299 citations
- Qatar FoundationQA19 papers
- Qatar UniversityQA9 papers
- Hong Kong University of Science and TechnologyHK5 papers
- Centre National de la Recherche ScientifiqueFR3 papers
- Franche-Comté Électronique Mécanique Thermique et Optique - Sciences et TechnologiesFR3 papers
- Nanyang Technological UniversitySG3 papers
- New Jersey Institute of TechnologyUS3 papers
- Purdue University West LafayetteUS3 papers
- Texas A&M University at QatarQA3 papers
- The University of TokyoJP3 papers
- Tsinghua UniversityCN3 papers
- Advanced Digital Sciences CenterSG2 papers
9 papers · 1 filter
Fine-grained Population Mapping from Coarse Census Counts and Open Geodata
Nando Metzger, John E. Vargas-Muñoz, Rodrigo C. Daudt +6
Fine-grained population maps are needed in several domains, like urban planning, environmental monitoring, public health, and humanitarian operations. Unfortunately, in many countr…
Artificial Intelligence-Based Methods for Fusion of Electronic Health Records and Imaging Data
Farida Mohsen, Hazrat Ali, Nady El Hajj +1
Healthcare data are inherently multimodal, including electronic health records (EHR), medical images, and multi-omics data. Combining these multimodal data sources contributes to a…
RL-DistPrivacy: Privacy-Aware Distributed Deep Inference for low latency IoT systems
Emna Baccour, Aiman Erbad, Amr Mohamed +2
Although Deep Neural Networks (DNN) have become the backbone technology of several ubiquitous applications, their deployment in resource-constrained machines, e.g., Internet of Thi…
Threshold-Based Data Exclusion Approach for Energy-Efficient Federated Edge Learning
Abdullatif Albaseer, Mohamed Abdallah, Ala Al-Fuqaha +1
Federated edge learning (FEEL) is a promising distributed learning technique for next-generation wireless networks. FEEL preserves the user's privacy, reduces the communication cos…
Analysis and Optimal Edge Assignment For Hierarchical Federated Learning on Non-IID Data
Naram Mhaisen, Alaa Awad, Amr Mohamed +2
Distributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devic…
RPT: Relational Pre-trained Transformer Is Almost All You Need towards Democratizing Data Preparation
Nan Tang, Ju Fan, Fangyi Li +5
Can AI help automate human-easy but computer-hard data preparation tasks that burden data scientists, practitioners, and crowd workers? We answer this question by presenting RPT, a…