Abstract
In privacy-preserving processing of outsourced data a Cloud server stores data provided by one or multiple data providers and then is asked to compute several functions over it. We propose an efficient methodology that solves this problem with the guarantee that a honest-but-curious Cloud learns no information about the data and the receiver learns nothing more than the results. Our main contribution is the proposal and efficient instantiation of a new cryptographic primitive called Labeled Homomorphic Encryption (labHE). The fundamental insight underlying this new primitive is that homomorphic computation can be significantly accelerated whenever the program that is being computed over the encrypted data is known to the decrypter and is not secret—previous approaches to homomorphic encryption do not allow for such a trade-off. Our realization and implementation of labHE targets computations that can be described by degree-two multivariate polynomials. As an application, we consider privacy preserving Genetic Association Studies (GAS), which require computing risk estimates from features in the human genome. Our approach allows performing GAS efficiently, non interactively and without compromising neither the privacy of patients nor potential intellectual property of test laboratories.

Abstract
Cyber-physical systems (CPS) are a new generation of systems that integrate computation and physical processes interacting with humans in different ways. Integrated networks of computers, sensors and similar technologies monitor and control the physical processes, reporting relevant data to planners and decision-makers, and vice versa. By means of case research, this paper analyzes the implementation of cyber-physical systems aiming at lead-time reduction in two manufacturing contexts, namely footwear and natural cork stoppers. The results of this research contribute to literature and practice with a conceptual framework for the implementation of cyber-physical systems and the discussion of the challenges of implementing this technology.

Abstract
Sign Language Recognition (SLR) has becoming one of the most important research areas in the field of human computer interaction. SLR systems are meant to automatically translate sign language into text or speech, in order to reduce the communicational gap between deaf and hearing people. The aim of this paper is to exploit multimodal learning techniques for an accurate SLR, making use of data provided by Kinect and Leap Motion. In this regard, single-modality approaches as well as different multimodal methods, mainly based on convolutional neural networks, are proposed. Experimental results demonstrate that multimodal learning yields an overall improvement in the sign recognition performance.

Abstract
Creating a mobile urban tourism storytelling application presents several interactivity challenges on how to convey an engaging multimedia experience on-site. This article describes a methodology for fast prototyping of a multimedia mobile applications dedicated to urban tourism storytelling. The application can be a game that takes advantage of several locationbased technologies, freely available geo-referenced media, and augmented reality for immersive gameplay. The goal is to create serious games for tourism that follow a main narrative but where the story can automatically adapt itself to the current location of the player, assimilate possible detours and allow posterior out-of-location playback. Adaptable stories can use dynamic information from map sources such as points of interest (POI), elevation or virtual buildings. The main focus is for these locationbased storytelling games to create more engagement between the tourists and the urban environment. To explore this concept, an application was designed for the city of Porto: Unlocking Porto. This location-based game with a central, yet adaptable, story engages the player into the main sights following an augmented reality path while playing small games. The article discusses and presents solutions for media acquisition, interactive storytelling, game-design interface and multi-disciplinary coordination for mobile app development.

Abstract

Abstract
Background/Aim: This study aimed to evaluate the in vitro efficacy of carboplatin and piroxicam, both in isolation and combined, against T24 and 5637 human urinary bladder cancer cell lines. Materials and Methods: Cell viability, drug interaction, cell morphology, cell proliferation, apoptosis and autophagy were analyzed after 72 h of drug exposure. Statistical analysis was performed and values of p<0.05 were considered statistically significant. Results: Drug exposure in combination led to a significant reduction of cell viability comparatively to single-drug exposure. These combinations resulted in a synergistic interaction in the T24 (combination index for 50% effect (CI50)=0.65) and 5637 (CI50=0.17) cell lines. Notable increase of morphological alterations, a marked decrease of Ki-67 expression, a considerable enhancement of autophagic vacuoles and a minimal effect on apoptosis was observed in both cell lines treated with combined drugs. Conclusion: Data showed that in vitro combination of carboplatin and piroxicam produced a more potent antiproliferative effect when compared to single drugs.