operator's role in industrial control centers takes place in time, which is one of the most important determinants of whether an expected action is going to be successful or not. in certain situations, due to the complex nature of the work, the existing interfaces and already prepared procedures do not meet the dynamic requirements of operator's cognitive demands, making the control tasks unnec...

conclusions the results of this study led to the identification of many of the errors and conditions that affect the performance of controllers, providing the ability to define safety and ergonomic interventions to reduce the risk of human error. therefore, the results of this study can be a basis for planning atm to prioritize prevention programs and safety enhancement results the analysis of ...

We are interested in scheduling tasks from several selfish agents on a set of parallel identical machines. A coordination mechanism consists in giving a scheduling policy to each machine. Given these policies, each agent chooses the machines on which she assigns her tasks, and her aim is to minimize the average completion times of her tasks. The aim of the system (social cost) is to minimize th...

Multitask learning has been applied successfully to a range of tasks, mostly morphosyntactic. However, little is known on when MTL works and whether there are data characteristics that help to determine the success of MTL. In this paper we evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine different auxiliary task configurations, amongst which a novel setup, and cor...

Tasks assignment in grid computing is a challenge for most researchers and developers of these types of systems. This is due to characteristics inherent to grid infrastructures, namely, heterogeneity, dynamicity and scalability. It becomes much more complex when it comes to assign tasks with precedence constraints represented by a Directed Acyclic Graph. The task assignment problem is well know...

In representational lifelong learning an agent aims to learn to solve novel tasks while updating its representation in light of previous tasks. Under the assumption that future tasks are ‘related’ to previous tasks, representations should be learned in such a way that they capture the common structure across learned tasks, while allowing the learner sufficient flexibility to adapt to novel aspe...

In Higher Education, there is currently a trend towards applying assessment to real world learning experiences. These types of assessment tasks often take the form of projects which are closely aligned with situations students might find themselves in when they graduate. Introducing new assessment tasks raises the issue of how do we assess students progress both reliably and validly. In this pa...

We propose a probabilistic segmentation scheme, which is widely applicable to some extend. Besides the segmentation itself our model incorporates object specific shading. Dependent upon application, the latter is interpreted either as a perturbation or as meaningful object characteristic. We discuss the recognition task for segmentation, learning tasks for parameter estimation as well as differ...