Institut für 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.

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.

Super-resolution time-resolved imaging using computational sensor fusion

Mon, 18 Jan 2021 00:00:00 GMT

Super-resolution time-resolved imaging using computational sensor fusion Callenberg, Clara; Lyons, Ashley; den Brok, Dennis; Fatima, Areeba; Turpin, Alex; Zickus, Vytautas; Machesky, Laura; Whitelaw, Jamie; Faccio, Daniele; Hulin, Matthias B. Imaging across both the full transverse spatial and temporal dimensions of a scene with high precision in all three coordinates is key to applications ranging from LIDAR to fluorescence lifetime imaging. However, compromises that sacrifice, for example, spatial resolution at the expense of temporal resolution are often required, in particular when the full 3-dimensional data cube is required in short acquisition times. We introduce a sensor fusion approach that combines data having low-spatial resolution but high temporal precision gathered with a single-photon-avalanche-diode (SPAD) array with data that has high spatial but no temporal resolution, such as that acquired with a standard CMOS camera. Our method, based on blurring the image on the SPAD array and computational sensor fusion, reconstructs time-resolved images at significantly higher spatial resolution than the SPAD input, upsampling numerical data by a factor 12 × 12 , and demonstrating up to 4 × 4 upsampling of experimental data. We demonstrate the technique for both LIDAR applications and FLIM of fluorescent cancer cells. This technique paves the way to high spatial resolution SPAD imaging or, equivalently, FLIM imaging with conventional microscopes at frame rates accelerated by more than an order of magnitude.