76 citations · 311 across the 16 of their papers we have counts for
17 papers
The Frustrometer: Detecting User Frustration in Data Visualization Tasks using Biomarkers and Interaction Patterns
Johannes Ellemose, Sophia Wanner, Djordje Slijepčević +4
Visualization research has largely solved \textit{how} to help a stuck or frustrated user -- through interactive onboarding, contextual help, and active guidance. The unsolved prob…
Explanatory Interactive Machine Learning for Bias Mitigation in Visual Gender Classification
Nathanya Satriani, Djordje Slijepčević, Markus Schedl +1
Explanatory interactive learning (XIL) enables users to guide model training in machine learning (ML) by providing feedback on the model's explanations, thereby helping it to focus…
Distilling Knowledge from Large Language Models: A Concept Bottleneck Model for Hate and Counter Speech Recognition
Roberto Labadie-Tamayo, Djordje Slijepčević, Xihui Chen +4
The rapid increase in hate speech on social media has exposed an unprecedented impact on society, making automated methods for detecting such content important. Unlike prior black-…
FHSTP@EXIST 2025 Benchmark: Sexism Detection with Transparent Speech Concept Bottleneck Models
Roberto Labadie-Tamayo, Adrian Jaques Böck, Djordje Slijepčević +3
Sexism has become widespread on social media and in online conversation. To help address this issue, the fifth Sexism Identification in Social Networks (EXIST) challenge is initiat…
Machine Learning in Biomechanics: Key Applications and Limitations in Walking, Running, and Sports Movements
Carlo Dindorf, Fabian Horst, Djordje Slijepčević +5
This chapter provides an overview of recent and promising Machine Learning applications, i.e. pose estimation, feature estimation, event detection, data exploration & clustering, a…
Exploring the Plausibility of Hate and Counter Speech Detectors with Explainable AI
Adrian Jaques Böck, Djordje Slijepčević, Matthias Zeppelzauer
In this paper we investigate the explainability of transformer models and their plausibility for hate speech and counter speech detection. We compare representatives of four differ…
Explaining YOLO: Leveraging Grad-CAM to Explain Object Detections
Armin Kirchknopf, Djordje Slijepcevic, Ilkay Wunderlich +3
We investigate the problem of explainability for visual object detectors. Specifically, we demonstrate on the example of the YOLO object detector how to integrate Grad-CAM into the…
Explaining machine learning models for age classification in human gait analysis
Djordje Slijepcevic, Fabian Horst, Marvin Simak +7
Machine learning (ML) models have proven effective in classifying gait analysis data, e.g., binary classification of young vs. older adults. ML models, however, lack in providing h…
Explaining automated gender classification of human gait
Fabian Horst, Djordje Slijepcevic, Matthias Zeppelzauer +6
State-of-the-art machine learning (ML) models are highly effective in classifying gait analysis data, however, they lack in providing explanations for their predictions. This "blac…
Trustworthy Visual Analytics in Clinical Gait Analysis: A Case Study for Patients with Cerebral Palsy
Alexander Rind, Djordje Slijepčević, Matthias Zeppelzauer +3
Three-dimensional clinical gait analysis is essential for selecting optimal treatment interventions for patients with cerebral palsy (CP), but generates a large amount of time seri…
Automatic Sexism Detection with Multilingual Transformer Models
Mina Schütz, Jaqueline Boeck, Daria Liakhovets +7
Sexism has become an increasingly major problem on social networks during the last years. The first shared task on sEXism Identification in Social neTworks (EXIST) at IberLEF 2021…
Multimodal Detection of Information Disorder from Social Media
Armin Kirchknopf, Djordje Slijepcevic, Matthias Zeppelzauer
Social media is accompanied by an increasing proportion of content that provides fake information or misleading content, known as information disorder. In this paper, we study the…
Bounded logit attention: Learning to explain image classifiers
Thomas Baumhauer, Djordje Slijepcevic, Matthias Zeppelzauer
Explainable artificial intelligence is the attempt to elucidate the workings of systems too complex to be directly accessible to human cognition through suitable side-information r…
-Anonymity in Practice: How Generalisation and Suppression Affect Machine Learning Classifiers
Djordje Slijepčević, Maximilian Henzl, Lukas Daniel Klausner +3
The protection of private information is a crucial issue in data-driven research and business contexts. Typically, techniques like anonymisation or (selective) deletion are introdu…
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis
Djordje Slijepcevic, Fabian Horst, Sebastian Lapuschkin +6
Machine learning (ML) is increasingly used to support decision-making in the healthcare sector. While ML approaches provide promising results with regard to their classification pe…
Automatic Classification of Functional Gait Disorders
Djordje Slijepcevic, Matthias Zeppelzauer, Anna-Maria Gorgas +5
This article proposes a comprehensive investigation of the automatic classification of functional gait disorders based solely on ground reaction force (GRF) measurements. The aim o…
KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis
Markus Wagner, Djordje Slijepcevic, Brian Horsak +3
In 2014, more than 10 million people in the US were affected by an ambulatory disability. Thus, gait rehabilitation is a crucial part of health care systems. The quantification of…