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Python

Course material for course Data Evaluation with Python: Tools and Good Practice

Sample project for course Data Evaluation with Python: Tools and Good Practice

Coding dojo for python. Unit Tests, Best practices, ...

ANA f0 file I/O for python

Analysis of pole deflection time series of engineered human myocardium

A GUI based solver and analyzer for the game Peg Solitaire

Die 1. Python-Übung zur Vorlesung Mathematik für Studierende der Chemie II.

A simple graph viewer for analytical functions. It is able to parse mathematical expressions into expression objects, which can then be called as functions.

It shows graphs of the functions, which are automatically expanded as the user zooms or pans.

A time tracking application, you can define names, that each get a stop watch or a timer. You can then switch between them to determine which name's time is counting up or down - just like a chess clock, but with as many 'players' as you want, and the time counts either up or down. You can also save the current timers, start a new one and then load it again later.

OCR and evaluation for OCR-D

Analysis code for Federated Random Forest for Partially Non-Overlapping Data

A Quality of Life (QoL) library that accumulates usefull tools, hacks and such, too small for their own lib.

The TextAPI implementation for the EUPT project.

Python/Flask-based webservice that mediates between the MetaboSERV frontend and the two associated databases, MariaDB and Elasticsearch.

Here, Digital DNA is a way to encode social media activity. It is compressed as an entropy measure to detect social media bots. This repository documents the code I use in my Bachelor's thesis to replicate Pasricha's & Hayes' (2019) Digital DNA compression approach. After generating the Digital DNA string, it is compressed and used for supervised learning to tell bots and genuine users apart. Standard metrics are used for evaluation purposes. Firstly, I exactly replicate their approach using the original code and the MIB dataset, which includes genuine Twitter users as well as bots. Secondly, I conceptually replicate their approach on the Twibot-20 dataset in four test cases. (A more detailed description of what Digital DNA does and what I replicate how can be found in my thesis. Read the section "Research Motivation and Procedure" in Chapter 1 to get a first idea.) My findings are that the results of Pasricha & Hayes (2019) can be fully replicated and confirmed. Applying their approach to the newer Twibot-20 dataset does not provide satisfactory results. This suggests the Digital DNA compression approach tested is not a suitable detection method for advanced bots.

Renders the online bibliography for the EUPT project.

Data and scripts for our perspective on e-field-based TMS dosing.

This is an UNOFFICIAL python library to interact with the GWDG LLM Service.