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CORDIS - Forschungsergebnisse der EU
CORDIS

Powder is the only app offering a complete turnkey solution to automatically detect the best gaming moments, earn rewards from quests and challenges, and compete with other gamers.

CORDIS bietet Links zu öffentlichen Ergebnissen und Veröffentlichungen von HORIZONT-Projekten.

Links zu Ergebnissen und Veröffentlichungen von RP7-Projekten sowie Links zu einigen Typen spezifischer Ergebnisse wie Datensätzen und Software werden dynamisch von OpenAIRE abgerufen.

Leistungen

Models creation and integration for new games (öffnet in neuem Fenster)

By adding new games to our pipeline we are able to offer richer quests and challenges experiences

Challenge feed recommendation system (öffnet in neuem Fenster)

Our existing RecSys project is reconverted and improved to support quests and challenges engagement

Wallet supports the 3 major blockchains (öffnet in neuem Fenster)

Continuous effort of adding multiple blockchains / wallets supported

PC configurations management (öffnet in neuem Fenster)

PC configurations management (continuous)

Integration of 30 detection models on PC (öffnet in neuem Fenster)

PC app supports 30 detection models

Integration of 20 detection models on PC (öffnet in neuem Fenster)

Integration of 20 AI detection models to our PC app

Onboarding discovery (öffnet in neuem Fenster)

The user experience of quests and challenges is optimised on mobile

Setting up a process for prototype testing (öffnet in neuem Fenster)

Industrialising our prototype testing

Continuous improvements on PC video recorder CPU & GPU usage (öffnet in neuem Fenster)

Reduce CPU GPU usage on the users devices

Production launch and nominal operation on PC (öffnet in neuem Fenster)

Crypto wallet is launched on our PC app at nominal operations

Digital assets marketplace test release (öffnet in neuem Fenster)

In-app test release

Partners due diligence and selection (Crypto wallet) (öffnet in neuem Fenster)

We identify audit and select one or multiple crypto wallet partners

Leaderboard launch (öffnet in neuem Fenster)

A leaderboard where users can see their ranking compared to other participants to quests and challenges is relased

Integration of 15 detection models on PC (öffnet in neuem Fenster)

PC app supports 15 detection models

Security and implementation testing (öffnet in neuem Fenster)

Security is critical when handling users assets

Reaching 80% fidelity in new models detection (öffnet in neuem Fenster)

Reaching high levels of fidelity in the new games added to the model

Improvement on the delays to create an integrate new models for new games (öffnet in neuem Fenster)

In the perspective of scaling our user base, scaling our approach becomes critical

Optimisation of video resolution (öffnet in neuem Fenster)

Optimisation of video resolution for external sharing

Redesign of the user profile to highlight rewards PC (öffnet in neuem Fenster)

Redesign of the user profile to highlight rewards user library

Guaranteeing efficient R&D activities (öffnet in neuem Fenster)

Ensuring an high level view of all R&D activities all along the project timeline

Redesign of the user profile to highlight rewards Mobile (öffnet in neuem Fenster)

The user profile is redesigned fully to offer a consistent and enjoyable app experience

Reduce crash rate (öffnet in neuem Fenster)

reduce the crash rate to 5 to offer a seamless experience

Digital assets marketplace product release (öffnet in neuem Fenster)

Product release in the public app

Set up of automatic bots to share clips, achievements, etc. with other users (öffnet in neuem Fenster)

Distribution set up of automatic bots to share clips achievements etc with other users

PhD research thesis (öffnet in neuem Fenster)

"PhD research thesis ""Analysis of human behavior from video games using deep learning approaches"" (due in 36 months)"

Reach 100k Discord users (öffnet in neuem Fenster)

Reach 100k Discord users on Powder servers / using the Powder discord bot

Veröffentlichungen

Pattern Recognition Letters (öffnet in neuem Fenster)

Autoren: Liam Schoneveld, Alice Othmani, Hazem Abdelkawy
Veröffentlicht in: Pattern Recognition Letters, 2021, ISSN 0167-8655
Herausgeber: Elsevier
DOI: 10.1016/j.patrec.2021.03.007

Comparing Learning Methodologies for Self-Supervised Audio-Visual Representation Learning (öffnet in neuem Fenster)

Autoren: H. Terbouche, L. Schoneveld, O. Benson, A. Othmani
Veröffentlicht in: IEEE Access, Ausgabe Volume 10, pp. 41622-41638, 2022, ISSN 2169-3536
Herausgeber: IEEE Access
DOI: 10.1109/ACCESS.2022.3164745

Towards a General Deep Feature Extractor for Facial Expression Recognition (öffnet in neuem Fenster)

Autoren: L. Schoneveld, A. Othmani
Veröffentlicht in: 2021 IEEE International Conference on Image Processing (ICIP), Ausgabe 2339-2342, 2021, ISSN 2381-8549
Herausgeber: IEEE Computer Society
DOI: 10.1109/ICIP42928.2021.9506025

Multi-Annotation Attention Model for Video Summarization

Autoren: Hacene Terbouche, Maryan Morel, Mariano Rodriguez, Alice Othmani
Veröffentlicht in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2023, ISSN 3142-3151
Herausgeber: IEEE/CVF (Computer Vision Foundation)

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