Find code of the ForgetIT Project and contribute if you like!

Preserve-or-Forget Middleware

The Middleware implements a linking layer between an information system and a preservatoin system and provides services that realize the concept of managed forgetting, contextualied remembering and synergetic presrvation. In summary, it makes preservation technology more inteligent and easier to use:


For more detail check out the documenttion in deliverable D8.6.


PHP CMIS Client is a port of OpenCMIS (Java) to PHP. Interfaces are mostly the same so most OpenCMIS examples should be also usable for this PHP CMIS Library.
Some basic examples can be found in the example folder (code is not nice but shows how it works).
The functionality is not complete yet but still under development.


Docker TYPO3 CMS. Out-of-the-box TYPO3 docker image which can be linked to MySQL.


There is also a docker image with Alfresco ready:


TYPO3 CMS Extensions

This set of TYPO3 CMS Extensions will enable you to use the ForgetIT Framework with TYPO3 CMS. 6.2.x. Currently under development, there will be a public demo installation availaible to test core functions later this year.

typo3-ext-contentdashboard https://github.com/dkd/typo3-ext-contentdashboard
typo3-ext-cmis-fal https://github.com/dkd/typo3-ext-cmis-fal
typo3-ext-cmis-service https://github.com/dkd/typo3-ext-cmis-service
typo3-ext-cmis-client https://github.com/dkd/typo3-ext-cmis-client

You can find a Blogpost on how to use the CMIS FAL Extension here:




Image Aeasthetic Quality Assessment Tool

This is a Matlab implementation of the feature extraction process for our Image Aesthetic Quality assessment method. Each image is represented according to a set of photographic rules, and five feature vectors are extracted, describing the image's simplicity, colorfulness, sharpness, pattern and composition.


Related publications [icip15]

Accelerated Kernel Subclass Discriminant Analysis

AKSDA is a new GPU-accelerated, state-of-the-art C++ library (also provided as command-line executable) for supervised dimensionality reduction and classification, using multiple kernels. It greatly reduces the dimensionality of the input data, while at the same time it increases their linear separability. Used in conjunction with linear SVMs, it achieves state-of-the-art classification results, consistently higher than Kernel SVM approaches, at orders-of-magnitude shorter training times.




Related publications [mm15] (see also [tnnls13][spl11] for more theoretical foundations)

Other components

More components will be added to this page once they can be shared with the public.



Our Partners

  • Deutsches Forschungszentrum für Künstliche Intelligenz GmbH
  • Turk Telekomunikasyon AS
  • The Chancellor, Masters and Scholars of the University of Oxford
  • IBM Israel - Science and Technology Ltd
  • Centre for Research and Technology Hellas
  • dkd Internet Service GmbH
  • The University of Sheffield
  • Gottfried Wilhelm Leibniz Universität Hannover
  • The University of Edinburgh
  • Luleå Tekniska Universitet
  • Eurix Srl