@inproceedings{7002, abstract = {Selecting an appropriate semantic segmentation model for a given application domain remains a challenging and time-consuming task for practitioners and researchers. This paper presents an interactive, web-based platform that enables side-by-side visual comparison of multiple neural network segmentation models applied to identical images. The system integrates three transformer-based segmentation models: a face-parsing network producing 19 semantic classes, a SegFormer-B3 clothing segmentation model with 18 classes, and a Mask2Former model for general-purpose scene segmentation spanning 150 ADE20K categories. Key contributions include side-by-side evaluation of model outputs across multiple architectures and image categories, with real-time segment highlighting and a scalable inference caching system that enables model comparisons without requiring repeated graphics processing unit (GPU) computation. The platform organizes a curated dataset of images under a hierarchical category taxonomy, supporting structured evaluation across demographic and contextual variables. As a practical use case, the system is applied within the ADRIAN project to assist in verifying identity consistency across images through segmentation-based analysis. The platform thus contributes a specialized artificial intelligence (AI) tool for systematic segmentation and object detection model evaluation within media analysis pipelines, where selecting appropriate models is a recurring challenge across tasks from identity verification to content moderation. It is available under https://github.com/vika-v-v/neural-networks-for-image-segmentation and designed to lower the barrier for comparative model evaluation in applied computer vision workflows.}, author = {Vovchenko, Viktoriia and Schultenkämper, Sergej and Bäumer, Frederik}, booktitle = {2026 The Second International Conference on AI-based Media Innovation}, keywords = {segmentation, model comparison, face parsing}, location = {Nizza, Frankreich}, publisher = {IARIA}, title = {{A Web-Based Platform for Interactive Comparison of Neural Network Image Segmentation Models}}, year = {2026}, } @inproceedings{6998, abstract = {Instruction-guided image editing models have made photorealistic facial manipulation accessible through natural language prompts, substantially lowering the barrier to misuse in the cybersecurity landscape. These systems facilitate attacks such as identity theft, phishing, misinformation, and social engineering, while also challenging existing forensic methods that were not designed for prompt-driven edits. However, current facial forensics datasets do not cover this manipulation paradigm. We present ADRIAN InstructFace-Edit, a dataset of 36,000 edited facial image pairs at 1024x1024 resolution generated by four state-of-the-art instruction-guided image editing models (FLUX.1-Kontext-dev, FLUX.2-dev, Qwen-Image-Edit-2511, and Step1X-Edit-v1.2) across six semantically distinct edit types, of which 18,768 are annotated as high quality. The full dataset, including all pairs with quality labels, is made publicly available. To improve data quality, we design a multi-stage filtering pipeline that integrates structural, semantic, and multimodal large language model–based quality checks, with thresholds derived empirically from a human-annotated subset of 1,500 images. Because open-weight vision-language models perform poorly on VIEScore evaluation out of the box, we fine-tune a LoRA adapter for Qwen3-VL-32B-Instruct that improves within +/-1 Semantic Consistency accuracy from near-random to 73.2%. We will publicly release the dataset, generation pipeline, and LoRA adapter to support future research on training, evaluating, and stress-testing detectors for instruction-guided facial manipulation.}, author = {Vovchenko, Viktoriia and Barberi, Vincenzo and Schultenkämper, Sergej and Bäumer, Frederik}, booktitle = {2026 The First International Conference on Security and Cybersecurity in the AI and Digital Context}, keywords = {Deepfakes, Image forensics, Diffusion models, Computer vision}, location = {Porto, Portugal}, title = {{ADRIAN InstructFace-Edit - Towards Robust Detection of AI-Manipulated Face Images}}, year = {2026}, } @misc{6993, author = {Kirsch, André and Rexilius, Jan}, publisher = {Hochschule Bielefeld}, title = {{Waste Bin Dataset }}, year = {2026}, } @inproceedings{6790, abstract = {Manual waste monitoring is a labor-intensive process that can lead to unnecessary trips and overflowing waste bins due to fixed inspection schedules. Existing