Robustness-Driven Resilience Evaluation of Self-Adaptive Software Systems

An increasingly important requirement for certain classes of software-intensive systems is the ability to self-adapt their structure and behavior at run-time when reacting to changes that may occur to the system, its environment, or its goals. A major challenge related to self-adaptive software systems is the ability to provide assurances of their resilience when facing changes. Since in these systems, the components that act as controllers of a target system incorporate highly complex software, there is the need to analyze the impact that controller failures might have on the services delivered by the system. In this paper, we present a novel approach for evaluating the resilience of self-adaptive software systems by applying robustness testing techniques to the controller to uncover failures that can affect system resilience. The approach for evaluating resilience, which is based on probabilistic model checking, quantifies the probability of satisfaction of system properties when the target system is subject to controller failures. The feasibility of the proposed approach is evaluated in the context of an industrial middleware system used to monitor and manage highly populated networks of devices, which was implemented using the Rainbow framework for architecture-based self-adaptation

EEG source connectivity to localize the seizure onset zone in patients with drug resistant epilepsy

Visual inspection of the EEG to determine the seizure onset zone (SOZ) in the context of the presurgical evaluation in epilepsy is time-consuming and often challenging or impossible. We offer an approach that uses EEG source imaging (ESI) in combination with functional connectivity analysis (FC) to localize the SOZ from ictal EEG. Ictal low-density-scalp EEG from 111 seizures in 27 patients who were rendered-seizure free after surgery was analyzed. For every seizure, ESI (LORETA) was applied on an artifact-free epoch selected around the seizure onset. Additionally, FC was applied on the reconstructed sources. We estimated the SOZ in two ways: (i)the source with highest power after ESI and (ii)the source with the most outgoing connections after ESI and FC. For both approaches, the distance between the estimated SOZ and the resected zone (RZ) of the patient were calculated. Using ESI alone, the SOZ was estimated inside the RZ in 31% of the seizures and within 10mm from the border of the RZ in 42%. For 18.5% of the patients, all seizures were estimated within 10mm of the RZ. Using ESI and FC, 72% of the seizures were estimated inside the RZ, and 94% within 10mm. For 85% of the patients, all seizures were estimated within 10mm of the RZ. FC provided a significant added value to ESI alone (p<0.001). ESI combined with subsequent FC is able to localize the SOZ in a non-invasive way with high accuracy. Therefore it could be a valuable tool in the presurgical evaluation of epilepsy