1 to 10 of 31 Results
Apr 28, 2025 - KR-Building (EXC IntCDC)
Pan, Xinyi; Hernández, Daniel; Seifer, Philipp; Lämmel, Ralf; Staab, Steffen, 2025, "eSPARQL Implementation", https://doi.org/10.18419/DARUS-4344, DaRUS, V1, UNF:6:t0V66ThHSAjSCe2jRbbJng== [fileUNF]
This project contains the code of an implementation of the eSPARQL language, which extends SPARQL-star with a `FROM BELIEF` that allows an easy formulation of epistemic queries. Further information can be found in the README.md file. |
Feb 28, 2025 - Analytic Computing
Fathallah, Nadeen, 2025, "Code for Improving Video Caption Accuracy with LLMs", https://doi.org/10.18419/DARUS-4776, DaRUS, V1
As part of the IKILeUS project at the University of Stuttgart, research was conducted to explore how Large Language Models (LLMs) can enhance the accuracy and contextual relevance of automatic speech recognition (ASR)-generated captions. While ASR tools provide a foundation for accessibility, they often produce grammatical errors, misinterpret homo... |
Feb 28, 2025 - Analytic Computing
Fathallah, Nadeen; Staab, Steffen, 2025, "Code for Caption Crowd (IKILeUS)", https://doi.org/10.18419/DARUS-4775, DaRUS, V1, UNF:6:e9mxpfNAwwwZlt8Uc4I+mQ== [fileUNF]
CaptionCrowd is an interactive platform developed within the IKILeUS project at the University of Stuttgart to improve video caption accuracy for the Deaf and Hard of Hearing (DHH) community. While automatic captions provide some accessibility, they often contain errors in grammar, homophones, and domain-specific terminology, making comprehension c... |
Feb 28, 2025 - Analytic Computing
Fathallah, Nadeen; Staab, Steffen, 2025, "Code for EchoTables (IKILeUS)", https://doi.org/10.18419/DARUS-4774, DaRUS, V1
EchoTables is an innovative accessibility tool developed as part of the IKILeUS project at the University of Stuttgart. It is designed to improve the usability of tabular data for visually impaired users by converting structured tables into concise, auditory-friendly textual summaries. Traditional screen readers navigate tables linearly, which impo... |
Feb 26, 2025 - InMotion (EXC)
Frank, Daniel, 2025, "Code for statesim - a python package to simulate dynamical systems from ordinary differential equations.", https://doi.org/10.18419/DARUS-4769, DaRUS, V1, UNF:6:afs/qXtsE2p0ZWIJY23a/A== [fileUNF]
Python package for simulating ordinary differential equations in state space form. See README.md for details. |
Feb 26, 2025 - InMotion (EXC)
Frank, Daniel, 2025, "Damped pendulum for nonlinear system identification - inputs are sampled from a multivariate-normal distribution - synthetically generated", https://doi.org/10.18419/DARUS-4770, DaRUS, V1
Overview This dataset contains input-output data of a damped nonlinear pendulum that is actuated at the mounting point. The data was generated with statesim [1], a python package for simulating linear and nonlinear ODEs, for the system actuated pendulum. The configuration .json files for the corresponding datasets (in-distribution and out-of-distri... |
Feb 26, 2025 - InMotion (EXC)
Frank, Daniel, 2025, "Coupled mass-spring-damper system for nonlinear system identification - actuated with random static inputs - synthetically generated", https://doi.org/10.18419/DARUS-4768, DaRUS, V1
Overview This dataset contains input-output data of a coupled mass-spring-damper system with a nonlinear force profile. The data was generated with statesim [1], a python package for simulating linear and nonlinear ODEs, for the system coupled-msd. The configuration .json files for the corresponding datasets (in-distribution and out-of-distribution... |
Feb 13, 2025Analytic Computing
Project Website: https://www.ki.uni-stuttgart.de/departments/ac/research/projects/inmotion/ |
Feb 13, 2025Analytic Computing
Project Website: https://www.ki.uni-stuttgart.de/departments/ac/research/projects/ikileus/ |
Jan 8, 2025 - KR-Building (EXC IntCDC)
Glaser, Gabriel Timon, 2025, "Knowledge Graph Generator", https://doi.org/10.18419/DARUS-4436, DaRUS, V1
Code and experiment results for a synthetic knowledge graph generator. The generator receives a set of rules, with an expected body support and support, and returns a knowledge graph that approximately matches the rules according to the body support and confidence. This code was developed during the Bachelor thesis by Gabriel Glaser, Generating Ran... |