Displaying 1 to 20 from 24 results

lwip - Light Weight Image Processor for NodeJS

  •    C

This module provides comprehensive, fast, and simple image processing and manipulation capabilities.There are no external runtime dependencies, which means you don't have to install anything else on your system.

vps-comparison - A comparison between some VPS providers

  •    Python

If you like this project, and you would like to have more plans and providers in the comparison, please take a look at this issue. A comparison between some VPS providers that have data centers located in Europe.

ungoogled-chromium - Modifications to Google Chromium for removing Google integration and enhancing privacy, control, and transparency

  •    Python

A number of features or background services communicate with Google servers despite the absence of an associated Google account or compiled-in Google API keys. Furthermore, the normal build process for Chromium involves running Google's own high-level commands that invoke many scripts and utilities, some of which download and use pre-built binaries provided by Google. Even the final build output includes some pre-built binaries. Fortunately, the source code is available for everything. ungoogled-chromium should not be considered a fork of Chromium. The main reason for this is that a fork is associated with more significant deviations from the Chromium, such as branding, configuration formats, file locations, and other interface changes. ungoogled-chromium will not modify the Chromium browser outside of the project's goals.

GlobaLeaks - Free and Open Source Software Intended to Enable Secure and Anonymous Whistleblowing Initiatives

  •    Python

GlobaLeaks is open-source / free software intended to enable secure and anonymous whistleblowing initiatives developed by the Hermes Center for Transparency and Digital Human Rights. For the user manual refer to the GlobaLeaks's User Manual.




seeThru - HTML5 video with alpha channel transparencies

  •    Javascript

Your HTML5 video source is re-rendered into a canvas-element, adding the possibility to use transparencies in your video. Alpha information is either included in the video's source file (moving) or in a seperate <img>-element (static). The package also ships with a simple CLI tool for automatically converting your RGBA video sources into the correct format.

awesome-humane-tech - Improve society using this list of humane tech projects

  •    

This list focuses on (mostly open-source) projects that are directly related to humane tech topics. Please read the guidelines for contributing before sending PR's. This section is for inactive projects that are nonetheless still of interest regarding their topics or code.


theyworkforyou - Keeping tabs on the UK's parliaments and assemblies

  •    Python

If you're looking to set up a new Parliamentary monitoring site then you should look at our Pombola probject at https://www.mysociety.org/international/pombola/ which takes the lessons we've learned from writing and running TheyWorkForYou and uses them to create a modern, flexible and more easily adaptable platform for creating your own Parliamentary monitoring site. We strongly encourage people to use this rather than trying to adapt TheyWorkForYou to their own requirements.If you want to dig in to the source of TheyWorkForYou then carry right on below.

trillian-examples - A place to store some examples which use Trillian APIs to build things.

  •    Go

This repository contains example applications built on top of Trillian, showing that it's possible to apply Transparency concepts to problems other than Certificates. These examples are not supported per-se, but the Trillian team will likely try to help where possible. You can contact them via the channels listed under Support on the Trillian repo.

translucify - Makes an image's background transparent. Replaces <img> with <canvas>.

  •    Javascript

Makes an image's background pixels transparent. Replaces img with canvas. Use the flood-fill method for making the background transparent.

django-public-project - Custom Python/Django CMS - Transparency for Public Projects (used for BERwatch/BLBwatch)

  •    Javascript

Django Public Project (DPP) is a custom CMS for making large public projects, political processes and enquiry commissions more transparent.

mli-resources - Machine Learning Interpretability Resources

  •    Jupyter

Machine learning algorithms create potentially more accurate models than linear models, but any increase in accuracy over more traditional, better-understood, and more easily explainable techniques is not practical for those who must explain their models to regulators or customers. For many decades, the models created by machine learning algorithms were generally taken to be black-boxes. However, a recent flurry of research has introduced credible techniques for interpreting complex, machine-learned models. Materials presented here illustrate applications or adaptations of these techniques for practicing data scientists. Want to contribute your own examples? Just make a pull request.

diabetes_use_case - Sample use case for Xavier AI in Healthcare conference: https://www

  •    Jupyter

Recent advances enable practitioners to break open machine learning’s “black box”. From machine learning algorithms guiding analytical tests in drug manufacture, to predictive models recommending courses of treatment, to sophisticated software that can read images better than doctors, machine learning has promised a new world of healthcare where algorithms can assist, or even outperform, professionals in consistency and accuracy, saving money and avoiding potentially life-threatening mistakes. But what if your doctor told you that you were sick but could not tell you why? Imagine a hospital that hospitalized and discharged patients but was unable to provide specific justification for these decisions. For decades, this was a roadblock for the adoption of machine learning algorithms in healthcare: they could make data-driven decisions that helped practitioners, payers, and patients, but they couldn’t tell users why those decisions were made.

interpretable_machine_learning_with_python - Practical techniques for interpreting machine learning models

  •    Jupyter

Monotonicity constraints can turn opaque, complex models into transparent, and potentially regulator-approved models, by ensuring predictions only increase or only decrease for any change in a given input variable. In this notebook, I will demonstrate how to use monotonicity constraints in the popular open source gradient boosting package XGBoost to train a simple, accurate, nonlinear classifier on the UCI credit card default data. Once we have trained a monotonic XGBoost model, we will use partial dependence plots and individual conditional expectation (ICE) plots to investigate the internal mechanisms of the model and to verify its monotonic behavior. Partial dependence plots show us the way machine-learned response functions change based on the values of one or two input variables of interest, while averaging out the effects of all other input variables. ICE plots can be used to create more localized descriptions of model predictions, and ICE plots pair nicely with partial dependence plots. An example of generating regulator mandated reason codes from high fidelity Shapley explanations for any model prediction is also presented. The combination of monotonic XGBoost, partial dependence, ICE, and Shapley explanations is likely the most direct way to create an interpretable machine learning model today.

transparency - Read-only mirror of https://owo.codes/whats-this/transparency

  •    

A log of all DMCA takedown requests and their outcome, removed file metadata, and our canary. All dates in this repository are in the format MMM DD, YYYY [HH:mm] or YYYY-MM-DD [HH:mm].

code-of-conduct - Internal documentation of the DSF Code of Conduct committee

  •    

On 31st July 2013, Django community adopted a Code of Conduct. This documentation aims to transparently describe how Django Code of Conduct committee enforces the Code of Conduct and how it all works behind the scenes. There is still a lot we're figuring out and learning as we go, so we don't consider this documentation to be finished or complete. However, we hope it's going to be useful base for other communities to not only adopt, but also implement and enforce the Code of Conduct.

supporters - 🙇 Details of our supporters and our commitments to them

  •    

This repository contains the details of our contractual obligations to our fiscal sponsor Brave New Software and any commitments we have made in exchange for financial support. This respository also contains any successful application for financial support in full. Our aim here are simple. Complete transparency and accountabilty of our actions on behalf of our community, by the community.





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