automated systems rely on static sensors installed inside each bin, which are difficult to scale and have not yet gained wider acceptance. Micro aerial vehicles (MAVs) can address this problem by carrying the necessary sensors to different locations as needed. This paper presents an MAV-based solution for automated waste bin monitoring. RGB images captured by the MAV are processed by a CNN to estimate fill levels without the need for in-bin sensors. The CNN is trained on a custom dataset of simulated waste bins and evaluated with respect to dataset performance and in a real application scenario. A mobile application enables operators to configure bin locations and monitor the process in real time. The MAV is controlled by a reinforcement learning policy and autonomously navigates to each bin location. The evaluation demonstrates that the CNN achieves reliable performance on both simulated and real images, and that the integrated system autonomously completes full inspection cycles. Overall, the proposed system offers a scalable and cost-efficient alternative for sustainable waste management.}, author = {Kirsch, André and Rexilius, Jan}, booktitle = {22nd International Conference on Intelligent Environments (IE)}, keywords = {Waste monitoring, Waste level estimation, MAV navigation}, location = {Lissabon, Portugal}, title = {{Vision-Based Autonomous Waste Bin Fill-Level Monitoring with a Micro Aerial Vehicle}}, doi = {10.1109/IE69249.2026.11539031}, year = {2026}, } @inproceedings{6987, author = {Wermuth, Stella and Ahmed, Qazi Arbab and Neumann, Klaus and Jungeblut, Thorsten}, location = {Lecce, Italien}, publisher = {Arxiv}, title = {{PMOF: A Dataset and Benchmark for Passenger Monitoring Using Overhead Fisheye Cameras}}, doi = {10.48550/arXiv.2606.13910}, year = {2026}, } @inproceedings{6989, author = {Sadeghi-Kohan, Somayeh and Awais, Muhammad and Ahmed, Qazi Arbab and Platzner, Marco and Hellebrand, Sybille and Jungeblut, Thorsten and Wunderlich, Hans-Joachim}, booktitle = {2026 IEEE 29th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS)}, location = {Bratislava, Slovakia}, pages = {1--5}, publisher = {IEEE}, title = {{Reliability Assessment in Approximate Accelerator Synthesis}}, doi = {10.1109/DDECS69233.2026.11520985}, year = {2026}, } @article{6990, author = {Schröder, Nadine and Falke, Andreas and Endres, Herbert}, issn = {0167-9236}, journal = {Decision Support Systems}, publisher = {Elsevier BV}, title = {{Guiding decisions in empirical research: A precision-driven app with context-specific criteria for confirmatory factor analysis and structural equation modeling}}, doi = {10.1016/j.dss.2026.114700}, volume = {208}, year = {2026}, } @article{6994, author = {Beer, Christian and Frieling, Melanie and Verhofen, Verena}, issn = {1617-8084}, journal = {KoR - Zeitschrift für internationale und kapitalmarktorientierte Rechnungslegung }, keywords = {Immobilien Bilanzierung HGB IFRS}, number = {6}, pages = {242--250}, publisher = {Verlag Dr. Otto Schmidt}, title = {{Gegenüberstellung der bilanziellen Abbildung von Immobilienvermögen nach HGB und IFRS}}, volume = {26}, year = {2026}, } @inproceedings{6992, author = {Bünte, Ellen and Töpler, Niels and Bünte, Andreas}, booktitle = {PCIM Conference 2026}, isbn = {978-3-8007-6716-8}, location = {Nürnberg}, publisher = {VDE VERLAG GMBH}, title = {{Simulation-Based Evaluation of Structural Switching Methods for AC Machines in the Field-Weakening Range}}, doi = {10.30420/566716404}, year = {2026}, } @article{6986, abstract = {Background: Artificial intelligence–based skin cancer screening apps (AISCSAs) offer diagnostic potential but face limited adoption. App store cues, such as ratings, may influence acceptance; yet, little is known about how users cognitively process app store information in high-stakes health contexts. To address this gap, eye-tracking was used to measure visual attention while participants evaluated a mock AISCSA app store listing. Objective: This study aimed to test whether a single negative rating captures visual attention and whether an extended technology acceptance model (TAM) can predict behavioral intention to use (BI) AISCSAs. Methods: Participants (N=76) evaluated a mock app store listing for an AISCSA under positive (n=42) or negative (n=34) rating conditions while their eye movements were recorded. Analyses combined fixation durations in defined areas of interest (AOIs) with self-reported measures of perceived usefulness (PU), perceived ease of use (PEOU), trust, BI, willingness to pay, and the self-rated importance of app attributes. Results: