In disruption-tolerant networking (DTN), data is transmitted in a store-carry-forward fashion from network node to network node. We are presenting free and open source DTN implementations of the recently released Bundle Protocol Version 7. DTN7-go is written in Go and provides features like memory safety and concurrent execution.
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OpenDrop is a command-line tool written in Python that allows sharing files between devices directly over Wi-Fi. Its unique feature is that it is protocol-compatible with Apple AirDrop which allows to share files with Apple devices running iOS and macOS.
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Open Wireless Link (OWL) is an open implementation of the Apple Wireless Direct Link (AWDL) ad hoc protocol for Linux and macOS written in C.
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OpenHaystack is a framework for tracking personal Bluetooth devices via Apple’s massive Find My network. Use it to create your own tracking tags that you can append to physical objects (keyrings, backpacks, …) or integrate it into other Bluetooth-capable devices such as notebooks.
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ChirpOTLE is a practical LoRaWAN security evaluation framework that provides tools for the deployment and management of a LoRa testbed based on COTS hardware. It allows managing LoRa field nodes from a central controller and to orchestrate experiments and tests using a Python 3 interface.
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“Adapter” refers to a set of newly introduced weights, typically within the layers of a transformer model. Adapters provide an alternative to fully fine-tuning the model for each downstream task, while maintaining performance.
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We proposed SPARCODE, a community detection method that uses spectral partitioning based on estimating a robust and sparse graph model.
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image_projection is a ROS package to create various projections from multiple calibrated cameras.
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We developed a solution named PrivateDrop to replace the flawed original AirDrop design. PrivateDrop is based on optimized cryptographic private set intersection protocols that can securely perform the contact discovery process between two users without exchanging vulnerable hash values.
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Computes the solution path of the Terminating-LARS (T-LARS) algorithm, which is a major building block of the T-Rex Selector.
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Performs fast variable selection in large-scale high-dimensional settings while controlling the false discovery rate (FDR) at a user-defined target level.
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Single-stage semantic segmentation from image labels
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Self-supervised augmentation consistency for adapting semantic segmentation
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Dense unsupervised learning for video segmentation
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