Abstract
Unknown input observers (UIO) can be used in the model-based fault diagnosis (FD) system to reduce or eliminate the effect of unknown disturbances present on the process and used to create a set of residuals that are decoupled and sensitive to faults. In this work, a new FD scheme of the In-Wheel motors electric vehicle (IWM-EV) with active front steering was carried out, as well as the design of the fault isolation banks of UIOs. These banks are used to generate residuals that are robust againts to noise and are sensitive to only one fault. This way the faults in the steering or in-wheel actuator are detected and isolated with a higher rate of accuracy. The proposed FD scheme is verified by Carsim® and Matlab/Simulink® cosimulation. © 2017 Taylor & Francis Group, London.

Abstract
Automated assessment is an essential part of eLearning. Although comparatively easy for multiple choice questions (MCQs), automated assessment is more challenging when exercises involve languages used in computer science. In this particular case, the assessment is more than just grading and must include feedback that leads to the improvement of the students’ performance. This paper presents ongoing work to develop Kora, an automated diagram assessment tool with enhanced feedback, targeted to the multiple diagrammatic languages used in computer science. Kora builds on the experience gained with previous research, namely: a diagram assessment tool to compute di erences between graphs; an IDE inspired web learning environment for computer science languages; and an extensible web diagram editor. Kora has several features to enhance feedback: it distinguishes syntactic and semantic errors, providing specialized feedback in each case; it provides progressive feedback disclosure, controlling the quality and quantity shown to each student after a submission; when possible, it integrates feedback within the diagram editor showing actual nodes and edges on the editor itself. © Hélder Correia, José Paulo Leal, and José Carlos Paiva

Abstract
Ambient Intelligence has always been associated with the promise of exciting new applications, aware of the users' needs and state, and proactive towards their goals. However, the acquisition of the necessary information for supporting such high-level learning and decision-making processes is not always straightforward. In this chapter we describe a multi-faceted smart environment for the acquisition of relevant contextual information about its users. This information, acquired transparently through the technological devices in the environment, supports the building of high-level knowledge about the users, including a quantification of aspects such as performance, attention, mental fatigue and stress. The environment described is particularly suited for milieus such as workplaces and classrooms, in which this kind of information may be very important for the effective management of human resources, with advantages for organizations and individuals alike. © 2017 The authors and IOS Press.

Abstract
Applying model-based testing to interactive systems enables the systematic testing of the system by automatically simulating user actions on the user interface. It reduces the cost of (expensive) user testing by identifying implementations errors without the involvement of human users, but raises a number of specific challenges, such as how to achieve good coverage of the actual use of the system during the testing process. This paper describes TOM, a model-based testing framework that uses a combination of tools and mutation testing techniques to maximize testing of user interface behaviors.

Abstract

Abstract
Urban form is an important driver of energy demand and therefore of GHG emissions in urban areas. Yet, research on urban form and energy remains sectorial and hasn't been able to deliver a full understanding of the impact of the physical structure of cities upon their energy demand. Most common approaches feature engineering models in buildings, and statistical models in transports. This study aims at contributing to the characterization of the link between urban form and energy considering altogether three distinct energy uses: ambient heating and cooling in buildings, and travel. A high-resolution methodology is proposed. It applies GIS to provide the analysis with a spatially-explicit character, and neural networks to model energy demand based on a set of relevant urban form indicators. The results confirm that the effect of urban form indicators on the overall energy needs is far from being negligible. In particular, the number of floors, the diversity of activities within a walking reach, the floor area and the subdivision of blocks evidenced a significant impact on the overall energy demand of the case study analyzed.