Abstract

Abstract
Kidney exchange programs aim at matching end-stage renal disease patients who have a willing but incompatible kidney donor with another donor. The programs comprise a pool of such incompatible patient-donor pairs and, whenever a donor from one pair is compatible with the patient of another pair, and vice versa, the pairs may be matched and exchange kidneys. This is typically a two-step process in which, first, a set of pairs is matched based on preliminary compatibility tests and, second, the matched pairs are notified and more accurate compatibility tests are performed to verify that actual transplantation can take place. These additional tests may reveal incompatibilities not previously detected. When that happens, the planned exchange will not proceed. Furthermore, pairs may drop out before the transplant, and thus the planned exchange is canceled. In this paper, we study the case in which a new set of pairs may be matched if incompatibilities are discovered or a pair withdraws from the program. The new set should be as close as possible to the initial set in order to minimize the material and emotional costs of the changes. Various recourse policies that determine the admissible second-stage actions are investigated. For each recourse policy, we propose a novel adjustable robust integer programming model. Wealso propose solution approaches to solve this model exactly. The approaches are validated through thorough computational experiments. Summary of Contribution: In the paper, we present an original work related to the modeling and optimization approaches for Kidney Exchange Programs (KEPs). Currently, KEPs represent an alternative way for patients suffering from renal failure to find a compatible (living) donor. The problem of determining an assignment of patients to (compatible) donors that maximizes the number of transplants in a KEP can be seen as a vertex-disjoint cycle packing problem. Thus, KEPs have been extensively studied in the literature of integer programming. In practice, the assignment determined to a KEP might not be implemented due to withdraws from the program (e.g., a more accurate compatible test shows a new incompatibility or a patient health condition unable him/her to participate on the KEP). In our paper, we model the problem of determining a robust solution to the KEP, i.e., a solution that minimizes the material and emotional costs of changing an assignment. In this way, we propose and design solution approaches for three recourse policies that anticipate withdraws. Through computational experiments we compare the three recourse policies and validate the practical interest of robust solutions.

Abstract
Nonprofit organizations are constantly challenged to find new ways of finding new donors and sources of funding for their solidarity actions. The traumatic events that occurred in the summer of 2017 in Portugal that caused more than 100 deaths consumed by the fires caused these organizations to have difficulties in coordinating the whole wave of solidarity generated in the community. In this sense, this study has developed a technological platform based exclusively on free technologies that allowed these entities to receive donations and manage this whole process. The application developed enables the reception of anonymous donations and monitoring the status of each donation. Furthermore, several requirements in terms of compatibility with mobile devices, usability, security, and privacy were implemented in the platform.

Abstract
COVID-19 has caused profound impacts on the economy and society. In Portugal, many companies needed to temporarily close, and many workers were forced to go into lay-off. However, at the same time, several companies are trying to respond to the challenges posed by this pandemic by introducing organisational innovations that would enable them to respond to the needs of the people and organisations that most require help during this period. In this sense, this study aims to explore the role of organisational innovation in the business sector in Portugal through seven case studies. The findings reveal a very diverse set of initiatives in which the role of internal and external sources of innovation stands out simultaneously. Most of the innovations identified have a procedural focus, while structural innovations have less influence. This study is particularly relevant in the practical dimension by encouraging other countries and companies to replicate these initiatives.

Abstract
The increasing pervasiveness of the Internet of Things is resulting in a steady increase of cyberattacks in all of its facets. One of the most predominant attack vectors is related to its identity management, as it grants the ability to impersonate and circumvent current trust mechanisms. Given that identity is paramount to every security mechanism, such as authentication and access control, any vulnerable identity management mechanism undermines any attempt to build secure systems. While digital certificates are one of the most prevalent ways to establish identity and perform authentication, their provision at scale remains open. This provisioning process is usually an arduous task that encompasses device configuration, including identity and key provisioning. Human configuration errors are often the source of many security and privacy issues, so this task should be semi-autonomous to minimize erroneous configurations during this process. In this paper, we propose an identity management (IdM) and authentication method called YubiAuthIoT. The overall provisioning has an average runtime of 1137.8 ms +/- 65.11+delta. We integrate this method with the FIWARE platform, as a way to provision and authenticate IoT devices.

Abstract
Although Deep Learning models have achieved incredible results in medical image classification tasks, their lack of interpretability hinders their deployment in the clinical context. Case-based interpretability provides intuitive explanations, as it is a much more human-like approach than saliency-map-based interpretability. Nonetheless, since one is dealing with sensitive visual data, there is a high risk of exposing personal identity, threatening the individuals' privacy. In this work, we propose a privacy-preserving generative adversarial network for the privatization of case-based explanations. We address the weaknesses of current privacy-preserving methods for visual data from three perspectives: realism, privacy, and explanatory value. We also introduce a counterfactual module in our Generative Adversarial Network that provides counterfactual case-based explanations in addition to standard factual explanations. Experiments were performed in a biometric and medical dataset, demonstrating the network's potential to preserve the privacy of all subjects and keep its explanatory evidence while also maintaining a decent level of intelligibility.