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Unveiling Hidden Energy Anomalies: Harnessing Deep Learning to Optimize Energy Management in Sports Facilities

arXiv · · Research Energy

This paper explores the use of deep learning for anomaly detection in sports facilities, with the goal of optimizing energy management. The researchers propose a method using Deep Feedforward Neural Networks (DFNN) and threshold estimation techniques to identify anomalies and reduce false alarms. They tested their approach on an aquatic center dataset at Qatar University, achieving 94.33% accuracy and 92.92% F1-score. Why it matters: The research demonstrates the potential of AI to improve energy efficiency and operational effectiveness in sports facilities within the GCC region.

PDNS-Net: A Large Heterogeneous Graph Benchmark Dataset of Network Resolutions for Graph Learning

arXiv · · Research Dataset

The Qatar Computing Research Institute (QCRI) has introduced PDNS-Net, a large heterogeneous graph dataset for malicious domain classification, containing 447K nodes and 897K edges. It is significantly larger than existing heterogeneous graph datasets like IMDB and DBLP. Preliminary evaluations using graph neural networks indicate that further research is needed to improve model performance on large heterogeneous graphs. Why it matters: This dataset will enable researchers to develop and benchmark graph learning algorithms on a scale relevant to real-world cybersecurity applications, particularly for identifying and mitigating malicious online activity.

Application of 2-D Convolutional Neural Networks for Damage Detection in Steel Frame Structures

arXiv · · CV Research

This paper presents a 2-D convolutional neural network (CNN) approach for damage detection in steel frame structures, using raw acceleration signals as input. The method employs a network of lightweight CNNs, each optimized for a specific element, to enhance accuracy and speed. The proposed framework is validated using the Qatar University Grandstand Simulator (QUGS) benchmark data. Why it matters: The research offers a promising AI-driven solution for real-time structural health monitoring, with potential applications for infrastructure maintenance and safety in the GCC region.

AI for Art, Culture and Heritage: History, Trends, Opportunities and Possible Impacts

MBZUAI · · AI Art

Dr. James She from Hamad Bin Khalifa University will give a talk on AI for Art, Culture and Heritage, covering history, trends, opportunities, and potential impacts in the Gulf region. His research focuses on AI and multimedia technologies for art, media, culture, and human creativity, with recent artworks using AI for cultural or heritage content in Arabic regions. He was also a visiting artist-in-residency at Fire Station Museum, Qatar Museums in 2020-2021. Why it matters: This lecture highlights the growing interest and applications of AI in preserving and promoting cultural heritage within the Gulf region, potentially fostering innovation in art and culture.