Abstract
Industrial agent technologies have been integrated in key elements coupling industrial systems and software logic, which is an important issue in the design of cyber-physical systems. Although several efforts have been tried out over the last decades to integrate software agents with physical hardware devices, and some commonalities can be observed among the existing practices, there is no uniform way overall. This work presents an empirical survey of existing practices in three application area, namely factory automation, power & energy systems and building automation. It identifies pertaining common issues and discusses how they integrate low level automation functions by utilizing industrial agents. The surveyed practices reveal high diversity, customized traditional integration focusing mostly on I/O functions, without security, and an overall approach that is mostly coupled rather than embedded.

Abstract
Embedded computers such as Raspberry Pi are gaining market as they offer considerable computation power on a flexible platform that can run different operating systems and user level libraries. There are a number of contributions on building middleware for connecting devices based on embedded computers in various ways; however, the temporal behavior of these systems has not been sufficiently covered, despite the fact that this is essential to validate the system design, operation, and timeliness that is needed in domains such as cyber-physical systems (CPS). This paper analyzes the temporal behavior of the connection among embedded computers and servers in the context of time sensitive deployments where some nodes can be virtualized offering mixed criticality execution platforms. We provide a scheme for using the Data Distribution Service standard to connect embedded computers based on Raspberry Pi and servers to analyze the temporal response stability.

Abstract
The problem of testing the null hypothesis of a common direction across several populations defined on the hypersphere arises frequently when we deal with directional data. We may consider the Analysis of Variance (ANOVA) for testing such hypotheses. However, for the Watson distribution, a commonly used distribution for modeling axial data, the ANOVA test is only valid for large concentrations. So we suggest to use alternative tests, such as bootstrap and permutation tests in ANOVA. Then, we investigate the performance of these tests for data from Watson populations defined on the hypersphere.

Abstract

Abstract
An overwhelming quantity of messages is posted in social networks every minute. To make the utilization of these platforms more productive, it is imperative to filter out information that is irrelevant to the general audience, such as private messages, personal opinions or well-known facts. This work is focused on the automatic classification of public social text according to its potential relevance, from a journalistic point of view, hopefully improving the overall experience of using a social network. Our experiments were based on a set of posts with several criteria, including the journalistic relevance, assessed by human judges. To predict the latter, we rely exclusively on linguistic features, extracted by Natural Language Processing tools, regardless the author of the message and its profile information. In our first approach, different classifiers and feature engineering methods were used to predict relevance directly from the selected features. In a second approach, relevance was predicted indirectly, based on an ensemble of classifiers for other key criteria when defining relevance-controversy, interestingness, meaningfulness, novelty, reliability and scope-also in the dataset. The first approach achieved a F (1)-score of 0.76 and an Area under the ROC curve (AUC) of 0.63. But the best results were achieved by the second approach, with the best learned model achieving a F (1)-score of 0.84 with an AUC of 0.78. This confirmed that journalistic relevance can indeed be predicted by the combination of the selected criteria, and that linguistic features can be exploited to classify the latter.

Abstract
One way of contributing to a demonstration that a medical device is acceptably safe is to show that the device satisfies a set of requirements known to mitigate hazards. This paper describes experience using formal techniques to model an IV infusion device and to prove that the modelled device captures a set of requirements. The requirements chosen for the study are based on a draft proposal developed by the US Food and Drug Administration (FDA). A major contributor to device related errors are (user) interaction errors. For this reason the chosen models and requirements focus on user interface related issues.