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models (VLMs) on edge hardware. While VLMs have demonstrated strong capabilities in multimodal reasoning and understanding, their high computational and memory demands pose significant challenges for real
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This project aims to develop a computer vision system capable of detecting and classifying domestic geographic landmarks in images and video content. By categorizing locations such as “childcare
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involve designing a multi-agent AI security architecture where different agents perform different roles, such as attack analyst, vulnerability investigator, response planner, patch generator, risk evaluator
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, software architectures, Machine Learning
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computational techniques can be combined with classical systems to improve performance, scalability, and solution quality for tasks such as: Similarity search and nearest-neighbour queries Graph and routing
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PatchSentinel-X: Transformer-Based Security Patch Intelligence for Vulnerability Lifecycle Assurance
, and regression signals. Contribution 2 A multi-modal Transformer architecture for security patch reasoning A model that jointly learns from code, diffs, natural-language advisories, and vulnerability
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Background and Motivation Modern deep learning models have achieved remarkable success in computer vision and natural language processing. However, they typically produce overconfident predictions
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management, distributed computing, and energy-aware computing, preparing them for impactful roles in industry and research. Key Components and Example Scenarios Predictive Resource Allocation and Load
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of Agentic Software Engineering (Agentic SWE) — redefining what it means to “develop software.” Students will gain exposure to autonomous AI systems, software intelligence architectures, and human-in-the-loop
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, identifying molecular disease signatures and matching them with the most effective therapeutic interventions are essential. The Hudson‐Monash Paediatric Precision Medicine (HMPPM) Program aims to develop and