The current largest challenge in ML-based malware detection is maintaining high detection rates while samples evolve. Although multiple works have proposed drift detectors and retraining-aware pipelines that work with reasonable efficiency, none of these detectors and pipelines are currently explainable, which limits our understanding of the threats’ evolution and the detector’s efficiency. Despite previous works that presented taxonomies of concept drift events, no practical solution for explainable drift detection in malware pipelines existed until this work. Our insight to change this scenario is to split the classifier knowledge into two: (1) the knowledge about the frontier between Malware (M) and Goodware (G); and (2) the knowledge about the concept of the (M and G) classes. Thus, we can understand whether the concept or the classification frontier changed by measuring the variations in these two domains. We make this approach practical by deploying a pipeline with meta-classifiers to measure these sub-classes of the main malware detector. We demonstrate via 5K+ experiment runs the viability of our solution by (1) illustrating how it explains every drift point of the DREBIN and AndroZoo datasets and (2) how an explainable drift detector makes online retraining to achieve higher rates and requires fewer retraining points.

This paper audits damage labels derived from coincident satellite and drone aerial imagery for 15,814 buildings across Hurricanes Ian, Michael, and Harvey, finding 29.02\% label disagreement and significantly different distributions between the two sources, which presents risks and potential harms during the deployment of machine learning damage assessment systems. Currently, there is no known study of label agreement between drone and satellite imagery for building damage assessment. The only prior work that could be used to infer if such imagery-derived labels agree is limited by differing damage label schemas, misaligned building locations, and low data quantities. This work overcomes these limitations by comparing damage labels using the same damage label schemas and building locations from three hurricanes, with the 15,814 buildings representing 19.05 times more buildings considered than the most relevant prior work. The analysis finds satellite-derived labels significantly under-report damage by at least 20.43\% compared to drone-derived labels (p<1.2x10-117), and satellite- and drone-derived labels represent significantly different distributions (p<5.1x10-175). This indicates that computer vision and machine learning (CV/ML) models trained on at least one of these distributions will misrepresent actual conditions, as the differing satellite and drone-derived distributions cannot simultaneously represent the distribution of actual conditions in a scene. This potential misrepresentation poses ethical risks and potential societal harm if not managed. To reduce the risk of future societal harms, this paper offers four recommendations to improve reliability and transparency to decision-makers when deploying CV/ML damage assessment systems in practice.

Non-navigable rivers and retention ponds play important roles in buffering communities from flooding, yet emergency planners often have no data as to the volume of water that they can carry before flooding the surrounding. This paper describes a practical approach for using an uncrewed marine surface vehicle (USV) to collect and merge bathymetric maps with digital surface maps of the banks of shallow bodies of water into a unified volumetric model. The below-waterline mesh is developed by applying the Poisson surface reconstruction algorithm to the sparse sonar depth readings of the underwater surface. Dense above-waterline meshes of the banks are created using commercial structure from motion (SfM) packages. Merging is challenging for many reasons, the most significant is gaps in sensor coverage, i.e., the USV cannot collect sonar depth data or visually see sandy beaches leading to a bank thus the two meshes may not intersect. The approach is demonstrated on a Hydronalix EMILY USV with a Humminbird single beam echosounder and Teledyne FLIR camera at Lake ESTI at the Texas A&M Engineering Extension Service Disaster City complex.