Rapid Trust Calibration through Interpretable and Uncertainty-Aware AI.

Artificial intelligence (AI) systems hold great promise as decision-support tools, but we must be able to identify and understand their inevitable mistakes if they are to fulfill this potential. This is particularly true in domains where the decisions are high-stakes, such as law, medicine, and the military. In this Perspective, we describe the particular challenges for AI decision support posed in military coalition operations.

HONEM: Learning Embedding for Higher Order Networks.

Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and, as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network, that is, the network that only captures the pairwise interactions between the nodes. As a result, these methods may fail to incorporate non-Markovian higher order dependencies in the network.

AspEm: Embedding Learning by Aspects in Heterogeneous Information Networks.

Heterogeneous information networks (HINs) are ubiquitous in real-world applications. Due to the heterogeneity in HINs, the typed edges may not fully align with each other. In order to capture the semantic subtlety, we propose the concept of aspects with each aspect being a unit representing one underlying semantic facet.

Embedding Learning with Events in Heterogeneous Information Networks.

In real-world applications, objects of multiple types are interconnected, forming . In such heterogeneous information networks, we make the key observation that many interactions happen due to some and the objects in each event form a complete semantic unit. By taking advantage of such a property, we propose a generic framework called - (Hebe) to learn object embeddings with events in heterogeneous information networks, where a encompasses the objects participating in one event.

Graph Regularized Meta-path Based Transductive Regression in Heterogeneous Information Network.

A number of real-world networks are heterogeneous information networks, which are composed of different types of nodes and links. Numerical prediction in heterogeneous information networks is a challenging but significant area because network based information for unlabeled objects is usually limited to make precise estimations. In this paper, we consider a graph regularized meta-path based transductive regression model (), which combines the principal philosophies of typical graph-based transductive classification methods and transductive regression models designed for homogeneous networks.

Diffuse prior monotonic likelihood ratio test for evaluation of fused image quality measures.

This paper introduces a novel method to score how well proposed fused image quality measures (FIQMs) indicate the effectiveness of humans to detect targets in fused imagery. The human detection performance is measured via human perception experiments. A good FIQM should relate to perception results in a monotonic fashion.