Critical Decentralisation Cluster 36c3 Linux on Open Source Hardware and Open Source Chip Design

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This talk will explore Open Source Hardware projects relevant to Linux, including boards like BeagleBone, Olimex OLinuXino, Giant board and more. Looking at the benefits and challenges of designing Open Source Hardware for a Linux system, along with BeagleBoard.org’s experience of working with community, manufacturers, and distributors to create an Open Source Hardware platform. In closing also looking at the future, Libre Silicon like RISC-V designs, and where this might take Linux.

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What is Xen Project / XCP NG Server Open Source Projects and How It All Works Together

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Open Source Is Hard | LINUX Unplugged 216

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It’s Wes & the Beard this week as we cover tons of stories. TopIcons is officially unmaintained, KDE launches the XFree kwin project in an attempt to rid themselves of XWayland, Synergy goes closed source, Microsoft & Canonical build a custom linux kernel & more!

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Understanding Source Code with Deep Learning

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Code is written by humans for humans and machines. By learning from the human-oriented components of code, recent research has invented models that start to “understand” some aspects of source code. This opens the exciting possibility of using machine learning to assist developers in their everyday tasks, such as writing new code and finding bugs.

In this talk, I will give a brief tour of our lab’s recent explorations in this area. Then I will focus on a specific kind of neural networks, namely graph neural networks (GNN). These networks allow us to learn from the rich semantic relationships within code and, by training them on a self-supervised task, they have allowed us to find bugs in open-source projects. I will conclude with a brief discussion of the practical challenges in using machine learning on source code.

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*Updated Version !* *[18/12/2021]*

*Updated Version !* *[18/12/2021]*

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Understanding Source Code with Deep Learning

Tanki Online Generator Click HERE

Code is written by humans for humans and machines. By learning from the human-oriented components of code, recent research has invented models that start to “understand” some aspects of source code. This opens the exciting possibility of using machine learning to assist developers in their everyday tasks, such as writing new code and finding bugs.

In this talk, I will give a brief tour of our lab’s recent explorations in this area. Then I will focus on a specific kind of neural networks, namely graph neural networks (GNN). These networks allow us to learn from the rich semantic relationships within code and, by training them on a self-supervised task, they have allowed us to find bugs in open-source projects. I will conclude with a brief discussion of the practical challenges in using machine learning on source code.

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