2,860 research outputs found

    The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences

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    Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective because it fails to account for context: the circumstances under which an application first requests access to data may be vastly different than the circumstances under which it subsequently requests access. We performed a longitudinal 131-person field study to analyze the contextuality behind user privacy decisions to regulate access to sensitive resources. We built a classifier to make privacy decisions on the user's behalf by detecting when context has changed and, when necessary, inferring privacy preferences based on the user's past decisions and behavior. Our goal is to automatically grant appropriate resource requests without further user intervention, deny inappropriate requests, and only prompt the user when the system is uncertain of the user's preferences. We show that our approach can accurately predict users' privacy decisions 96.8% of the time, which is a four-fold reduction in error rate compared to current systems.Comment: 17 pages, 4 figure

    2011-2012 Philharmonia in Concert at Boca West

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    https://spiral.lynn.edu/conservatory_philharmonia/1063/thumbnail.jp

    Doctoral Recital

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    List of performers and performances

    2011-2012 Lynn Philharmonia No. 3

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    2011 Concerto Competition Winners December 3, 2011 at 7:30 PM and December 4, 2011 at 4:00 PM Bruce Polay, guest conductor ; Heqing Huang, piano ; Doniyor Zuparov, cello ; Chun Yu Tsai, marimba ; Anastasiya Timofeeva, piano Overture to Rienzi / Richard Wagner -- Piano Concerto No. 1 in C Major, op. 15 / Ludwig van Beethoven -- Cello Concerto No. 1 in E-flat Major, op. 107 / Dmitri Shostakovich -- Concerto for Marimba and String Orchestra / Emmanuel Séjourné -- Piano Concerto No. 1 in B-flat Minor, op. 23 / Pyotr Ilyich Tchaikovsky 2011-2012 Philharmonia Season Programhttps://spiral.lynn.edu/conservatory_philharmonia/1021/thumbnail.jp

    Inpaint3D: 3D Scene Content Generation using 2D Inpainting Diffusion

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    This paper presents a novel approach to inpainting 3D regions of a scene, given masked multi-view images, by distilling a 2D diffusion model into a learned 3D scene representation (e.g. a NeRF). Unlike 3D generative methods that explicitly condition the diffusion model on camera pose or multi-view information, our diffusion model is conditioned only on a single masked 2D image. Nevertheless, we show that this 2D diffusion model can still serve as a generative prior in a 3D multi-view reconstruction problem where we optimize a NeRF using a combination of score distillation sampling and NeRF reconstruction losses. Predicted depth is used as additional supervision to encourage accurate geometry. We compare our approach to 3D inpainting methods that focus on object removal. Because our method can generate content to fill any 3D masked region, we additionally demonstrate 3D object completion, 3D object replacement, and 3D scene completion
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