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Interactive Distillation of Large Single-Topic Corpora of Scientific Papers

Highly specific datasets of scientific literature are important for both research and education. However, it is difficult to build such datasets at scale. A common approach is to build these datasets reductively by applying topic modeling on an …

MalwareDNA: Simultaneous Classification of Malware, Malware Families, and Novel Malware

Malware is one of the most dangerous and costly cyber threats to national security and a crucial factor in modern cyber-space. However, the adoption of machine learning (ML) based solutions against malware threats has been relatively slow. …

Malware-DNA: Machine Learning for Malware Analysis that Treats Malware as Mutations in the Software Genome

Malware is one of the most dangerous and costly cyber threats to organizations, the public, and national security, and a crucial factor in modern warfare. The adoption of ML-based solutions against malware threats has been relatively slow despite the …

Malware-DNA: Machine Learning for Malware Analysis that Treats Malwares as Mutations in the Genome of the Software

Sub-topic and Semantic Sub-structure Extraction via SPLIT: Joint Nonnegative Matrix Factorization (NMF) with Automatic Model Selection

Topic modeling is one of the key analytic techniques for organizing and analysis of large text corpora. One approach to topic modeling is the recently introduced SeNMFk, a method based on semantic non-negative matrix factorization (NMF) with …