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This paper asks a practical question that most text-only plagiarism detectors dodge: can we detect LLM-assisted academic dishonesty by looking at how the text was produced, not just the final prose…
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This paper addresses the increasing ineffectiveness of conventional human verification methods such as CAPTCHAs against advanced AI bots capable of solving visual puzzles, emulating typing behavior…
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This paper addresses online learning to rank under adversarially corrupted click feedback, modeled as cascading bandits
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This paper studies robust streaming algorithms under a novel adversarial model where the adversary is adaptive but memory constrained—either memoryless (no persistent memory) or low-memory (a small…
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This paper addresses an emerging integrity risk in crowdsourcing platforms like Amazon Mechanical Turk (MTurk) — the presence of "puppeteers"—human workers who control multiple "puppet" accounts tha…
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This paper provides a comprehensive review and synthesis of the mathematical concepts of chaos, cantori, and turnstiles as they apply to magnetic field lines in toroidal fusion plasmas, specificall…
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This paper asks whether ANN and KDE sketches can be made truly sublinear in streaming settings without giving up useful approximation guarantees
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This paper investigates whether authorship attribution classifiers used on social media text are fair across different demographic groups, specifically gender, native language, and age
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PassREfinder-FL tackles a practical problem that sits between pure detection and pure policy enforcement: how to predict which website pairs are likely to share reused passwords, so administrators …
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The paper addresses a critical bottleneck in neural theorem proving: the excessive length and complexity of formal proofs generated by state-of-the-art reinforcement learning (RL) trained language …
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This paper asks a practical but underexplored question: what happens to web content delivery when the access network is not a terrestrial ISP but a LEO satellite network like Starlink? The authors …
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This paper addresses the problem of automatically detecting a user's familiarity with virtual reality (VR) systems by analyzing behavioral biometrics during VR interactions
