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As mobile applications become increasingly complex and privacy regulations continue to evolve, the task of accurately identifying app violations in compliance detection has become a major challenge. Prior works mainly relied on taint analysis and dynamic monitoring to address this issue. However, taint analysis requires specifying sources and sinks for each violation, leading to multiple analysis rounds and inefficiency. Meanwhile, dynamic monitoring suffers from incomplete coverage, resulting in high false negatives. In this paper, we propose a novel graph structure, called Behavior Property Graph (BPG), for detecting non-compliant behaviors in Android applications. BPG integrates the features from various graph representations, including Abstract Syntax Tree (AST), Control Flow Graph (CFG), Call Graph (CG), Program Dependency Graph (PDG), and Pointer Assignment Graph (PAG), enabling comprehensive modeling of complex app behaviors. Violations are identified by querying the BPG using behavioral patterns extracted from real-world apps. We develop a prototype system called BPGᴇɴ to generate BPGs and evaluated its performance by testing seven types of non-compliant behaviors on a dataset of 200 real-world apps. Notably, BPGᴇɴ detects 14 violations within 13 previously unreported non-compliant applications. The results show that BPGᴇɴ can efficiently and effectively detect app compliance violations.

Topological data analysis (TDA) has exploded as a tool for analyzing and making sense of high dimensional datasets across a variety of fields. Mapper is a tool from TDA that captures low-dimensional structure from high-dimensional data, precisely the approach needed to capture relevant information from high-dimensional neural time series. Electrical potential scalp recording, or electroencephalography (EEG), is routinely used in clinical applications and research studies thanks to its noninvasive nature, relatively inexpensive equipment, and high temporal resolution. But, it is prone to contamination, exhibits low spatial resolution, and has a non-stationary nature. Thus, it requires advanced signal processing and mathematical analysis methods for tasks requiring unsupervised brain state clustering. We introduce MapperEEG, an approach to unsupervised brain state clustering that uses tools from classical EEG analysis combined with Mapper to cluster and connect brain states. We show that MapperEEG can serve as a clustering algorithm in the spectral domain and provide additional information about the underlying brain state connectivity in a tapping task. Additionally, we use a go/no-go shooting task to explore how MapperEEG can still provide insight into the underlying structure and clusters of brain states even when it and other clustering methods fail. We demonstrate that it outperforms six other clustering algorithms such as hierarchical clustering, Hidden Markov Models, and basic autoencoders on identifying states in a tapping task. MapperEEG offers a novel and effective approach to analyzing EEG data, showing promise for brain state clustering and analysis.

help optimize medical and surgical decision-making for posterior cervical spinal fusion patients on GLP-1 agonists.

• TGD individuals face healthcare trade-offs due to systemic barriers. • Trade-offs include sacrificing geographic convenience, preferred care, affordability, and high-quality care pathways. • Structural constraints, not individual choices, drive healthcare trade-offs among TDG patients. • Findings call for policy reforms in insurance, training, and telehealth access. • Study reframes trade-offs as outcomes of structural neglect, not resilience.