Abstract
Facial expression recognition (FER) is currently one of the most active research topics due to its wide range of applications in the human-computer interaction field. An important part of the recent success of automatic FER was achieved thanks to the emergence of deep learning approaches. However, training deep networks for FER is still a very challenging task, since most of the available FER data sets are relatively small. Although transfer learning can partially alleviate the issue, the performance of deep models is still below of its full potential as deep features may contain redundant information from the pre-trained domain. Instead, we propose a novel end-to-end neural network architecture along with a well-designed loss function based on the strong prior knowledge that facial expressions are the result of the motions of some facial muscles and components. The loss function is defined to regularize the entire learning process so that the proposed neural network is able to explicitly learn expression-specific features. Experimental results demonstrate the effectiveness of the proposed model in both lab-controlled and wild environments. In particular, the proposed neural network provides quite promising results, outperforming in most cases the current state-of-the-art methods.

Abstract
The use of Wireless Sensor Network (WSN) based technologies is an attractive solution for large-scale sensing applications (wide area deployment), such as environmental monitoring, precision agriculture and industrial automation. IEEE 802.15.4/ZigBee standards are the most used communication protocols for WSN technologies, where the cluster-tree topology is pointed out as a suitable topology to support the implementation of large-scale WSNs. These networks are usually scheduled to prioritise convergecast (upstream) traffic generated from sensor nodes toward the sink node. However, this scheduling pattern results in higher delays for control messages (downstream traffic). Within this context, this paper proposes a Hybrid Beacon Scheduling (Fast-HyBeS) scheme to enable the periodic reconfiguration of cluster-tree WSNs. The underlying idea is to periodically schedule a downstream opportunity window, to allow a faster dissemination of control messages. This opportunity window follows a top-down scheduling approach that prioritises the downstream traffic. Simulation results show that the use of Fast-HyBeS can significantly decrease the end-to-end communication delay for control messages, when compared to the use of static convergecast scheduling schemes. Moreover, the simulation results also highlight that the Fast-HyBeS has a negligible impact upon end-to-end communication delays of the monitoring traffic.

Abstract
Sensory tests are quality assurance tools commonly used to measure and/or detect the presence of abnormal characteristics perceived through the senses in lots of raw material and final products in many manufacturing and food industries. In this paper two acceptance sampling plans for sensory evaluation are designed, and an illustration of the performance of such plans applied to a real data set is presented.

Abstract
Agent-based systems, an approach derived from distributed artificial intelligence, have been introduced for designing large complex systems. They are also suitable to solve challenging problems in industrial environments, being an appropriate technology for realizing cyber-physical systems. In such configuration, they need to interface with automation and control devices. However, until now there is no widely accepted practice nor pattern to interface the software agents with the automation functions. This work addresses this issue and introduces corresponding integration patterns in order to achieve full interoperability and reusability. This work, therefore, provides a methodology for mapping existing practices into a set of generic templates and also discusses the applicability of the proposed approach to different industrial application domains. © 2018 IEEE.

Abstract
We report the detection of continuous positional and polarization changes of the compact source SgrA* in high states ("flares") of its variable near-infrared emission with the near-infrared GRAVITY-Very Large Telescope Interferometer (VLTI) beam-combining instrument. In three prominent bright flares, the position centroids exhibit clockwise looped motion on the sky, on scales of typically 150 mu as over a few tens of minutes, corresponding to about 30% the speed of light. At the same time, the flares exhibit continuous rotation of the polarization angle, with about the same 45(+/- 15) min period as that of the centroid motions. Modelling with relativistic ray tracing shows that these findings are all consistent with a near face-on, circular orbit of a compact polarized "hot spot" of infrared synchrotron emission at approximately six to ten times the gravitational radius of a black hole of 4 million solar masses. This corresponds to the region just outside the innermost, stable, prograde circular orbit (ISCO) of a Schwarzschild-Kerr black hole, or near the retrograde ISCO of a highly spun-up Kerr hole. The polarization signature is consistent with orbital motion in a strong poloidal magnetic field.

Abstract