Abstract
Hippocampal sclerosis (HS) is the most common cause of temporal lobe epilepsy (TLE) and can be identified in magnetic resonance imaging as hippocampal atrophy and subsequent volume loss. Detecting this kind of abnormalities through simple radiological assessment could be difficult, even for experienced radiologists. For that reason, hippocampal volumetry is generally used to support this kind of diagnosis. Manual volumetry is the traditional approach but it is time consuming and requires the physician to be familiar with neuroimaging software tools. In this paper, we propose an automated method, written as a script that uses FSL-FIRST, to perform hippocampal segmentation and compute an index to quantify hippocampi asymmetry (HAI). We compared the automated detection of HS (left or right) based on the HAI with the agreement of two experts in a group of 19 patients and 15 controls, achieving 84.2% sensitivity, 86.7% specificity and a Cohen's kappa coefficient of 0.704. The proposed method is integrated in the "Advanced Brain Imaging Lab" (ABrIL) cloud neurocomputing platform. The automated procedure is 77% (on average) faster to compute vs. the manual volumetry segmentation performed by an experienced physician.

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Abstract
This paper presents an architecture that supports Quality of Service (QoS) in an Arrowhead-compliant System of Systems (SoS). The Arrowhead Framework supports local cloud functionalities for automation applications, provided by means of a Service Oriented Architecture (SOA), by offering a number of services that ease application development. On such applications the QoS guarantees are required for service fruition, and are themselves requested as services from the framework. To fulfil this objective we start by describing the Arrowhead architecture and the components needed to dynamically in run-time negotiate a system configuration that guarantees the QoS requirements between application services.

Abstract
The blooming of different cloud data management infrastructures has turned multistore systems to a major topic in the nowadays cloud landscape. In this demonstration, we present a Cloud Multidatastore Query Language (CloudMdsQL), and its query engine. CloudMdsQL is a functional SQL-like language, capable of querying multiple heterogeneous data stores (relational and NoSQL) within a single query that may contain embedded invocations to each data store's native query interface. The major innovation is that a CloudMdsQL query can exploit the full power of local data stores, by simply allowing some local data store native queries (e.g. a breadth-first search query against a graph database) to be called as functions, and at the same time be optimized. Within our demonstration, we focus on two use cases each involving four diverse data stores (graph, document, relational, and key-value) with its corresponding CloudMdsQL queries. The query execution flows are visualized by an embedded real-time monitoring subsystem. The users can also try out different ad-hoc queries, not necessarily in the context of the use cases.

Abstract
Online recommender systems often deal with continuous, potentially fast and unbounded ows of data. Ensemble methods for recommender systems have been used in the past in batch algorithms, however they have never been studied with incremental algorithms, that are capable of processing those data streams on the y. We propose online bagging, using an incremental matrix factorization algorithm for positiveonly data streams. Using prequential evaluation, we show that bagging is able to improve accuracy more than 20% over the baseline with small computational overhead.