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A Github Pages template for academic websites. This was forked (then detached) by Stuart Geiger from the Minimal Mistakes Jekyll Theme, which is © 2016 Michael Rose and released under the MIT License. See LICENSE.md.

I think I've got things running smoothly and fixed some major bugs, but feel free to file issues or make pull requests if you want to improve the generic template / theme.

Note: if you are using this repo and now get a notification about a security vulnerability, delete the Gemfile.lock file.

Instructions

  1. Register a GitHub account if you don't have one and confirm your e-mail (required!)
  2. Fork this repository by clicking the "fork" button in the top right.
  3. Go to the repository's settings (rightmost item in the tabs that start with "Code", should be below "Unwatch"). Rename the repository "[your GitHub username].github.io", which will also be your website's URL.
  4. Set site-wide configuration and create content & metadata (see below -- also see this set of diffs showing what files were changed to set up an example site for a user with the username "getorg-testacct")
  5. Upload any files (like PDFs, .zip files, etc.) to the files/ directory. They will appear at https://[your GitHub username].github.io/files/example.pdf.
  6. Check status by going to the repository settings, in the "GitHub pages" section
  7. (Optional) Use the Jupyter notebooks or python scripts in the markdown_generator folder to generate markdown files for publications and talks from a TSV file.

See more info at https://academicpages.github.io/

To run locally (not on GitHub Pages, to serve on your own computer)

  1. Clone the repository and made updates as detailed above
  2. Make sure you have ruby-dev, bundler, and nodejs installed: sudo apt install ruby-dev ruby-bundler nodejs
  3. Run bundle clean to clean up the directory (no need to run --force)
  4. Run bundle install to install ruby dependencies. If you get errors, delete Gemfile.lock and try again.
  5. Run bundle exec jekyll liveserve to generate the HTML and serve it from localhost:4000 the local server will automatically rebuild and refresh the pages on change.

Changelog -- bugfixes and enhancements

There is one logistical issue with a ready-to-fork template theme like academic pages that makes it a little tricky to get bug fixes and updates to the core theme. If you fork this repository, customize it, then pull again, you'll probably get merge conflicts. If you want to save your various .yml configuration files and markdown files, you can delete the repository and fork it again. Or you can manually patch.

To support this, all changes to the underlying code appear as a closed issue with the tag 'code change' -- get the list here. Each issue thread includes a comment linking to the single commit or a diff across multiple commits, so those with forked repositories can easily identify what they need to patch.

artificialnouveau's Projects

deepface icon deepface

A Lightweight Face Recognition and Facial Attribute Analysis (Age, Gender, Emotion and Race) Library for Python

deepvoice3_pytorch icon deepvoice3_pytorch

PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

deface icon deface

Video anonymization by face detection

detectron2 icon detectron2

Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.

easy-document-scanner icon easy-document-scanner

Developed a simple function that detects and rotates documents so that it can be viewed from a bird's eye view.

emotion icon emotion

A pipeline to collect data and fine-tune computer vision models to detect emotions in human faces

face_recognition_project icon face_recognition_project

Gender/Race/Emotion classifications based on facial multi-attribute detection were realized through data pre-processing, face detection and extraction by OpenCV, developing training models through “Fisherfaces” and “Convolutional Neural Network” approaches by TensorFlow and Keras with accuracy of 98.44%, 84.24%, and 70% respectively. A web app was developed for better demonstration and further model optimization by expanding training dataset and user labeling, adopted Nginx to serve web server and deployed on DigitalOcean.

faceoff icon faceoff

Steps towards physical adversarial attacks on facial recognition

facial-expression-recognition icon facial-expression-recognition

Facial expression recognition aims to detect the persons feeling by using a video stream from the webcam. This detector can detect 7 human emotions which are Angry, Disgust, Fear, Happy, Neutral, Sad, Surprise

facial_expression_recognition icon facial_expression_recognition

A convolutional neural network (CNN) in Keras to recognize facial expressions. The data consists of 48x48 pixel grayscale images of faces. The faces are classified based on the emotion shown in the facial expression into one of seven categories (0=Angry, 1=Disgust, 2=Fear, 3=Happy, 4=Sad, 5=Surprise, 6=Neutral). OpenCV has been used to automatically detect faces in images and draw bounding boxes around them. A web interface is used to perform real-time facial expression recognition on video and image data.

facial_expression_recognition_6emo icon facial_expression_recognition_6emo

Classifies face based on emotion into Angry, Disgust, Fear, Happy, Sad, Surprise, Neutral. A web interface is used to perform real-time on image and video data.

forwardtacotron icon forwardtacotron

⏩ Generating speech in a single forward pass without any attention!

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