5 citations · 10 across the 9 of their papers we have counts for
9 papers
mstlo: Efficient Online Monitoring of Signal Temporal Logic
Andreas Kaag Thomsen, Niels Viggo Stark Madsen, Valdemar Tang Evans +3
We present mstlo (mistletoe), a Rust library for high-performance online monitoring of signal temporal logic (STL), with Python bindings. The library provides: (i) a unified interf…
Fixed-Persona SLMs with Modular Memory: Scalable NPC Dialogue on Consumer Hardware
Martin Braas, Lukas Esterle
Large Language Models (LLMs) have demonstrated remarkable capabilities in generating human-like text, yet their applicability to dialogue systems in computer games remains limited.…
Impact of Labeling Inaccuracy and Image Noise on Tooth Segmentation in Panoramic Radiographs using Federated, Centralized and Local Learning
Johan Andreas Balle Rubak, Khuram Naveed, Sanyam Jain +3
Objectives: Federated learning (FL) may mitigate privacy constraints, heterogeneous data quality, and inconsistent labeling in dental diagnostic AI. We compared FL with centralized…
Towards Graph-Based Privacy-Preserving Federated Learning: ModelNet -- A ResNet-based Model Classification Dataset
Abhisek Ray, Lukas Esterle
Federated Learning (FL) has emerged as a powerful paradigm for training machine learning models across distributed data sources while preserving data locality. However, the privacy…
Software Engineering for Self-Adaptive Robotics: A Research Agenda
Hassan Sartaj, Shaukat Ali, Ana Cavalcanti +6
Self-adaptive robotic systems operate autonomously in dynamic and uncertain environments, requiring robust real-time monitoring and adaptive behaviour. Unlike traditional robotic s…
Are LLM-based methods good enough for detecting unfair terms of service?
Mirgita Frasheri, Arian Bakhtiarnia, Lukas Esterle +1
Countless terms of service (ToS) are being signed everyday by users all over the world while interacting with all kinds of apps and websites. More often than not, these online cont…
fmiSwap: Run-time Swapping of Models for Co-simulation and Digital Twins
Henrik Ejersbo, Kenneth Lausdahl, Mirgita Frasheri +1
Digital Twins represent a new and disruptive technology, where digital replicas of (cyber)-physical systems operate for long periods of time alongside their (cyber)-physical counte…
Adaptive Parameterization of Deep Learning Models for Federated Learning
Morten From Elvebakken, Alexandros Iosifidis, Lukas Esterle
Federated Learning offers a way to train deep neural networks in a distributed fashion. While this addresses limitations related to distributed data, it incurs a communication over…
Analysis of the Effect of Low-Overhead Lossy Image Compression on the Performance of Visual Crowd Counting for Smart City Applications
Arian Bakhtiarnia, Błażej Leporowski, Lukas Esterle +1
Images and video frames captured by cameras placed throughout smart cities are often transmitted over the network to a server to be processed by deep neural networks for various ta…