This study evaluated the adversarial robustness of five state-of-the-art Arabic Language Models against various Arabic adversarial attacks at character, word, and sentence levels. It found that diacritic insertion could reduce model accuracy by up to 92%, while manipulating Arabic conjunctions led to a 58% accuracy degradation, and paraphrasing reduced performance by an average of 76%. While adversarial training improved overall resilience, particularly for MARBERT and AraBERT, challenges against character-level noise persist. Why it matters: These findings are crucial for understanding and mitigating security vulnerabilities in Arabic AI, guiding the development of more robust and safe Arabic NLP systems.
Technology Innovation Institute (TII) has launched Electromagnetic Compatibility (EMC) laboratories in Abu Dhabi, the first such facility in the Arab world. The facility at TII's Directed Energy Research Center (DERC) includes three labs: an EMC semi-anechoic chamber, a pulsed power laboratory, and a low-noise emanation laboratory. These labs will enable evaluation of technologies against electromagnetic interference and support in-country R&D and local industry in line with UAE's 'Operation 300bn'. Why it matters: This advanced infrastructure signals the UAE's commitment to fostering innovation in electronics and related sectors, reducing reliance on foreign testing and certification.
A KAUST-led study published in Science found overwhelming evidence that man-made noise negatively impacts marine fauna and their ecosystems, disrupting behavior, physiology, and reproduction. The researchers assessed over 10,000 papers to demonstrate that noise pollution from shipping, fishing, and infrastructure development harms marine life from invertebrates to whales. They call for human-induced noise to be considered a prevalent stressor at the global scale and for policy to be developed to mitigate its effects. Why it matters: This research highlights the need to consider acoustic dimensions in ocean health restoration efforts, promoting management actions to reduce noise levels and allow marine animals to re-establish their use of ocean sound.
A new paper coauthored by researchers at The University of Melbourne and MBZUAI explores disagreement in human annotation for AI training. The paper treats disagreement as a signal (human label variation or HLV) rather than noise, and proposes new evaluation metrics based on fuzzy set theory. These metrics adapt accuracy and F-score to cases where multiple labels may plausibly apply, aligning model output with the distribution of human judgments. Why it matters: This research addresses a key challenge in NLP by accounting for the inherent ambiguity in human language, potentially leading to more robust and human-aligned AI systems.
Researchers from MBZUAI, Khalifa University, and Sorbonne University Abu Dhabi developed H-SURF, a system of underwater robotic fish that can swim, communicate, and gather information without human guidance. The robotic fish use bioinspired robotics with streamlined bodies, fins, and propellers to produce fluid movement. They communicate with each other using light instead of sound to reduce noise. Why it matters: This award-winning system represents a significant advancement in autonomous underwater robotics, offering a less intrusive way to monitor marine environments and gather data, with potential applications in marine biology and environmental research.
This paper introduces BRIQA, a new method for automated assessment of artifact severity in pediatric brain MRI, which is important for diagnostic accuracy. BRIQA uses gradient-based loss reweighting and a rotating batching scheme to handle class imbalance in artifact severity levels. Experiments show BRIQA improves average macro F1 score from 0.659 to 0.706, especially for Noise, Zipper, Positioning and Contrast artifacts.
A KAUST-led meta-study published in Science examines the increasing ocean noise pollution from human activities like shipping and seismic blasting. The study synthesizes findings from 10,000 papers, revealing that anthropogenic noise interferes with marine animals' communication and ecological processes. The research highlights the need for policymakers to address this issue for ocean health and sustainable economies. Why it matters: Understanding and mitigating ocean noise pollution is crucial for preserving marine ecosystems and the biodiversity of the Red Sea.
This paper introduces a novel two-step method for predicting urban expansion using time-series satellite imagery. The approach combines semantic image segmentation with a CNN-LSTM model to learn temporal features. Experiments on satellite images from Riyadh, Jeddah, and Dammam in Saudi Arabia demonstrate improved performance compared to existing methods based on Mean Square Error, Root Mean Square Error, Peak Signal to Noise Ratio, Structural Similarity Index, and overall classification accuracy.