Human Action Recognition (HAR) models are increasingly deployed in high-stakes environments, yet their fairness across different human appearances has not been analyzed. We introduce a framework for auditing bias in HAR models using synthetic video data, generated with full control over visual identity attributes such as skin color. Unlike prior work that focuses on static images or pose estimation, our approach preserves temporal consistency, allowing us to isolate and test how changes to a single attribute affect model predictions. Through controlled interventions using the BEDLAM simulation platform, we show whether some popular HAR models exhibit statistically significant biases on the skin color even when the motion remains identical. Our results highlight how models may encode unwanted visual associations, and we provide evidence of systematic errors across groups. This work contributes a framework for auditing HAR models and supports the development of more transparent, accountable systems in light of upcoming regulatory standards.

Weighted model counting (WMC) solvers play a key role in Bayesian inference applications, used for medical diagnosis [17] [16] and risk assessment [14]. Ongoing efforts to improve WMC solver developers aim to develop a fuzzer to identify bugs. This research is aimed at enhancing the quality of this fuzzer by developing an additional method to generate WMC instances, EXTREMEgen [21]. The functionality of this new approach relies on generating practical instances from Bayesian networks and breaking the solvers using extreme weights. Our empirical experiments show that EXTREMEgen exposes bugs in state-ofthe-art WMC solvers. The generated instances are solved fast enough to be usable in fuzzing. However, generation speed needs to be optimized to become practical for fuzzing. When optimized, EXTREMEgen could become the first generator of WMC instances specifically designed for fuzzing.

Model Counting solvers are critical in many domains. One way of validating them is through fuzzing. However, current fuzzing approaches lack systematic methods to evaluate how different test generators compare in bug-triggering behavior. This paper proposes three methods for evaluating fuzzer similarity: black-box analysis, differential testing, and white-box analysis.

A similarity metric was defined using differential testing, incorporating solver behavior, solve time, and count differences. A case study was conducted on three fuzzers across numerous configurations and four SOTA model counters, followed by an analysis of correlations between CNF feature distribution similarities and fuzzer behavioral similarities.

The results show moderate correlations between graph structure features (minimum variable node degrees) and fuzzer similarity, while clause balance features show negative correlations. However, the method proves inconclusive for selecting dissimilar fuzzers due to limited incorrect counts in our dataset, uncertainty about crash causes, and the similarity metric's inherent subjectivity. Further research is needed, either by expanding the scope of the experiment with more diverse fuzzers, CNF features, and counters, or by pursuing another method recommended by this study.

Propositional model counting (#SAT) is the counting variant of the Boolean Satisfiability (SAT) problem. Development of #SAT solvers has seen a boom in recent years. These tools are complex and hard to debug. To address this, we propose a delta debugger that reduces fault-triggering unweighted model counting instances. Our delta debugger shows an improvement compared to state of the art in the related field of SAT solvers.

Horizontal gene transfer (HGT) trough plasmids is one of the main contributors to the rapid increase of antimicrobial resistance (AMR). Studying wastewater from wastewater treatment plants (WWTPs) allows us new insights into HGT as bacteria from different sources come together. Currently the analysis of HGT is limited, as plasmids cannot be linked to their host species with only metagenomic samples, however when combined with Hi-C sequencing data, sequences from the same cell can be linked together.
We developed a method that uses metagenomic Hi-C data to link bacterial genera together with detected plasmid consensus clusters and resistance genes. Using this method, we analysed datasets from two sources: activated sludge put into a reactor with an antibiotic pressure and a WWTP entrance. The activated sludge dataset was sequenced at two timepoints with an increasing antibiotic concentration. This allowed us to compare degrees of antibiotic resistance in different antibiotic pressures as well as detect broad host-range resistant plasmids. We detected an increase in acquired resistance in environments with a higher antibiotic pressure and detected a resistant plasmid in both locations, linked to both pathogenic as well as bacteria found in active sludge.