Abstract
Software for robotic systems is becoming progressively more complex despite the existence of established software ecosystems like ROS, as the problems we delegate to robots become more and more challenging. Ensuring that the software works as intended is a crucial (but not trivial) task, although proper quality assurance processes are rarely seen in the open-source robotics community. This paper explains how we analyzed and improved a specialized path planner for steep-slope vineyards regarding its software dependability. The analysis revealed previously unknown bugs in the system, with a relatively low property specification effort. We argue that the benefits of similar quality assurance processes far outweigh the costs and should be more widespread in the robotics domain.

Abstract
With the increasing development of human-AI teaming structures within and across geographies, the time is ripe for a continuous and objective look at the predictors, barriers, and facilitators of human-AI scientific collaboration from a multidisciplinary point of view. This paper aims at contributing to this end by exploiting a set of factors affecting attitudes towards the adoption of human-AI interaction into scientific work settings. In particular, we are interested in identifying the determinants of trust and acceptability when considering the combination of hybrid human-AI approaches for improving research practices. This includes the way as researchers assume human-centered artificial intelligence (AI) and crowdsourcing as valid mechanisms for aiding their tasks. Through the lens of a unified theory of acceptance and use of technology (UTAUT) combined with an extended technology acceptance model (TAM), we pursue insights on the perceived usefulness, potential blockers, and adoption drivers that may be representative of the intention to use hybrid intelligence systems as a way of unveiling unknown patterns from large amounts of data and thus enabling novel scientific discoveries.

Abstract

Abstract
The usage of non-traditional data in credit scoring, from microfinance institutions, is very useful when trying to address the problem, very common in emerging markets, of the lack of a verifiable customers' credit history. In this context, this paper relies on data acquired from smartphones in a loan classification problem. We conduct a set of experiments concerning feature selection, strategies to deal with imbalanced datasets and algorithm choice, to define a baseline model. This model is, then, compared to others adding network features to the original ones. For that comparison, we generate a network that links a given user to its phone book contacts which are users of a given mobile application, taking into account the ethics and privacy concerns involved, and use some feature extraction techniques, such as the introduction of centrality measures and the definition of node embeddings, in order to capture certain aspects of the network's topology. Several node embedding algorithms are tested, but only Node2Vec proves to be significantly better than the baseline model, applying Friedman's post hoc tests. This node embedding algorithm outperforms all the other, representing a relative improvement, in comparison with the baseline model, of 5.74% on the mean accuracy, 7.13% on the area under the Receiver Operating Characteristic curve and 30.83% on the Kolmogorov-Smirnov statistic scores. This method, therefore, proves to be very promising when trying to discriminate between "good" and "bad" customers, in credit scoring classification problems.

Abstract
The tradition of wine production and consumption in Portugal is widely spread since the country presents climatic and territorial characteristics which have made wine-making an important strategic sector. In addition, the essence of the wine industry has led to greater tourism, thus enhancing the growth of enotourism. Given the importance of the wine production sector in the national context, as well as the potential of Industry 4.0 to stimulate improvements both in efficiency and competitiveness, the objective of this work is to achieve a better understanding of how Industry 4.0 and its key features, namely simulation, can influence wine production and enotourism. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Abstract
Grapes reception is a key process in wine production. The harvest days are extremely challenging days in managing the reception of the grapes, as the winery needs to deal with the non-uniform arrival of the grapes, while guaranteeing suppliers' satisfaction and wine quality. The best management of the resources of the suppliers (i.e., grapes and trucks) and winery (i.e., grain-tanks and pressing machines) must be ensured. In this paper, the underlying optimization problem for grape reception is solved by developing a genetic algorithm (GA) tailored for this specific challenge. The results of this algorithm are compared with a FIFO policy for a typical scenario that occurs on the harvest days of a real winery. Additionally, different scenarios are simulated to assess the validity and quality of the solutions found. The results show that, using modest computational resources, it is possible to achieve better solutions with the proposed GA. This allows for the algorithm to be used in real time, even whenever plant conditions change significantly (e.g., when a new truck arrives, when a machine fails). Furthermore, the trucks and grapes waiting time for the results using the developed GA are significantly smaller than the ones observed using a FIFO approach.