KAUST hosted representatives from Islamic Development Bank (IsDB) member countries to showcase its aquaculture expertise. The IsDB funded the visit, with co-investment from Innovative Contractors for Advanced Dimensions (ICAD), to introduce KAUST's aquaculture technology to representatives from Morocco, Mali, Burkina Faso and Egypt. The visit aimed to accelerate aquaculture capabilities in North and West Africa, with ICAD pledging up to $20 million in grants for future projects using KAUST technology. Why it matters: This collaboration demonstrates KAUST's role as a regional hub for advanced aquaculture technology and promotes sustainable food production in IsDB member countries.
This paper introduces a unified deep autoregressive model (UAE) for cardinality estimation that learns joint data distributions from both data and query workloads. It uses differentiable progressive sampling with the Gumbel-Softmax trick to incorporate supervised query information into the deep autoregressive model. Experiments show UAE achieves better accuracy and efficiency compared to state-of-the-art methods.
The Symposium on Data Mining and Applications (SDMA 2014) was organized by MEGDAM to foster collaboration among data mining and machine learning researchers in Saudi Arabia, GCC countries, and the Middle East. The symposium covered areas such as statistics, computational intelligence, pattern recognition, databases, Big Data Mining and visualization. Acceptance was based on originality, significance and quality of contribution.
The paper introduces ADAB (Arabic Politeness Dataset), a new annotated Arabic dataset for politeness detection collected from online platforms. The dataset covers Modern Standard Arabic and multiple dialects (Gulf, Egyptian, Levantine, and Maghrebi). It contains 10,000 samples across 16 politeness categories and achieves substantial inter-annotator agreement (kappa = 0.703). Why it matters: This dataset addresses the under-explored area of Arabic-language resources for politeness detection, which is crucial for culturally-aware NLP systems.