The researchers introduce KAU-CSSL, the first continuous Saudi Sign Language (SSL) dataset focusing on complete sentences. They propose a transformer-based model using ResNet-18 for spatial feature extraction and a Transformer Encoder with Bidirectional LSTM for temporal dependencies. The model achieved 99.02% accuracy in signer-dependent mode and 77.71% in signer-independent mode, advancing communication tools for the SSL community.
The Communications and Computing Systems Lab (CCSL) at KAUST received two awards in the International Telecommunication Union AI for Good Machine Learning Challenge and tinyML Hackathon Challenge 2023: Pedestrian Detection. The KAUST team's solution achieved high accuracy in pedestrian identification using event-based cameras, while consuming less power and achieving lower latency. They also received an award for innovative use of "Edge Impulse" for building datasets and training models. Why it matters: This recognition highlights KAUST's growing influence in AI research, particularly in edge computing and computer vision applications for public safety.
KAUST and King Abdulaziz University (KAU) have signed a memorandum of understanding (MoU) to collaborate and fund scientific research. The collaboration will focus on areas including cancer research, energy harvesting and storage, solar technology, renewable energy, and desert agriculture. The MoU was signed by Donal Bradley, KAUST VP for Research, and Yusuf Al Turki, KAU VP for Graduate Studies and Scientific Research. Why it matters: This partnership between two leading Saudi universities will help advance research and development in areas critical to Saudi Vision 2030.
Saudi Arabia's Minister of Environment, Water and Agriculture inaugurated the Climate Change Center at KAUST. The center will serve as a resource for policymakers, providing climate information and supporting initiatives to mitigate climate change effects. It aligns with Vision 2030 and the Middle East Green Initiative. Why it matters: The center signifies Saudi Arabia's commitment to environmental sustainability and its role in regional and global climate action, potentially fostering AI applications for climate modeling and mitigation.
KAUST researchers found Y-series nonfullerene acceptors enhance the outdoor stability of organic solar cells, enabling energy-efficient windows. They also used satellite data to show managed vegetation can mitigate rising temperatures across Saudi Arabia's agricultural regions. Additionally, they developed DeepKriging, a deep neural network, to solve complex spatiotemporal datasets and tested it on air pollution. Why it matters: This research addresses critical challenges in renewable energy, climate change, and AI data privacy relevant to Saudi Arabia and the broader region.