Fachgruppe Informatik

Toward Minimalist HUDs? Longitudinal Trends and Player Perceptions in Action-Adventure and Action-RPG Games

Fri, 12 Jun 2026 00:00:00 GMT

Toward Minimalist HUDs? Longitudinal Trends and Player Perceptions in Action-Adventure and Action-RPG Games Meyer, Julian; Homsani, Faouzi Heads-up displays (HUDs) in contemporary action-adventure and action-RPG games are often described as becoming more minimalist, but evidence for this claim is largely anecdotal. Moreover, it remains unclear whether expert-coded HUD minimalism corresponds to how players actually perceive and evaluate game interfaces. To address this gap, we combine a longitudinal analysis of prominent commercial games with a clip-based perception study. We coded 30 Game of the Year nominees released between 2014 and 2025 using a four-dimensional framework of HUD minimalism capturing diegetic integration, persistence, density, and contextuality, and complemented this analysis with a remote within-subject study (N = 59) in which participants rated perceived minimalism, expected immersion, clarity, preference, and HUD reduction intention across gameplay clips.
Results show a clear upward trend in composite HUD minimalism over time: later games more often present information through diegetic and context-dependent means while reducing persistent and visually dense overlays. Player judgments closely tracked this distinction. Interfaces perceived as more minimalist were also associated with higher expected immersion and stronger preference, whereas clarity was driven more by familiarity than by perceived minimalism. Rather than indicating a simple disappearance of HUDs, these findings point to a broader design shift toward selectively surfaced, integrated, and state-dependent information presentation in prominent action games.

Co-ReaSON: EEG-based Onset Detection of Focal Epileptic Seizures with Multimodal Feature Representations

Wed, 01 May 2024 00:00:00 GMT

Co-ReaSON: EEG-based Onset Detection of Focal Epileptic Seizures with Multimodal Feature Representations Kumar, Uttam; Yu, Ran; Wenzel, Michael; Demidova, Elena Yang, De-Nian; Xie, Xing; Tseng, Vincent S.; Pei, Jian; Huang, Jen-Wei; Lin, Jerry Chun-Wei Early detection of an epileptic seizure's onset is crucial to reduce the impact of seizures on the patient's health. The Electroencephalogram (EEG) has been widely used in clinical epileptology for continuous, long-term measurement of electrical activity in the brain. Despite numerous EEG-based approaches employing diverse models and feature extraction methods for seizure detection, these methods rarely tackle the more challenging task of early detection of the seizure onset, especially as only a few EEG channels are impacted at the onset, and the seizure evidence is minimal. Furthermore, EEG-based seizure onset detection remains challenging due to the sparse, imbalanced, and noisy data, as well as the complexity posed by the diverse nature of epileptic seizures in patients. In this paper, we propose Co-ReaSON – a novel approach towards early detection of focal seizure onsets by considering the onset-specific increase in spatio-temporal correlations across the EEG channels observed over a range of multimodal EEG feature representations, combined in a ResNet18-based model architecture. Evaluation on a real-world dataset demonstrates that Co-ReaSON outperforms the state-of-the-art baselines in focal seizure onset detection by at least 5 percent points regarding the macro-average F1-score.

Leveraging Synthetically Generated Data for Real Estate Document Classification

Mon, 01 Dec 2025 00:00:00 GMT

Leveraging Synthetically Generated Data for Real Estate Document Classification Deußer, Tobias; Ramien, Gregor; Weber, Nico; Meidinger, Maximilian; Hahnbück, Max; Bauckhage, Christian; Sifa, Rafet Document classification in regulated domains like law, finance, or real estate is hindered by the scarcity of labeled data and strict privacy constraints. This paper presents a pipeline for synthetically generating training data for document classifiers using a combination of domain-specific templates, large language models, and data augmentation techniques. Focusing on two key document types relevant to real estate workflows, Child Support Certificate and Refurbishment Roadmap, we construct realistic multi-page documents and generate negative classes using LLM-generated distractors. We train a BERT-based classifier on this synthetic dataset and evaluate it on real-world OCR-extracted documents, achieving strong performance despite the absence of real documents in training. Our findings highlight the feasibility of using synthetic data to overcome annotation bottlenecks and pave the way for broader applications in privacy-sensitive industries.

A Survey on Current Trends and Recent Advances in Text Anonymization

Wed, 01 Oct 2025 00:00:00 GMT

A Survey on Current Trends and Recent Advances in Text Anonymization Deußer, Tobias; Sparrenberg, Lorenz; Berger, Armin; Hahnbück, Max; Bauckhage, Christian; Sifa, Rafet The proliferation of textual data containing sensitive personal information across various domains requires robust anonymization techniques to protect privacy and comply with regulations, while preserving data usability for diverse and crucial downstream tasks. This survey provides a comprehensive overview of current trends and recent advances in text anonymization techniques. We begin by discussing foundational approaches, primarily centered on Named Entity Recognition, before examining the transformative impact of Large Language Models, detailing their dual role as sophisticated anonymizers and potent de-anonymization threats. The survey further explores domain-specific challenges and tailored solutions in critical sectors such as healthcare, law, finance, and education. We investigate advanced methodologies incorporating formal privacy models and risk-aware frameworks, and address the specialized subfield of authorship anonymization. Additionally, we review evaluation frameworks, comprehensive metrics, benchmarks, and practical toolkits for real-world deployment of anonymization solutions. This review consolidates current knowledge, identifies emerging trends and persistent challenges, including the evolving privacy-utility trade-off, the need to address quasi-identifiers, and the implications of LLM capabilities, and aims to guide future research directions for both academics and practitioners in this field.