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From lab to libre software: how can academic software research become open source?

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OSS
Sci/Tech

Academics generate enormous amounts of software, some of which inspires commercial innovations in networking and other areas. But little academic software gets released to the public and even less enters common use. Is some vast "dark matter" being overlooked in the academic community? Would the world benefit from academics turning more of their software into free and open projects?

I asked myself these questions a few months ago when Red Hat, at its opening of a new innovation center in Boston's high-tech Fort Point neighborhood, announced a unique partnership with the goal of tapping academia. Red Hat is joining with Boston-area computer science departments—starting with Boston University—to identify promising software developed in academic projects and to turn it into viable free-software projects. Because all software released by Red Hat is under free licenses, the partnership suggests a new channel by which academic software could find wider use.

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FOSS in Science: Harvard, Xiaopeng, Georgia Tech

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OSS
Sci/Tech
  • Making transparency work for Harvard's Dataverse Project

    A culture of transparency permeates the Dataverse project, contributing to its adoption in dozens of research institutions around the world. Headquartered at Harvard University, the Dataverse development team has more than a decade of experience operating as an open source project within an organization that values transparency: the Institute of Quantitative Social Science (IQSS). Working transparently helps the Dataverse team communicate changes to current development efforts, provides opportunities for the community to support each other, and facilitates contribution to the project.

  • Chinese Startup Goes All Out Cloning Tesla Via Open-Source Patents

    Thanks to Tesla’s decision to open-source its patents, Chinese startup Xiaopeng Motors has already manufactured several Model X clones.

  • Open Source Machine Learning Helps the Fight Against Cancer

    Here's an open invitation to steal. It goes out to cancer fighters and tempts them with a new program that predicts cancer drug effectiveness via machine learning and raw genetic data.

    The researchers who built the program at the Georgia Institute of Technology would like cancer fighters to take it for free, or even just swipe parts of their programming code, so they've made it open source. They hope to attract a crowd of researchers who will also share their own cancer and computer expertise and data to improve upon the program and save more lives together.

  • Georgia Tech researchers release open-source AI algorithm to predict effectiveness of cancer drugs

    A team of researchers from Atlanta-based Georgia Institute of Technology introduced an open-source algorithm Oct. 26 that predicts a cancer drug's effectiveness based on a patient's genetic data.

    The researchers developed the machine learning algorithm using gene expression and drug response data from the National Cancer Institute's panel of 60 human cancer cell lines. Their goal was to create an algorithm that predicts optimal drug therapies based on individual patient tumors.

    In a study of 273 cancer patients, researchers found the algorithm to be about 85 percent accurate in assessing the effectiveness of nine drugs. By releasing the algorithm on an open-source platform, they hope other researchers will participate in refining their work.

KStars 2.8.7 Released!

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KDE
Sci/Tech

Another minor KStars release is now available for Linux, MacOS, & Windows. KStars 2.8.7 brings several bug fixes and new improvements!

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KStars 2.8.5 is released!

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KDE
Software
Sci/Tech

So here comes another minor release for KStars v2.8.5 just less than two weeks after 2.8.4 was released. KStars v2.8.5 is available for Windows, MacOS, and Linux.

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Release GCompris Qt 0.80

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KDE
Sci/Tech

We are pleased to announce the release of GCompris version 0.80.

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Fake News From Fossbytes and Techworm

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Software
Sci/Tech

Artificial intelligence/Machine learning

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OSS
Sci/Tech
  • Is your AI being handed to you by Google? Try Apache open source – Amazon's AWS did

    Surprisingly, the MXNet Machine Learning project was this month accepted by the Apache Software Foundation as an open-source project.

    What's surprising about the announcement isn't so much that the ASF is accepting this face in the crowd to its ranks – it's hard to turn around in the software world these days without tripping over ML tools – but rather that MXNet developers, most of whom are from Amazon, believe ASF is relevant.

  • Current Trends in Tools for Large-Scale Machine Learning

    During the past decade, enterprises have begun using machine learning (ML) to collect and analyze large amounts of data to obtain a competitive advantage. Now some are looking to go even deeper – using a subset of machine learning techniques called deep learning (DL), they are seeking to delve into the more esoteric properties hidden in the data. The goal is to create predictive applications for such areas as fraud detection, demand forecasting, click prediction, and other data-intensive analyses.

  • Your IDE won't change, but YOU will: HELLO! Machine learning

    Machine learning has become a buzzword. A branch of Artificial Intelligence, it adds marketing sparkle to everything from intrusion detection tools to business analytics. What is it, exactly, and how can you code it?

  • Artificial intelligence: Understanding how machines learn

    Learning the inner workings of artificial intelligence is an antidote to these worries. And this knowledge can facilitate both responsible and carefree engagement.

  • Your future boss? An employee-interrogating bot – it's an open-source gift from Dropbox

    Dropbox has released the code for the chatbot it uses to question employees about interactions with corporate systems, in the hope that it can help other organizations automate security processes and improve employee awareness of security concerns.

    "One of the hardest, most time-consuming parts of security monitoring is manually reaching out to employees to confirm their actions," said Alex Bertsch, formerly a Dropbox intern and now a teaching assistant at Brown University, in a blog post. "Despite already spending a significant amount of time on reach-outs, there were still alerts that we didn't have time to follow up on."

Scientific Linux 7.3 Released

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Red Hat
Sci/Tech
  • Scientific Linux 7.3 Officially Released, Based on Red Hat Enterprise Linux 7.3

    After two Release Candidate (RC) development builds, the final version of the Scientific Linux 7.3 operating system arrived today, January 26, 2017, as announced by developer Pat Riehecky.

    Derived from the freely distributed sources of the commercial Red Hat Enterprise Linux 7.3 operating system, Scientific Linux 7.3 includes many updated components and all the GNU/Linux/Open Source technologies from the upstream release.

    Of course, all of Red Hat Enterprise Linux's specific packages have been removed from Scientific Linux, which now supports Scientific Linux Contexts, allowing users to create local customization for their computing needs much more efficiently than before.

  • Scientific Linux 7.3 Released

    For users of Scientific Linux, the 7.3 release is now available based off Red Hat Enterprise Linux 7.3.

The code that took America to the moon was just published to GitHub, and it’s like a 1960s time capsule

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OSS
Sci/Tech

When programmers at the MIT Instrumentation Laboratory set out to develop the flight software for the Apollo 11 space program in the mid-1960s, the necessary technology did not exist. They had to invent it.

They came up with a new way to store computer programs, called “rope memory,” and created a special version of the assembly programming language. Assembly itself is obscure to many of today’s programmers—it’s very difficult to read, intended to be easily understood by computers, not humans. For the Apollo Guidance Computer (AGC), MIT programmers wrote thousands of lines of that esoteric code.

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Linux 4.10 To Linux 4.15 Kernel Benchmarks

The ThinkPad X1 Carbon has been enjoying its time on Linux 4.15. In addition to the recent boot time tests and kernel power comparison, here are some raw performance benchmarks looking at the speed from Linux 4.10 through Linux 4.15 Git. With this Broadwell-era Core i7 5600U laptop with 8GB RAM, HD Graphics, and 128GB SATA 3.0 SSD with Ubuntu 17.10 x86_64, the Linux 4.10 through 4.15 Git mainline kernels were benchmarked. Each one was tested "out of the box" and the kernel builds were obtained from the Ubuntu Mainline Kernel archive. Read more