As digital interfaces become increasingly prevalent, a series of ethical issues have surfaced, with dark patterns emerging as a key research focus. These manipulative design strategies are widely employed in User Interfaces (UI) with the primary aim of steering user behavior in favor of service providers, often at the expense of the users themselves. This paper aims to address three main challenges in the study of dark patterns: inconsistencies and incompleteness in classification, limitations of detection tools, and inadequacies in data comprehensiveness. In this paper, we introduce a comprehensive framework, called the Dark Pattern Analysis Framework (DPAF). Using this framework, we construct a comprehensive taxonomy of dark patterns, encompassing 64 types, each labeled with its impact on users and the likely scenarios in which it appears, validated through an industry survey. When assessing the capabilities of the detection tools and the completeness of the dataset, we find that of all dark patterns, the five detection tools can only identify 32, yielding a coverage rate of merely 50 % . Although the four existing datasets collectively contain 5,566 instances, they cover only 32 of all types of dark patterns, also resulting in a total coverage rate of 50 % . The results discussed above suggest that there is still significant room for advancement in the field of dark pattern detection. Through this research, we not only deepen our understanding of dark pattern classification and detection tools, but also offer valuable insights for future research and practice in this field.

The application of computer vision-powered large language models (LLMs) for medical image diagnosis has significantly advanced healthcare systems. Recent progress in developing symmetrical architectures has greatly impacted various medical imaging tasks. While CNNs or RNNs have demonstrated excellent performance, these architectures often face notable limitations of substantial losses in detailed information, such as requiring to capture global semantic information effectively and relying heavily on deep encoders and aggressive downsampling. This paper introduces a novel LLM-based Hybrid-Transformer Network (HybridTransNet) designed to encode tokenized Big Data patches with the transformer mechanism, which elegantly embeds multimodal data of varying sizes as token sequence inputs of LLMS. Subsequently, the network performs both inter-scale and intra-scale self-attention, processing data features through a transformer-based symmetric architecture with a refining module, which facilitates accurately recovering both local and global context information. Additionally, the output is refined using a novel fuzzy selector. Compared to other existing methods on two distinct datasets, the experimental findings and formal assessment demonstrate that our LLM-based HybridTransNet provides superior performance for brain tumor diagnosis in healthcare informatics.

Recent research on Polycystic Ovary Syndrome (PCOS) detection increasingly employs intelligent algorithms to assist gynecologists in more accurate and efficient diagnoses. However, intelligent PCOS detection faces notable challenges: absence of standardized feature taxonomies, limited research on available datasets, and insufficient understanding of existing detection tools' capabilities. This paper addresses these gaps by introducing a novel analytical framework for PCOS diagnostic research and developing a comprehensive taxonomy comprising 108 features across 8 categories. Furthermore, we analyzed available datasets and assessed current intelligent detection tools. Our findings reveal that 12 publicly accessible datasets cover only 54% of the 108 features identified in our taxonomy. These datasets frequently lack multimodal integration, regular updates, and clear license information—constraints that potentially limit detection tool development. Additionally, our analysis of 42 detection tools identifies several limitations: high computational resource requirements, inadequate multimodal data processing, insufficient longitudinal analysis capabilities, and limited clinical validation. Based on these observations, we highlight critical challenges and future research directions for advancing intelligent PCOS detection tools.

Rug pulls present a critical threat in Decentralized Finance (DeFi), causing substantial financial losses and eroding ecosystem trust. Despite research advances, effective detection remains hampered by fragmented taxonomies, limited datasets, and inadequate tool evaluations. Through systematic analysis of academic and industry sources, we develop a comprehensive taxonomy of 35 distinct rug pull types, including 9 previously undocumented variants. Our analysis reveals significant detection gaps: existing datasets cover only 20% of known types, leading us to create an enhanced dataset of 2,391 instances that increases coverage to 82.9%. Evaluation of 13 detection tools shows substantial capability variation (25.7% to 62.9%), with 9 types completely undetectable. Most critically, tool performance degrades significantly when facing complex attacks, with maximum detection rates dropping from 55.6% for single-vector cases to 31.3% for compound scenarios. These findings provide essential insights for developing more robust security testing approaches for smart contract vulnerabilities in decentralized systems.