A Lightweight Social Computing Approach to Emergency Management Policy Selection.

In order to select effective policies for emergency management in a timely manner, this paper proposes an agile and lightweight social computing approach to facilitating policy selection, evaluation, and adjustment relative to emergency management in both quantitative and qualitative ways. The approach consists of three components represented as PZE: 1) (P) emergency management policy selecting; 2) (Z) modeling artificial societies with the zombie-city model (a general and formal artificial society model); and 3) (E) policy evaluation. The formal specification of the zombie-city model and rigorous expressions of scenarios enable rigorous description and formal reasoning of an artificial society.

Automated Cognitive Health Assessment Using Smart Home Monitoring of Complex Tasks.

One of the many services that intelligent systems can provide is the automated assessment of resident well-being. We hypothesize that the functional health of individuals, or ability of individuals to perform activities independently without assistance, can be estimated by tracking their activities using smart home technologies. In this paper, we introduce a machine learning-based method for assessing activity quality in smart homes.

RE-PLAN: An Extensible Software Architecture to Facilitate Disaster Response Planning.

Computational tools are needed to make data-driven disaster mitigation planning accessible to planners and policymakers without the need for programming or GIS expertise. To address this problem, we have created modules to facilitate quantitative analyses pertinent to a variety of different disaster scenarios. These modules, which comprise the REsponse PLan ANalyzer (RE-PLAN) framework, may be used to create tools for specific disaster scenarios that allow planners to harness large amounts of disparate data and execute computational models through a point-and-click interface.