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
The process of migrating from a monolithic to a microservices based architecture is currently described as a form of modernizing applications. The core principles of microservices, which mostly reside in achieving loose coupling between the services, highly depend on the implementation approaches used. Being microservices a complete change of paradigm that contrasts with the traditional way of developing software, the current lack of established principles often results in implementations that conflict with its alleged benefits. Given its distributed nature, performance is affected, but specific implementation patterns can further impact it. This paper aims to address the impact that microservices-based solutions, featuring different implementation patterns, have on performance and how it compares with monolithic applications. To do so, benchmarks are conducted over one application developed following a traditional monolithic approach, and two equivalent microservices-based implementations featuring distinct inter-service communication mechanisms and data management methodologies.

Abstract
This paper presents a data market aimed at trading energy forecasts data. The system architecture is built using blockchain as a service, allowing access to data streams and establishing a distributed settlement between stakeholders. Energy Forecasts data is presented as the commodity traded in the market, whose settlement is provided through the blockchain on the basis of the extracted value provided by market stakeholders. Our proposal allows market stakeholders to acquire energy forecasts and pay according to the data accuracy, solving the confidentiality problem of freely sharing data. A data quality reward is introduced, steering the compensation sent to market participants. The data market design is presented and an evaluation campaign is performed, showing that the data market produced functionally valid results in comparison with the results achieved with a central simulated approach. Moreover, results show that the data market architecture is able to scale.

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
Construction and analysis of distributed systems is difficult. Multiparty session types (MPST) constitute a method to make it easier. The idea is to use type checking to statically prove deadlock freedom and protocol compliance of communicating processes. In practice, the premier approach to apply the MPST method in combination with mainstream programming languages has been based on API generation. In this paper (pearl), we revisit and revise this approach. Regarding our “revisitation”, using Scala 3, we present the existing API generation approach, which is based on deterministic finite automata (DFA), in terms of both the existing states-as-classes encoding of DFAs as APIs, and a new states-as-type-parameters encoding; the latter leverages match types in Scala 3. Regarding our “revision”, also using Scala 3, we present a new API generation approach that is based on sets of pomsets instead of DFAs; it crucially leverages match types, too. Our fresh perspective allows us to avoid two forms of combinatorial explosion resulting from implementing concurrent subprotocols in the DFA-based approach. We implement our approach in a new API generation tool.