Abstract

Abstract
A demonstration is presented of how automated reasoning tools can be used to check the predictability of a user interface. Predictability concerns the ability of a user to determine the outcomes of their actions reliably. It is especially important in situations such as a hospital ward where medical devices are assumed to be reliable devices by their expert users (clinicians) who are frequently interrupted and need to quickly and accurately continue a task. There are several forms of predictability. A definition is considered where information is only inferred from the current perceptible output of the system. In this definition, the user is not required to remember the history of actions that led to the current state. Higher-order logic is used to specify predictability, and the Symbolic Analysis Laboratory is used to automatically verify predictability on real interactive number entry systems of two commercial drug infusion pumps—devices used in the healthcare domain to deliver fluids (e.g., medications, nutrients) into a patient’s body in controlled amounts. Areas of unpredictability are precisely identified with the analysis. Verified solutions that make an unpredictable system predictable are presented through design modifications and verified user strategies that mitigate against the identified issues. © 2013, Springer-Verlag London.

Abstract
iMOOC is a new pedagogical model for massive open online courses (Teixeira & Mota, 2013), that evolved from UAb’s online model (Pereira et al., 2008), based on its four pillars of student-centered learning, interaction, flexibility and digital inclusion. It is also a software platform, that supports this model, and that was developed at UAb in close articulation with the pedagogical model. In this paper we describe the guidelines that oriented such development, and argue in favor of the use (or re-use) of well-established and robust software components for this purpose, as opposed to building platforms from scratch. The emergence of MOOCs as open courses, where participants have free access to the course, created new challenges in a closed, formatted LMS landscape. This led to the development of whole new environments that addressed those requirements (edX, Coursera). The iMOOC approach, however, was to build a platform from existing open source software components using an integration of Moodle (https://moodle.org/), which was previously adapted to UAb’s pedagogical model (Rocio & Coelho, 2009), and Elgg (http://elgg.org/), combining the advantages of both formal and informal learning modes, and addressing the pedagogical requirements in a cost-effective way. The integration was achieved using the IMS specification for LTI (learning tools interoperability) (Severance, 2010). As a result, the iMOOC platform has been successfully used both in stand-alone projects, and also in the european ECO project, where the effort to turn it project-compliant was relatively simple, due to the adoption of well-established protocols.

Abstract
In our daily life, we often have a sense of being exhausted due to mental or physical work, together with a feeling of performance degradation in the accomplishment of simple tasks. This is in part due to the fact that the working capacity and the performance of an individual, either physical or mental, generally decrease as the day progresses, although factors like motivation also play a significant role. These negative effects are especially significant when carrying out long or demanding tasks, as often happens in an educational context. In order to avoid these effects, initiatives to promote a good management of the time and effort invested in each task are mandatory. Such initiatives, when effective, can have a wide range of positive effects, including on the performance, productivity, attention and even mental health. Seeking to find a viable and realistic approach to address this problem, this paper presents a non-invasive and non-intrusive way to measure mental workload, one of the aspects that affects mental fatigue the most. Specifically, we target scenarios of e-learning, in which the professor may not be present to assess the student's state. The aim is to create a tool that enables an actual management of fatigue in such environments and thus allows for the implementation of more efficient learning processes, adapted to the abilities and state of each student.

Abstract
Outlying observations are commonly encountered in the analysis of time series. In this paper a Bayesian approach is employed to detect additive outliers in order one Poisson integer-valued autoregressive time series. The methodology is informative and allows the identification of the observations which require further inspection. The procedure is illustrated with simulated and observed data sets.

Abstract
Intelligent service robots should be able to improve their knowledge from accumulated experiences through continuous interaction with the environment, and in particular with humans. A human user may guide the process of experience acquisition, teaching new concepts, or correcting insufficient or erroneous concepts through interaction. This paper reports on work towards interactive learning of objects and robot activities in an incremental and open-ended way. In particular, this paper addresses human-robot interaction and experience gathering. The robot's ontology is extended with concepts for representing human-robot interactions as well as the experiences of the robot. The human-robot interaction ontology includes not only instructor teaching activities but also robot activities to support appropriate feedback from the robot. Two simplified interfaces are implemented for the different types of instructions including the teach instruction, which triggers the robot to extract experiences. These experiences, both in the robot activity domain and in the perceptual domain, are extracted and stored in memory, and they are used as input for learning methods. The functionalities described above are completely integrated in a robot architecture, and are demonstrated in a PR2 robot. © 2014 IEEE.