Collective Classification for Social Media Credibility Estimation

We introduce a novel extension of the iterative classification algorithm to heterogeneous graphs and apply it to estimate credibility in social media. Given a heterogeneous graph of events, users, and websites derived from social media posts, and given prior knowledge of the credibility of a subset of graph nodes, the approach iteratively converges to a set of classifiers that estimate credibility of the remaining nodes. To measure the performance of this approach, we train on a set of manually labeled events extracted from a corpus of Twitter data and calculate the resulting receiver operating characteristic (ROC) curves. We show that collective classification outperforms independent classification approaches, implying that graph dependencies are crucial to estimating credibility in social media

XLab: Early Indications & Warnings from Open Source Data with Application to Biological Threat

XLab is an early warning system that addresses a broad range of national security threats using a flexible, rapidly reconfigurable architecture. XLab enables intelligence analysts to visualize, explore, and query a knowledge base constructed from multiple data sources, guided by subject matter expertise codified in threat model graphs. This paper describes a novel system prototype that addresses threats arising from biological weapons of mass destruction. The prototype applies knowledge extraction analytics-”including link estimation, entity disambiguation, and event detection-”to build a knowledge base of 40 million entities and 140 million relationships from open sources. Exact and inexact subgraph matching analytics enable analysts to search the knowledge base for instances of modeled threats. The paper introduces new methods for inexact matching that accommodate threat models with temporal and geospatial patterns. System performance is demonstrated using several simplified threat models and an embedded scenario

A Reverse Approach to Named Entity Extraction and Linking in Microposts

ABSTRACT In this paper, we present a pipeline for named entity extraction and linking that is designed specifically for noisy, grammatically inconsistent domains where traditional named entity techniques perform poorly. Our approach leverages a large knowledge base to improve entity recognition, while maintaining the use of traditional NER to identify mentions that are not co-referent with any entities in the knowledge base

Developing a Series of AI Challenges for the United States Department of the Air Force

Through a series of federal initiatives and orders, the U.S. Government has been making a concerted effort to ensure American leadership in AI. These broad strategy documents have influenced organizations such as the United States Department of the Air Force (DAF). The DAF-MIT AI Accelerator is an initiative between the DAF and MIT to bridge the gap between AI researchers and DAF mission requirements. Several projects supported by the DAF-MIT AI Accelerator are developing public challenge problems that address numerous Federal AI research priorities. These challenges target priorities by making large, AI-ready datasets publicly available, incentivizing open-source solutions, and creating a demand signal for dual use technologies that can stimulate further research. In this article, we describe these public challenges being developed and how their application contributes to scientific advances

Heat trace asymptotics for domains with singular boundaries

This dissertation consists of three main results regarding heat trace asymptotics for bounded domains with cusps. First, a refined asymptotic expansion for the Dirichlet heat trace θ(t) = Treᵗ(Δ)ᴰ on a planar domain with a single cusp is presented. First three terms appeared earlier in the physics literature. We prove them, together with logarithmic remainder estimate. Second, we obtain similar results for a family of three-dimensional solids of revolution with a cusp. Third, we calculate bounds for the Neumann heat trace Ψ(t) = Treᵗ(Δ)ᴺ on a planar region a cusp, which then allow us to conclude the first two terms in the asymptotic expansion of Ψ(t). For the upper bound, we use Golden-Thompson inequality. All the results for the Dirichlet heat trace and the calculation of the lower bound for the Neumann heat trace use Brownian motion

Heat Trace Asymptotics for Domains with Singular Boundaries

This paper presents three results regarding heat trace asymptotics for bounded domains with cusps. First, a refined asymptotic expansion for the Dirichlet heat trace \Theta(t) = Tre t\Delta D on a planar domain with a single cusp is presented. First three terms appeared earlier in the physics literature. They are proved here, together with logarithmic remainder estimate. Second, similar results are obtained for a family of threedimensional solids of revolution with a cusp. Third, bounds for the Neumann heat trace \Psi(t) = Tre t\Delta N on a planar region with a cusp are calculated, which then allow one to conclude the first two terms in the asymptotic expansion of \Psi(t). For the upper bound, Golden-Thompson inequality is used. All the results for the Dirichlet heat trace and the calculation of the lower bound for the Neumann heat trace use Brownian motion