metadata format datasets that simplifies how used by ML tools and frameworks. Croissant makes more discoverable, portable interoperable, thereby addressing significant challenges in management responsible AI. already supported several popular dataset repositories, spanning hundreds of thousands datasets, ready to be loaded into the most

10.1145/3650203.3663326 article EN 2024-05-29

Online evaluation methods for information retrieval use implicit signals such as clicks from users to infer preferences between rankers. A highly sensitive way of inferring these is through interleaved comparisons. Recently, comparisons that allow simultaneous more than two rankers have been introduced. These so-called multileaving are even their interleaving counterparts. Probabilistic interleaving--whose main selling point the potential reuse historical data--has no counterpart yet. We...

10.1145/2766462.2767838 article EN Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval 2015-08-04

Data is a critical resource for Machine Learning (ML), yet working with data remains key friction point. This paper introduces Croissant, metadata format datasets that simplifies how used by ML tools and frameworks. Croissant makes more discoverable, portable interoperable, thereby addressing significant challenges in management responsible AI. already supported several popular dataset repositories, spanning hundreds of thousands datasets, ready to be loaded into the most

10.1145/3650203.3663326 preprint EN arXiv (Cornell University) 2024-03-28

We present a study of border surveillance systems for automatic threat estimation. The should allow control operators to be triggered in time so that adequate responses are possible. Examples threats smuggling, possibly by using small vessels, cars or drones, and caused unwanted persons (e.g. terrorists) crossing the border. These revealed indicators which often not exact evidence these incorporates significant amounts uncertainty. This is linked European Horizon 2020 project ALFA, focuses...

10.1117/12.2532308 article EN 2019-10-07
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