Normalized fixation durations (seconds per square pixel) revealed the highest attention to the description (0.166 s/px2), followed by the reviews (0.11 s/px2) and the ratings (0.04 s/px2), while the price and the data protection received the least attention. Of the 5 self-rated app attributes, only reviews correlated positively with fixation durations on the reviews-AOI (r=0.28; P=.01). Rating valence had no significant effect on gaze patterns, PU, PEOU, trust, BI, or willingness to pay (all Ps>.05). However, PEOU (P=.001), PU (P<.001), and trust (P<.001) were significantly correlated with BI. Conclusions: Although the expected attentional capture effect of the negative rating was not observed, the weak or nonexistent associations between fixation durations on the AOIs and the self-rated importance of app attributes suggest that eye-tracking captures aspects of information processing that are not directly reflected in self-reported evaluations. These findings indicate that eye-tracking provides a more direct approximation of actual user behavior by revealing implicit attentional processes beyond what is captured by questionnaires. While the technology acceptance model constructs and trust predicted BI, rating valence alone did not affect acceptance or gaze behavior. In high-stakes health contexts, textual information may outweigh rating valence in driving adoption. Future research should explore conditions under which rating valence matters, including more extreme rating contrasts, variations in accompanying review texts, and the influence of individual differences such as preexisting attitudes toward artificial intelligence and levels of artificial intelligence literacy.}, author = {Jagemann, Inga and Hegner, Sabrina and Hirschfeld, Gerrit}, issn = {2292-9495}, journal = {JMIR Human Factors}, pages = {e93489--e93489}, publisher = {JMIR Publications Inc.}, title = {{The Role of Rating Valence in AI Skin Cancer App Acceptance: Eye-Tracking and Questionnaire Study}}, doi = {10.2196/93489}, volume = {13}, year = {2026}, } @inbook{6788, author = {Madeira Firmino, Nadine}, booktitle = {Kita und soziale Ungleichheit}, editor = {Meiner-Teubner, Christiane and Roland, Merten}, publisher = {Beltz Juventa}, title = {{Sprachliche Bildung und soziale Ungleichheit - Das Spannungsfeld zwischen Teilhabe und Selektion in der frühen Kindheit}}, year = {2026}, } @inproceedings{6982, author = {Riechmann-Thom, Malte and Rexilius, Jan}, booktitle = {IEEE International Conference on Robot and Human Interactive Communication (RO-MAN)}, location = {Kitakyushu}, title = {{Multi-Perspective AR Interaction Through Robot Viewpoint Control}}, year = {2026}, } @article{6980, author = {Gaerner, Maik and Silber, Robin and Schäffer, Malte and Hamrle, Jaroslav and Ehrmann, Andrea and Wortmann, Martin and Kuschel, Timo}, issn = {1077-3118}, journal = {Applied Physics Letters}, number = {22}, publisher = {AIP Publishing}, title = {{Cubic magneto-optic Kerr effect in Co(111) thin films}}, doi = {10.1063/5.0332728}, volume = {128}, year = {2026}, } @inproceedings{6823, author = {Lammert, Maike and Teschke, Karin and Bakker, Pia and Bernloehr, Annette}, booktitle = {Hebapäd; 3. Konferenz zur pädagogischen Arbeit im Hebammenstudium; Abstractband 03. Mai 2026}, keywords = {Pädagogik, Hebammenwissenschaft, Konzeption, Simulationsbasierte Trainings, Beckenendlage, Vierfüßlerstand, HSBI-All4s, Optimierung Geburtssimulator}, location = {Leipzig}, title = {{Maßgeschneiderte Lösungen für das Skills-Training: Optimierungsoptionen für den „PROMPT Flex Geburtssimulator“ und das vaginale Untersuchungsmodul}}, doi = {10.57720/6823}, year = {2026}, } @article{6936, abstract = {The data presented here derives from the Sustainable Working Conditions in Academia survey (STAIRCASE) on researcher mental health. The survey addresses the ongoing mental health crisis in academia by providing comprehensive, multilevel data on researcher well-being and its structural determinants. We employed a cross-sectional between-participant study design to collect data from 4,296 researchers between September 15, 2023, and August 26, 2024. The sample, which primarily includes researchers based in European countries, has a mean age of 38.1 years (SD = 10.7) and consists of 63.7% female participants. Participants provided data on key mental health outcomes – including depression, anxiety, stress, burnout, and well-being – alongside detailed assessments of working conditions, leadership behavior, and organizational characteristics. The dataset facilitates a holistic, multilevel investigation of academic mental health beyond individual risk factors by including the individual, leadership, institutional, and national context. By enabling analyses across hierarchical levels this dataset provides the necessary evidence to identify systemic drivers of mental (ill) health and inform the development of effective, system-wide strategies for meaningful change in academic work environments.