The River Rotte Fish Migration Project: A bayesian Network approach for fish habitat suitability

The political interest in fish habitat suitability and, consequently, of fish populations has increased. The fish habitat suitability is a key factor for successful ecological restoration, for example via dam removal and implementation of fish passage. Furthermore, the fish population composition determines the ecological quality of water bodies...

SoK: Explainable Machine Learning for Computer Security Applications

Explainable Artificial Intelligence (XAI) aims to improve the transparency of machine learning (ML) pipelines. We systematize the increasingly growing (but fragmented) microcosm of studies that develop and utilize XAI methods for defensive and offensive cybersecurity tasks. We identify 3 cybersecurity stakeholders, i.e., model users, designers,...

Differential Diagnosis and Molecular Stratification of Gastrointestinal Stromal Tumors on CT Images Using a Radiomics Approach

Treatment planning of gastrointestinal stromal tumors (GISTs) includes distinguishing GISTs from other intra-abdominal tumors and GISTs’ molecular analysis. The aim of this study was to evaluate radiomics for distinguishing GISTs from other intra-abdominal tumors, and in GISTs, predict the c-KIT, PDGFRA, BRAF mutational status, and mitotic...

Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction

Objective. Machine Learning methods can learn how to reconstruct magnetic resonance images (MRI) and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physics-informed networks incorporate the forward model of accelerated MRI reconstruction in the learning process. With increasing network complexity,...

Support vector regression for high-resolution beach surface moisture estimation from terrestrial LiDAR intensity data

Beach Surface Moisture (BSM) is a key attribute in the coastal investigations of land-atmospheric water and energy fluxes, groundwater resource budgets and coastal beach/dune development. In this study, an attempt has been made for the first time to estimate BSM from terrestrial LiDAR intensity data based on the Support Vector Regression (SVR...

Adversarially Robust Decision Trees Against User-Specified Threat Models

In the present day we use machine learning for sensitive tasks that require models to be both understandable and robust. Although traditional models such as decision trees are understandable, they suffer from adversarial attacks. When a decision tree is used to differentiate between a user's benign and malicious behavior, an adversarial attack...

CaptureBias: Supporting Media Scholars with Ambiguity-Aware Bias Representation for News Videos

In this project we explore the presence of ambiguity in textual and visual media and its influence on accurately understanding and<br/>capturing bias in news. We study this topic in the context of supporting<br/>media scholars and social scientists in their media analysis. Our focus<br/>lies on racial and gender bias as well as framing and the...

A Human in the Loop Approach to Capture Bias and Support Media Scientists in News Video Analysis

Bias is inevitable and inherent in any form of communication. News often appear biased to citizens with dierent political orientations, and understood dierently by news media scholars and the broader public. In this paper we advocate the need for accurate methods for bias identication in video news item, to enable rich analytics capabilities in...