Among the modalities of medical data, the clinical summaries have higher risks to be attacked because they are generated by third-party companies.
This ontology forms the basis for the malware threat intelligence knowledge graph, MalKG, which we exemplify using three different, non-overlapping demonstrations.
Backdoor attacks mislead machine-learning models to output an attacker-specified class when presented a specific trigger at test time.
We propose a novel supervised open information extraction (Open IE) framework that leverages an ensemble of unsupervised Open IE systems and a small amount of labeled data to improve system performance.
We describe the systems developed by the UMBC team for 2018 SemEval Task 8, SecureNLP (Semantic Extraction from CybersecUrity REports using Natural Language Processing).
In this pa-per, we describe a novel method to train domain-specificword embeddings from sparse texts.
To overcome this limitation, we present Distributed Infinite Tucker (DINTUCKER), a large-scale nonlinear tensor decomposition algorithm on MAPREDUCE.