}, author = {Lasser, Jana and Mol, Stefan T. and Čontala, Alja and Slavec, Ana and de Swarte, Andreja Zulim and Khachatryan, Anna and Eleuteri, Anna Maria and Haque, Anupoma and Jansone, Baiba and Vrenozi, Blerina and Cahill, Brian and Trenado, Carlos and Schwieren, Christiane and Iacob, Claudia Iuliana and Tejada-Gallardo, Claudia and Mairean, Cornelia and McCashin, Darragh and Chery, Deborah and Özel, Dilara and Stephen, Dimity and Mijakoski, Dragan and Ronda, Elena and Ricci, Eleonora and Ibrahimi, Eliana and Vita, Emese and Kamberi, Fatjona and Benavides, Fernando G. and Gonçalves, Francisco Valente and Kismihók, Gábor and Manich, Gemma Pascual and Esnaola, Igor and Portoghese, Igor and van der Weijden, Inge and Canu, Irina Guseva and Mehmeti, Irsida and Petrovic, Ivana B. and Šindelář, Jakub and Ferreira, João Miguel Alves and Gabrani, Jonila and Kovács, Karolina Eszter and Pöllänen, Katri and Geles, Konstantinos and Elkheir, Lamis Yahia Mohamed and Cudris-Torres, Lorena and Løvseth, Lise T. and Ioannidou, Louiza and Popovic, Luksa and Aljunaidy, Mais M. and Saboya, Maria Fatjó and Christensen, Marit and Santos, Marlene and Miklikowska, Marta and Paoli, Mateja Erce and Schroijen, Mathias and Fusi, Mathieu and Sundukova, Mayya and Kurtoğlu, Mete and Vukelić, Milica and Anghelache, Mirela Adriana and Adi, Mohamad Nadim and Barkçin, Mümine and Rocha, Nuno Barbosa and Pranjic, Nurka and Bogolyubova, Olga and Moreira, Paulo Alexandre Soares and Moreira, Paulo and Saugmann, Pil Maria and Tuval-Mashiach, Rivka and Mendes, Rui Amaral and Osmanovic, Sabina and Ozdede, Sinem and Lackner, Simone and Gauttier, Stéphanie and Rusu, Szidónia and Lagouri, Theodota and Yüce-Selvi, Ümran and Ziemiańczyk, Urszula and Dhamo, Xhilda}, issn = {2050-9863}, journal = {Journal of Open Psychology Data}, number = {1}, publisher = {Ubiquity Press, Ltd.}, title = {{Data from the Researcher Mental Health Observatory STAIRCASE Survey}}, doi = {10.5334/jopd.136}, volume = {14}, year = {2026}, } @inproceedings{6927, author = {Petersen, Mirko and Ballschmieter, Ingo and Kampe, Tim}, booktitle = {5th Bielefeld International Conference on Applied Business (BiCAB) on "Bridges for Impact: Linking Science, Economy, Politics, and Society", Bielefeld, 22.05.}, keywords = {open science, open innovation, third mission, university-industry collaboration}, location = {Bielefeld}, title = {{Bridging the Gap between Open Science and Open Innovation: Challenges of Universities’ Third Mission in Germany}}, year = {2026}, } @inbook{6974, author = {Özlü, Ismail and Pietsch, Severin and Helten, Svenja}, booktitle = {Nachhaltige RegionalGesundheit Ostwestfalen-Lippe. Bestandsaufnahme und Handlungsperspektiven. Edition: Nachhaltige Gesundheit in Stadt und Region/ Band 8. }, editor = {Hornberg, Claudia and Freymüller, Julius and Ritzinger, Silja and Fehr, Rainer }, isbn = {9783987265334}, pages = {308--314}, publisher = {Oekom}, title = {{Ansätze zur Verbesserung der (Primär-) Versorgung von Menschen mit chronischen Erkrankungen unter Einsatz von Advanced Practice Nurses in Ostwestfalen-Lippe}}, volume = {8}, year = {2026}, } @article{6961, author = {Lukassen, Fabian and Herrmann, Jan and Weisser, Christoph and Säfken, Benjamin and Kneib, Thomas}, journal = {Preprint}, publisher = {Arxiv}, title = {{From XAI to Stories: A Factorial Study of LLM-Generated Explanation Quality}}, doi = {10.48550/arXiv.2601.02224}, year = {2026}, } @article{6960, author = {Lukassen, Fabian and Weisser, Christoph and Schlee, Michael and Kumar, Manish and Thielmann, Anton and Säfken, Benjamin and Kneib, Thomas and Silbersdorff, Alexander}, journal = {Preprint}, publisher = {Arxiv}, title = {{LLM-Augmented Change Point Detection: A Methodological Framework for Ensemble Detection and Automated Explanation}}, doi = {10.48550/arXiv.2601.02957}, year = {2026}, } @article{6959, author = {Schlee, Michael and Kivimaki, Timo and Mashiku, Melchizedek and Weisser, Christoph and Säfken, Benjamin}, journal = {Preprint}, publisher = {Arxiv}, title = {{LabelFusion: Fusing Large Language Models With Transformer Encoders for Robust Financial News Classification}}, doi = {10.48550/arXiv.2512.10793}, year = {2026}, }