Abstract
This study intends to provide an insight on the possibilities of detecting structural damage through the analysis of the measured structural response of a cable-stayed bridge to daily and seasonal temperature variations. The Corgo Bridge, where a continuous monitoring system has been installed and is acquiring data for more than one year, is selected as case study. The characterization of the thermal action in its three components (uniform, linear and non-linear) is performed using a finite element thermal analysis in which the boundary conditions are defined using the measured wind velocity, radiation, ambient temperature and air temperature inside the box-girder. Once the calculated concrete temperatures fit well those measured by the embedded temperature sensors, the corresponding time histories of the uniform and differential temperature components are applied to a mechanical finite element model to obtain the structural response of the bridge. The results of the numerical simulations are compared with one year and half of experimental measurements and reasonable agreement is found. Finally, two damage scenarios are simulated involving small decreases in stiffness of the stay cables. The ability of detecting damage using the structural response to thermal loads is evaluated applying Multilinear Regression Analysis (MLR) and Principal Component Analysis (PCA) to the simulated data sets. Finally, some conclusions are put forward regarding the feasibility of early damage detection in the selected cable-stayed bridge using the adopted methodology and the installed monitoring system.

Abstract

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. Copyright is held by the owner/author(s).

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.

Abstract
A great variety of human gesture recognition methods exist in the literature, yet there is still a lack of solutions to encompass some of the challenges imposed by real life scenarios. In this document, a gesture recognition for robotic search and rescue missions in the high seas is presented. Themethod aims to identify shipwrecked people by recognizing the hand waving gesture sign. We introduce a novelmotion descriptor, through which high recognition accuracy can be achieved even for low resolution images. The method can be simultaneously applied to rigid object characterization, hence object and gesture recognition can be performed simultaneously. The descriptor has a simple implementation and is invariant to scale and gesture speed. Tests, preformed on a maritime dataset of thermal images, proved the descriptor ability to reach a meaningful representation for very low resolution objects. Recognition rates with 96.3% of accuracy were achieved.

Abstract
Travel on Virtual Environments is the simple action where a user moves from a starting point A to a target point B. Choosing an incorrect type of technique could compromise the Virtual Reality experience and cause side effects such as spatial disorientation, fatigue and cybersickness. The design of effective travelling techniques demands to be as natural as possible, thus real walking techniques presents better results, despite their physical limitations. Approaches to surpass these limitations employ techniques that provide an indirect travel metaphor such as point-steering and target-based. In fact, target-based techniques evince a reduction in fatigue and cybersickness against the point-steering techniques, even though providing less control. In this paper we investigate further effects of speed and transition on target-based techniques on factors such as comfort and cybersickness using a Head-Mounted Display setup.