Abstract
In the last few years we have been seeing a drastic change in the way software is developed. Large-scale software projects are being assembled by a flexible composition of many (small) components possibly written in different programming languages and deployed anywhere in the cloud - the so-called microservice-based applications. The dramatic growth in popularity of microservice-based applications has pushed several companies to apply major refactorings to their software systems. However, this is a challenging task that may take several months or even years. We propose a methodology to automatically evolve a Java monolithic application into a microservice-based one. Our methodology receives the Java code and a proposition of microservices and refactors the original classes to make each microservice independent. Our methodology creates an API for each method call to classes that are in other services. The database entities are also refactored to be included in the corresponding service. The initial evaluation shows that our tool can successfully refactor 80% of the applications tested. © 2021 ACM.

Abstract

Abstract
Linear programming is a mathematical optimization technique used in numerous fields including mathematics, economics, and computer science, with numerous industrial contexts, including solving optimization problems such as planning routes, allocating resources, and creating schedules. As a result of its wide breadth of applications, a considerable amount of its user base is lacking in terms of programming knowledge and experience and thus often resorts to using graphical software such as Microsoft Excel. However, despite its popularity amongst less technical users, the methodologies used by these tools are often ad-hoc and prone to errors. To counteract this problem we propose creating a block-based language that allows users to create linear programming models using data contained inside spreadsheets. This language will guide the users to write syntactically and semantically correct programs and thus aid them in a way that current languages do not.

Abstract
Portugal is one of the European countries that implemented early protective measures in the context of the COVID-19 pandemic. Portugal declared a state of emergency on 18 March, and a set of regional and national preventive public health measures was progressively implemented. Studies on the psychological impact of pandemics show evidence of the negative impact on mental health. Of particular concern are individuals with previous fragility (e.g. personal, family or occupational) and those undergoing life transitions. In this paper, we present a telephone-based psychological crisis intervention that was implemented to provide brief, appropriate, and timely psychological help. This intervention follows standard models of crisis intervention and is structured in five phases and five different intervention modules to take into account the impact of the pandemic on the mental health of specific risk groups. With these support services, we hope to help our community better cope with the immediate impact of the pandemic and to contribute to preventing serious mental health problems in the medium and long term.

Abstract

Abstract
Encrypted databases systems and searchable encryption schemes still leak critical information (e.g.: access patterns) and require a choice between privacy and efficiency. We show that using ORAM schemes as a black-box is not a panacea and that optimizations are still possible by improving the data structures. We design an ORAM-based secure database that is built from the ground up: we replicate the typical data structure of a database system using different optimized ORAM constructions and derive a new solution for oblivious searches on databases. Our construction has a lower bandwidth overhead than state-of-the-art ORAM constructions by moving client-side computations to a proxy with an intermediate (rigorously defined) level of trust, instantiated as a server-side isolated execution environment. We formally prove the security of our construction and show that its access patterns depend only on public information. We also provide an implementation compatible with SQL databases (PostgresSQL). Our system is 1.2 times to 4 times faster than state-of-the-art ORAM-based solutions.