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GraphCP achieves up to 20 times faster processing speeds compared to current graph processing technologies, providing substantial enhancements in data analysis across various industries.
A team of researchers has introduced a method using text prompts to improve the security of AI systems against cyber threats, aiming to enhance safety in critical areas such as finance and healthcare.
The newly developed Joint Matrix Decomposition and Factorization (JMDF) framework improves the accuracy and robustness of moving target detection in video streams through the use of fuzzy logic and adaptive constraints.
Researchers from Hubei Minzu University and Wuhan University have further advanced CRD-CGAN, an AI model that generates detailed and varied images from text, offering enhanced applications in sectors like digital marketing and virtual reality.
Researchers have developed the Interactive Relation Embedding Network (IRE-Net), an AI system that can detect contactless human interactions in crowded settings, enhancing tools for public safety and social behavior analysis.
Graph-Segmenter improves image segmentation with graph transformers and boundary-aware attention, boosting road safety and medical diagnostics.
A novel encrypt-then-index strategy improves secure data queries on encrypted cloud databases, offering enhanced efficiency and privacy-preserving machine learning.
MARISMA is a risk analysis framework designed to improve cybersecurity for businesses by offering adaptive, real-time protection for digital assets against evolving threats.
Researchers from Islamic Azad University have developed innovative designs for quantum circuits that reduce costs by over 25% and significantly enhance error detection. These advancements aim to improve the efficiency and reliability of quantum computing.
A new study reveals that 94% of business spreadsheets contain errors, leading to significant risks.
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