Computational Intelligence

Emergency  managers  of  today  grapple  with  post-hurricane damage assessment that is often labor-intensive, slow,costly,   and   error-prone.   As   an   important   first   step   towards addressing  the   challenge,   this   paper   presents   the   development of  benchmark  datasets  to  enable  the  automatic  detection  ofdamaged buildings from post-hurricane remote sensing imagerytaken  from  both  airborne  and  satellite  sensors.  Our  work  has two  major  contributions:  (1)  we  propose  a  scalable  framework to  create  benchmark  datasets  of  hurricane-damaged  buildings

The dataset was constructed by capturing real-time background traffic of 9 applications. The 9 applications represent different types of network behaviour in the background, for high level of network

This folder contains the code and datasets necessary for performing experiments on the Steimann and Defects4J coverage matrices detailed in "Doric: Foundations for Statistical Fault Localisation". See the README.txt inside the folder for further details.

Context: Multiple types of processing units (e.g., CPUs, GPUs and FPGAs) can be used jointly to achieve better performance in computational systems. However, these units are built with fundamentally different characteristics and demand attention especially towards software deployment.