Two Six Labs, LLC

Chae A Clark

Robert Paul Gove, Jr

James Gentile

Violet Lingenfelter

Ben Gelman

Simon Cho

Anthony Wong

Reed Gordon-Sarney

An extractive summarization model provides extraction and classification of research assertions, or claims, made by a documented body of research such as a scientific paper or article. Modern electronic publication and dissemination allows tremendous capability for researching and scrutinizing previous documented efforts for further research and study. Accordingly, a substantial volume of material is easily obtained in response to research efforts. The extractive summarization model provides a summarization of this scientific literature by identifying and classifying asserted claims made by a particular research document. Researchers may quickly identify relevant documents based on the extracted claims asserted by the document, facilitating substantive review.

Multi-stage narrative analysis

A network disruption detection engine gathers and stores network routing path update messages that routinely modify routing tables used by internet routers for transporting message traffic between a source and a destination. Routing path messages continually maintain a network transport infrastructure both for intranets used for particular entities such as corporations, and internet traffic between arbitrary source and destination nodes. Major disruptions or outages typically result in an increase in routing path messages, typically focused on a particular set, region or network entity where the disruption occurred, as other routing entities seek to avoid the troubled region. Analysis of this sudden activity of routing messages and extracting message content about the network region they seek to avoid allows identification and queries of a widespread network outage.

Internet disruption detection

A machine learning approach for evaluating the replicability of research receives a scholarly paper or publication about a research effort, and applies learning models and natural language processing (NLP) to identify replicability of the asserted results. Rule based and machine learning approaches employ models based on preexisting research papers having defined replicability, and compares language features, research parameters and factual assertions extracted from a research paper for evaluation. A pipeline of evaluations and feature extraction analyzes the content of the research paper and renders a prediction value that the described research can be repeated with similar results.

Research replicability assessment

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Arlington, VA, United States of America

Chae A Clark