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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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accountability for designing, establishing, and governing the financial architecture, management reporting structures, statutory compliance, and consolidation frameworks across all Monash University controlled
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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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cooperating with each other, but in many cases competing for individual gains. This structure may not always work for the benefit of science. The purpose of this project is to use game theory and computational
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presence of irrelevant features can conceal the presence of anomalies. For this we propose to explore architectures such as deep belief networks (DBNs) as they are a promising technique for learning robust