Abstract
In the last years, developments in data collection, storing, processing and analysis technologies resulted in an unprecedented use of data by organizations. The volume and variety of data, combined with the velocity at which decisions must now be taken and the dynamism of business environments, pose new challenges to Machine Learning. Namely, algorithms must now deal with streaming data, concept drift, distributed datasets, among others. One common task nowadays is to update or re-train models when data changes, as opposed to traditional one-shot batch systems, in which the model is trained only once. This paper addresses the issue of when to update or re-train a model, by proposing an approach to predict the performance metrics of the model if it were trained at a given moment, with a specific set of data. We validate the proposed approach in an interactive Machine Learning system in the domain of fraud detection.

Abstract
Open innovation is proved to be determinant in the rationalization of sustainable innovation ecosystems. Firms, universities, governments, user communities and the overall environment are called to contribute to this dynamic process. This study aims to contribute to a better understanding of the impact of open innovation on firms' performance and to empirically assess whether university-industry collaborations are complementary or substitutes for this activity. Primary data were collected from a survey encompassing 908 firms, and then combined with performance indicators from SABI (Spanish and Portuguese business information). Econometric estimations were run to evaluate the role of open innovation and university-industry collaboration in the firm innovative propensity and performance. Results highlight the importance of diversity in collaborations with the academia and inbound open innovation strategy as enhancers of firm performance. The two activities reinforce each other. By testing the impact of open innovation practices on company performance, the need for heterogeneity in terms of contact type and university is also demonstrated. Findings cast light on the need to reformulate existing policy packages, reinforcing the ties with academia as well as the promotion of open innovation strategies. The connection to the innovation ecosystem needs to be further encouraged as well as the promotion of persistent connections with the knowledge sources in an open and multilateral framework.

Abstract

Abstract
Automated Program Repair (APR) is an area of research focused on the automatic generation of bug-fixing patches. Current APR approaches present some limitations, namely overfitted patches and low maintainability of the generated code. Several works are tackling this problem by attempting to come up with algorithms producing higher quality fixes. In this experience paper, we explore an alternative. We believe that by using existing low-cost APR techniques, fast enough to provide real-time feedback, and encouraging the developer to work together with the APR inside the IDE, will allow them to immediately discard proposed fixes deemed inappropriate or prone to reduce maintainability. Most developers are familiar with real-time syntactic code suggestions, usually provided as code completion mechanisms. What we propose are semantic code suggestions, such as code fixes, which are seldom automatic and rarely real-time. To test our hypothesis, we implemented a Visual Studio Code extension (named pAPRika), which leverages unit tests as specifications and generates code variations to repair bugs in JavaScript. We conducted a preliminary empirical study with 16 participants in a crossover design. Our results provide evidence that, although incorporating APR in the IDE improves the speed of repairing faulty programs, some developers are too eager to accept patches, disregarding maintenance concerns.

Abstract
The worldwide pandemic caused by the new coronavirus (COVID-19) has encouraged the development of multiple computer-aided diagnosis systems to automate daily clinical tasks, such as abnormality detection and classification. Among these tasks, the segmentation of COVID lesions is of high interest to the scientific community, enabling further lesion characterization. Automating the segmentation process can be a useful strategy to provide a fast and accurate second opinion to the physicians, and thus increase the reliability of the diagnosis and disease stratification. The current work explores a CNN-based approach to segment multiple COVID lesions. It includes the implementation of a U-Net structure with a ResNet34 encoder able to deal with the highly imbalanced nature of the problem, as well as the great variability of the COVID lesions, namely in terms of size, shape, and quantity. This approach yields a Dice score of 64.1%, when evaluated on the publicly available COVID-19-20 Lung CT Lesion Segmentation GrandChallenge data set.

Abstract
Optical fiber sensing systems have been widely developed for several fields such as biomedical diagnosis, food technology, military and industrial applications and civil engineering. Nowadays, the growth and advances of optical fiber sensors (OFS) are focused on the development of novel sensing concepts and transducers as well as sensor cost reduction. This review provides an overview of the state-of-the-art of OFS based on sol-gel materials for diverse applications with particular emphasis on OFS for structural health monitoring of concrete structures. The types of precursors used in the development of sol-gel materials for OFS functionalization to monitor a wide range of analytes are debated. The main advantages of OFS compared to other sensing systems such as electrochemical sensors are also considered. An interdisciplinary review to a broad audience of engineers and materials scientists is provided and the relationship between the chemistry of sol-gel material synthesis and the development of OFS is considered. To the best of the authors' knowledge, no review manuscripts were found in which the fields of sol-gel chemistry and OFS are correlated. The authors consider that this review will serve as a reference as well as provide insights for experts into the application of sol-gel chemistry and OFS in the civil engineering field.