Chuqiao Gu

Sharmishtha Dutta, Nidhi Rastogi, Destin Yee et al.

Cyber threat and attack intelligence information are available in non-standard format from heterogeneous sources. Comprehending them and utilizing them for threat intelligence extraction requires engaging security experts. Knowledge graphs enable converting this unstructured information from heterogeneous sources into a structured representation of data and factual knowledge for several downstream tasks such as predicting missing information and future threat trends. Existing large-scale knowledge graphs mainly focus on general classes of entities and relationships between them. Open-source knowledge graphs for the security domain do not exist. To fill this gap, we've built \textsf{TINKER} - a knowledge graph for threat intelligence (\textbf{T}hreat \textbf{IN}telligence \textbf{K}nowl\textbf{E}dge g\textbf{R}aph). \textsf{TINKER} is generated using RDF triples describing entities and relations from tokenized unstructured natural language text from 83 threat reports published between 2006-2021. We built \textsf{TINKER} using classes and properties defined by open-source malware ontology and using hand-annotated RDF triples. We also discuss ongoing research and challenges faced while creating \textsf{TINKER}.

Wuyang Zhang, Zhen Luo, Chuqiao Gu et al.

Automated EEG monitoring requires clinician-level precision for seizure detection and reporting. Clinical EEG recordings exceed LLM context windows, requiring extreme compression (400:1+ ratios) that destroys fine-grained temporal precision. A 0.5 Hz error distinguishes absence epilepsy from Lennox-Gastaut syndrome. LLMs lack inherent time-series comprehension and rely on statistical associations from compressed representations. This dual limitation causes systems to hallucinate clinically incorrect measurement values. We separate measurement extraction from text generation. Our hybrid architecture computes exact clinical values via signal processing before compression, employs a cross-modal bridge for EEG-to-language translation, and uses parameter-efficient fine-tuning with constrained decoding around frozen slots. Multirate sampling maintains long-range context while preserving event-level precision. Evaluation on TUH and CHB-MIT datasets achieves 60% fewer false alarms, 50% faster detection, and sub-clinical measurement precision. This is the first system guaranteeing clinical measurement accuracy in automated EEG reports.