Abstract
The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms' ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology's performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario.

Abstract
A recent relevant innovation in last-mile delivery is to consider the possibility of goods being delivered by couriers appointed through crowdsourcing. In this paper we focus on the setting of in-store customers delivering goods, ordered by online customers, on their way home. We assume that not all the proposed delivery tasks will necessarily be accepted, and use logistic regression to model the crowd agents' willingness to undertake a delivery. This model is then used to build a novel compensation scheme that determines reward values, based on the current plan for the professional fleet's routes and on the couriers' probabilities of acceptance, by employing a direct search algorithm that seeks to minimise the expected cost.

Abstract
Purpose: Responsible innovation assessment tools (RIATs) are key instruments that can help organizations, associations and individuals measure responsible innovation. Accordingly, this study aims to review the current status of research on responsible innovation and, in particular, of studies that either present the relevance of RIATs or provide empirical evidence of their adoption. Design/methodology/approach: A systematic literature review is conducted to identify and review how RIATs are being addressed in academic research and the applications that are proposed. A systematic process is implemented using the Web of Science and Scopus bibliographic databases, aiming not only to summarize existing studies, but also to include a perspective on gaps and future research. Findings: A total of 119 publications were identified and included in the review process. The study identifies that RIATs have attracted growing interest from the scientific community, with a greater predominance of studies involving qualitative and mixed methods. A well-balanced mix of conceptual and exploratory studies is also registered, with a greater predominance of analysis of RIATs application domains in the past years, with greater incidence in the finance, water, energy, construction, manufacturing and health sectors. Originality/value: This study is pioneering in identifying 16 dimensions and 60 sub-dimensions for measuring responsible innovation. It also suggests the need to include multidimensional perspectives and individuals with interdisciplinary competencies in this process.

Abstract
The lack of interpretability of Deep Learning models hinders their deployment in clinical contexts. Case-based explanations can be used to justify these models' decisions and improve their trustworthiness. However, providing medical cases as explanations may threaten the privacy of patients. We propose a generative adversarial network to disentangle identity and medical features from images. Using this network, we can alter the identity of an image to anonymize it while preserving relevant explanatory features. As a proof of concept, we apply the proposed model to biometric and medical datasets, demonstrating its capacity to anonymize medical images while preserving explanatory evidence and a reasonable level of intelligibility. Finally, we demonstrate that the model is inherently capable of generating counterfactual explanations.

Abstract

Abstract
In this paper we study the heart sound segmentation problem using Deep Neural Networks. The impact of available electrocardiogram (ECG) signals in addition to phonocardiogram (PCG) signals is evaluated. To incorporate ECG, two different models considered, which are built upon a 1D U-net - an early fusion one that fuses ECG in an early processing stage, and a late fusion one that averages the probabilities obtained by two networks applied independently on PCG and ECG data. Results show that, in contrast with traditional uses of ECG for PCG gating, early fusion of PCG and ECG information can provide more robust heart sound segmentation. As a proof of concept, we use the publicly available PhysioNet dataset. Validation results provide, on average, a sensitivity of 97.2%, 94.5%, and 95.6% and a Positive Predictive Value of 97.5%, 96.2%, and 96.1% for Early-fusion, Late-fusion, and unimodal (PCG only) models, respectively, showing the advantages of combining both signals at early stages to segment heart sounds.