Abstract
As blockchains go beyond cryptocurrencies into applications in multiple industries such as Insurance, Healthcare and Banking, handling personal or sensitive data, data access control becomes increasingly relevant. Access control mechanisms proposed so far are mostly based on requester identity, particularly for permissioned blockchain platforms, and are limited to binary, all-or-nothing access decisions. This is the case with Hyperledger Fabric's native access control mechanisms and, as permission updates require consensus, these fall short regarding the flexibility required to address GDPR-derived policies and client consent management. We propose SDAM, a novel access control mechanism for Fabric that enables fine-grained and dynamic control policies, using both contextual and resource attributes for decisions. Instead of binary results, decisions may also include mandatory data transformations as to conform with the expressed policy, all without modifications to Fabric. Results show that SDAM's overhead w.r.t baseline Fabric is acceptable. The scalability of the approach w.r.t to the number of concurrent clients is also evaluated and found to follow Fabric's.

Abstract

Abstract
Digital musical instruments have become increasingly prevalent in musical creation and production. Optimizing their usability and, particularly, their expressiveness, has become essential to their study and practice. The absence of multimodal feedback, present in traditional acoustic instruments, has been identified as an obstacle to complete performer-instrument interaction in particular due to the lack of embodied control. Mobile-based digital musical instruments present a particular case by natively providing the possibility of enriching basic auditory feedback with additional multimodal feedback. In the experiment presented in this article, we focused on using visual and haptic feedback to support and enrich auditory content to evaluate the impact on basic musical tasks (i.e., note pitch tuning accuracy and time). The experiment implemented a protocol based on presenting several musical note examples to participants and asking them to reproduce them, with their performance being compared between different multimodal feedback combinations. Collected results show that additional visual feedback was found to reduce user hesitation in pitch tuning, allowing users to reach the proximity of desired notes in less time. Nonetheless, neither visual nor haptic feedback was found to significantly impact pitch tuning time and accuracy compared to auditory-only feedback.

Abstract
Healthcare environments are generating a deluge of sensitive data. Nonetheless, dealing with large amounts of data is an expensive task, and current solutions resort to the cloud environment. Additionally, the intersection of the cloud environment and healthcare data opens new challenges regarding data privacy. With this in mind, we propose MEDCLOUDCARE (MCC), a healthcare application offering medical image viewing and processing tools while integrating cloud computing and AI. Moreover, MCC provides security and privacy features, scalability and high availability. The system is intended for two user groups: health professionals and researchers. The former can remotely view, process and share medical imaging information in the DICOM format. Also, it can use pre-trained Machine Learning (ML) models to aid the analysis of medical images. The latter can remotely add, share, and deploy ML models to perform inference on DICOM images. MCC incorporates a DICOM web viewer enabling users to view and process DICOM studies, which they can also upload and store. Regarding the security and privacy of the data, all sensitive information is encrypted at rest and in transit. Furthermore, MCC is intended for cloud environments. Thus, the system is deployed using Kubernetes, increasing the efficiency, availability and scalability of the ML inference process.

Abstract
This paper introduces a class of automata and associated languages, suitable to model a computational paradigm of fuzzy systems, in which both vagueness and simultaneity are taken as first-class citizens. This requires a weighted semantics for transitions and a precise notion of a synchronous product to enforce the simultaneous occurrence of actions. The usual relationships between automata and languages are revisited in this setting, including a specific Kleene theorem.

Abstract
The promise of increased agility, autonomy, scalability, and reusability has made the microservices architecture a de facto standard for the development of large-scale and cloud-native commercial applications. Software patterns are an important design tool, and often they are selected and combined with the goal of obtaining a set of desired quality attributes. However, from a research standpoint, many patterns have not been widely validated against industry practice, making them not much more than interesting theories. To address this, we investigated how practitioners perceive the impact of 14 patterns on 7 quality attributes. Hence, we conducted 9 semi-structured interviews to collect industry expertise regarding (1) knowledge and adoption of software patterns, (2) the perceived architectural trade-offs of patterns, and (3) metrics professionals use to measure quality attributes. We found that many of the trade-offs reported in our study matched the documentation of each respective pattern, and identified several gains and pains which have not yet been reported, leading to novel insight about microservice patterns.