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This paper addresses the critical challenge of data scarcity and structural distortion for handwritten Chinese character recognition (HCCR), especially in high-security applications like signature …
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This paper addresses the problem of approximate membership queries (AMQs) over streams where recent-window semantics are needed—that is, answering if an item appeared in the last W insertions, rath…
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HANSEL addresses the challenging problem of verifying complex multi-step tasks performed by AI web agents, such as product search and comparison, where users often face overwhelming and unstructure…
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This survey paper explores the emerging intersection of quantum technologies and ophthalmology, highlighting four key directions — photon-limited retinal imaging, correlation-based (ghost) imaging, …
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This paper addresses a critical gap in Controller Area Network (CAN) intrusion detection — stealthy masquerade and tampering attacks that substitute legitimate frames in situ, preserving traffic per…
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This paper addresses the challenge of monocular 3D occupancy perception for mobile robots navigating cluttered, less structured sidewalk environments
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This paper studies how developers with different problem-solving styles and experience interact with conversational programming assistants, focusing on GitHub Copilot Chat
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This paper addresses the challenging problem of spatiotemporal super-resolution (SR) for complex nonlinear dynamical systems defined on irregular geometries, with a focus on reconstructing high-res…
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This paper addresses a fundamental tension in automatic evaluation metrics for open-ended, opinion-driven question answering by Large Language Models (LLMs)
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This paper addresses the challenge of limited and diverse training data in indoor scene recognition by proposing a novel semantic-aware data augmentation approach using Stable Diffusion (SD), a sta…
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This study addresses the challenge of early prediction and risk stratification of delirium, a common and serious complication in ICU patients linked to worsened outcomes and healthcare costs
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This paper tackles fundamental challenges in unsupervised time series anomaly detection using reconstruction-based methods
