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
SEAS Shared Intelligence (SEAS SI) is a platform for algorithms sharing and execution developed under the scope of Smart Energy Aware Systems (SEAS) project to promote the intelligent management of smart grids and microgrids, by means of the shared usage of algorithms and tools, while ensuring code and intellectual protection. In this paper the platform goals and architecture are described, focusing on the recent achievement regarding the connection of distinct algorithms, which enables the execution of dynamic simulations using sequences of algorithms from distinct sources. A case study based on several SEAS SI available algorithms is presented with the objective of showing the advantages of the SEAS SI capability of supporting simulations based on sequences of algorithms. Namely, electricity market bid values are calculated by a metalearner, which is fed by market price forecasts using different methods, and by their respective forecasting errors. A case study presents some results to validate the presented work, through the simulation of the MIBEL electricity market using MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). © 2017 IEEE.

Abstract
This paper presents the specification of an architecture for self-organizing scheduling systems. The proposed architecture uses learning by observing the experts and interpretation of scheduling experience. The design of intelligent systems that learn with experts is a very hard and challenging domain because current systems are becoming more and more complex and subject to rapid changes. In this work, different areas as Intelligent and Adaptive Human-Machine Interfaces, Metacognition and Learning from Observation, Self-managed Systems, amongst others, are joint together resulting in a global fully integrated architecture for self-organizing scheduling systems.

Abstract
The energy consumption of the current building stock represents about 40% of the total final energy consumption in Europe. New gamification techniques may play a significant role in helping users adopt new and more energy efficient behaviours. This paper presents the advances achieved within the context of the EU-funded project EnerGAware - Energy Game for Awareness of energy efficiency in social housing communities. The main objective of the project, funded by the European Union under the Horizon2020 programme, is to reduce the energy consumption and carbon emissions in a sample of European social housing by changing the energy efficiency behaviour of the social tenants through the implementation of a serious game linked to the real energy use of the participants' homes. © 2017 IEEE.