A modern, contested environment produces exponential amounts of data from a vast array of
multimodal sensory inputs for intelligence actively updating our situational awareness (SA) …

This paper presents a processing pipeline for fusingraw'and/or feature-level multi-sensor
data-upstream fusion-and initial results from this pipeline using imagery, radar, and radio …

In this position paper we argue for the role that Topological Data Modeling (TDM) principles
can play in providing a framework for sensor integration. While used successfully in …

In this paper, we present an end-to-end approach that employs machine learning
techniques and Ontology-based Bayesian Networks (BN) to characterize the behavior of …

Uncovering hidden relations in complex data sets is a key step to making sense of the data,
which is a hot topic in our era of data deluge. Graph-based representations are examples of …

The increasing availability of many sensing modalities (imagery, radar, radio frequency (RF)
signals, acoustical, and seismic data) reporting on the same phenomena introduces new …

Using the theory of affordance from perceptual psychology and through discussion of
literature within visual data mining and immersive analytics, a position for the multi-sensory …

Analyzing the impact of disastrous events has been central to understanding and
responding to crises. Traditionally, the assessment of disaster impact has primarily relied on …

With more than 4 billion mobile devices in the world today, mobile computing is quickly
becoming the universal computational platform of the world (P. Baudisch and C. Holz,“My …

Moving traces are essential data for target detection and associated behavior recognition.
Previous studies have used time–location sequences, route maps, or tracking videos to …