Abstract
As the benefits and applicability of microservice architectures become better understood by the software industry, and this architecture becomes increasingly more adopted for building stable, independent and scalable cloud applications, a new set of concerns have alerted developers regarding communication between the different microservices. A service mesh tries to address this issue by creating a clear separation of concerns between application logic and the infrastructure needed for the communication between the different services. This is accomplished by abstracting the cross-cutting concerns related with communication out of the internal services making it possible to be reused by the different services. Existing literature describes a service mesh pattern and a sidecar pattern. This paper leans on these patterns and proposes six patterns found by observing the, what is commonly called, good practices. The six patterns are service mesh, shared communication library, node agent, sidecar, service mesh team and control plane per cluster.

Abstract
Although outlier detection/elimination has been studied before, few comprehensive studies exist on when exactly this technique would be useful as preprocessing in classification tasks. The objective of our study is to fill in this gap. We have performed experiments with 12 various outlier elimination methods and 10 classification algorithms on 50 different datasets. The results were then processed by the proposed reduction method, whose aim is identify the most useful workflows for a given set of tasks (datasets). The reduction method has identified that just three OEMs that are generally useful for the given set of tasks. We have shown that the inclusion of these OEMs is indeed useful, as it leads to lower loss in accuracy and the difference is quite significant (0.5%) on average.

Abstract
Artificial intelligence techniques are now being applied in different medical solutions ranging from disease screening to activity recognition and computer-aided diagnosis. The combination of computer science methods and medical knowledge facilitates and improves the accuracy of the different processes and tools. Inspired by these advances, this paper performs a literature review focused on state-of-the-art glaucoma screening, segmentation, and classification based on images of the papilla and excavation using deep learning techniques. These techniques have been shown to have high sensitivity and specificity in glaucoma screening based on papilla and excavation images. The automatic segmentation of the contours of the optic disc and the excavation then allows the identification and assessment of the glaucomatous disease's progression. As a result, we verified whether deep learning techniques may be helpful in performing accurate and low-cost measurements related to glaucoma, which may promote patient empowerment and help medical doctors better monitor patients.

Abstract
Applying machine learning (ML), and especially deep learning, to understand visual content is becoming common practice in many application areas. However, little attention has been given to its use within the multimedia creative domain. It is true that ML is already popular for content creation, but the progress achieved so far addresses essentially textual content or the identification and selection of specific types of content. A wealth of possibilities are yet to be explored by bringing the use of ML into the multimedia creative process, allowing the knowledge inferred by the former to influence automatically how new multimedia content is created. The work presented in this article provides contributions in three distinct ways towards this goal: firstly, it proposes a methodology to re-train popular neural network models in identifying new thematic concepts in static visual content and attaching meaningful annotations to the detected regions of interest; secondly, it presents varied visual digital effects and corresponding tools that can be automatically called upon to apply such effects in a previously analyzed photo; thirdly, it defines a complete automated creative workflow, from the acquisition of a photograph and corresponding contextual data, through the ML region-based annotation, to the automatic application of digital effects and generation of a semantically aware multimedia story driven by the previously derived situational and visual contextual data. Additionally, it presents a variant of this automated workflow by offering to the user the possibility of manipulating the automatic annotations in an assisted manner. The final aim is to transform a static digital photo into a short video clip, taking into account the information acquired. The final result strongly contrasts with current standard approaches of creating random movements, by implementing an intelligent content- and context-aware video.

Abstract
Professional theatre actors are highly specialized in controlling their own expressive behaviour and non-verbal emotional expressiveness, so they are of particular interest in fields of study such as affective computing. We present Acting Emotions, an experimental protocol to investigate the physiological correlates of emotional valence and arousal within professional theatre actors. Ultimately, our protocol examines the physiological agreement of valence and arousal amongst several actors. Our main contribution lies in the open selection of the emotional set by the participants, based on a set of four categorical emotions, which are self-assessed at the end of each experiment. The experiment protocol was validated by analyzing the inter-rater agreement (> 0.261 arousal, > 0.560 valence), the continuous annotation trajectories, and comparing the box plots for different emotion categories. Results show that the participants successfully induced the expected emotion set to a significant statistical level of distinct valence and arousal distributions. © 2022 Owner/Author.

Abstract
Ocean exploration is of major importance for several reasons, including energy and mineral resource retrieval, sovereignty, and environmental concerns. The use of autonomous underwater vehicles (AUV) has thus been receiving increased attention from the scientific community. In this context, it has been shown that the use of buoyancy change modules (BCMs) can significantly improve the energy efficiency of an AUV. However, the literature regarding the detailed design of these modules is scarce. This paper contributes to this field by describing the development of an electromechanical buoyancy change module prototype to be incorporated into an existing AUV. A detailed description of the constraints and compromises existing in the design of the device components is presented. In addition, the mechanical design of the hull based on FEM simulations is described in detail. The prototype is experimentally tested in a shallow pool where its full functionality is shown. The paper also presents preliminary experimental values of the power consumption of the device and compares them with the ones provided by existing models in the literature.