@article{BelchevaErmakovaFabian, author = {Belcheva, Veronika and Ermakova, Tatiana and Fabian, Benjamin}, title = {Understanding Website Privacy Policies—A Longitudinal Analysis Using Natural Language Processing}, series = {Information}, volume = {14}, journal = {Information}, number = {11}, editor = {Liu, Fei}, publisher = {MDPI}, issn = {2078-2489}, doi = {10.3390/info14110622}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:523-18227}, abstract = {Privacy policies are the main method for informing Internet users of how their data are collected and shared. This study aims to analyze the deficiencies of privacy policies in terms of readability, vague statements, and the use of pacifying phrases concerning privacy. This represents the undertaking of a step forward in the literature on this topic through a comprehensive analysis encompassing both time and website coverage. It characterizes trends across website categories, top-level domains, and popularity ranks. Furthermore, studying the development in the context of the General Data Protection Regulation (GDPR) offers insights into the impact of regulations on policy comprehensibility. The findings reveal a concerning trend: privacy policies have grown longer and more ambiguous, making it challenging for users to comprehend them. Notably, there is an increased proportion of vague statements, while clear statements have seen a decrease. Despite this, the study highlights a steady rise in the inclusion of reassuring statements aimed at alleviating readers' privacy concerns.}, subject = {Datenschutz}, language = {en} } @article{ZhangBelchevaErmakova, author = {Zhang, Weijie and Belcheva, Veronika and Ermakova, Tatiana}, title = {Interpretable Deep Learning for Diabetic Retinopathy: A Comparative Study of CNN, ViT, and Hybrid Architectures}, series = {Computers}, volume = {14}, journal = {Computers}, number = {5}, editor = {Chowdhury, Rafiqul}, publisher = {MDPI}, issn = {2073-431X}, doi = {10.3390/computers14050187}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:523-20853}, abstract = {Diabetic retinopathy (DR) is a leading cause of vision impairment worldwide, requiring early detection for effective treatment. Deep learning models have been widely used for automated DR classification, with Convolutional Neural Networks (CNNs) being the most established approach. Recently, Vision Transformers (ViTs) have shown promise, but a direct comparison of their performance and interpretability remains limited. Additionally, hybrid models that combine CNN and transformer-based architectures have not been extensively studied. This work systematically evaluates CNNs (ResNet-50), ViTs (Vision Transformer and SwinV2-Tiny), and hybrid models (Convolutional Vision Transformer, LeViT-256, and CvT-13) on DR classification using publicly available retinal image datasets. The models are assessed based on classification accuracy and interpretability, applying Grad-CAM and Attention-Rollout to analyze decision-making patterns. Results indicate that hybrid models outperform both standalone CNNs and ViTs, achieving a better balance between local feature extraction and global context awareness. The best-performing model (CvT-13) achieved a Quadratic Weighted Kappa (QWK) score of 0.84 and an AUC of 0.93 on the test set. Interpretability analysis shows that CNNs focus on fine-grained lesion details, while ViTs exhibit broader but less localized attention. These findings provide valuable insights for optimizing deep learning models in medical imaging, supporting the development of clinically viable AI-driven DR screening systems.}, subject = {Diabetic retinopathy}, language = {en} }