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Creators/Authors contains: "Kastner, Ryan"

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  1. Free, publicly-accessible full text available December 1, 2025
  2. Education technology (EdTech) is an important tool for streamlining and improving course administration and teaching. Many modern EdTech tools rely on cloud services to host containerized applications. While this is convenient, it is also costly in terms of both dollars and carbon emissions. We propose the alternative approach of hosting containerized EdTech applications on local clusters of upcycled Android devices. We perform an evaluation of the Google Pixel Fold for handling educational workloads. Our findings suggest that such repurposed device could effectively bridge the gap between mobile and traditional computing platforms in education, open new avenues for accessible educational computing environments. 
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    Free, publicly-accessible full text available November 2, 2025
  3. Ecologists interested in monitoring the effects caused by climate change are increasingly turning to passive acoustic monitoring, the practice of placing autonomous audio recording units in ecosystems to monitor species richness and occupancy via species calls. However, identifying species calls in large datasets by hand is an expensive task, leading to a reliance on machine learning models. Due to a lack of annotated datasets of soundscape recordings, these models are often trained on large databases of community created focal recordings. A challenge of training on such data is that clips are given a "weak label," a single label that represents the whole clip. This includes segments that only have background noise but are labeled as calls in the training data, reducing model performance. Heuristic methods exist to convert clip-level labels to "strong" call-specific labels, where the label tightly bounds the temporal length of the call and better identifies bird vocalizations. Our work improves on the current weakly to strongly labeled method used on the training data for BirdNET, the current most popular model for audio species classification. We utilize an existing RNN-CNN hybrid, resulting in a precision improvement of 12% (going to 90% precision) against our new strongly hand-labeled dataset of Peruvian bird species.Jacob Ayers (Engineers for Exploration at UCSD); Sean Perry (University of California San Diego); Samantha Prestrelski (UC San Diego); Tianqi Zhang (Engineers for Exploration); Ludwig von Schoenfeldt (University of California San Diego); Mugen Blue (UC Merced); Gabriel Steinberg (Demining Research Community); Mathias Tobler (San Diego Zoo Wildlife Alliance); Ian Ingram (San Diego Zoo Wildlife Alliance); Curt Schurgers (UC San Diego); Ryan Kastner (University of California San Diego) 
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    Free, publicly-accessible full text available December 13, 2025