Abstract
Presentation attack detection (PAD) methods are commonly evaluated using metrics based on the predicted labels. This is a limitation, especially for more elusive methods based on deep learning which can freely learn the most suitable features. Though often being more accurate, these models operate as complex black boxes which makes the inner processes that sustain their predictions still baffling. Interpretability tools are now being used to delve deeper into the operation of machine learning methods, especially artificial networks, to better understand how they reach their decisions. In this paper, we make a case for the integration of interpretability tools in the evaluation of PAD. A simple model for face PAD, based on convolutional neural networks, was implemented and evaluated using both traditional metrics (APCER, BPCER and EER) and interpretability tools (Grad-CAM), using data from the ROSE Youtu video collection. The results show that interpretability tools can capture more completely the intricate behavior of the implemented model, and enable the identification of certain properties that should be verified by a PAD method that is robust, coherent, meaningful, and can adequately generalize to unseen data and attacks. One can conclude that, with further efforts devoted towards higher objectivity in interpretability, this can be the key to obtain deeper and more thorough PAD performance evaluation setups.

Abstract
Objective: Frailty, a state of decreased physiological reserve, increases the risk of adverse outcomes. There is no standard tool for frailty during perioperative period. Autonomic dysfunction, an underlying process in frailty, could result in hemodynamic fluctuations. Complexity, the physiological adaptability of a system can quantify these fluctuations. The authors hypothesized that complexity could be a marker for frailty and explored their relationship in cardiac surgical patients. Design: Prospective, observational study. Setting: Single-center teaching hospital. Participants: Three hundred and sixty-four adult patients undergoing cardiac surgery. Intervention: None. Measurements and Main Results: Preoperative beat-to-beat systolic arterial pressure (SAP) and mean arterial pressure (MAP) time series were obtained. Complexity indices were calculated using multiscale entropy (MSE) analysis. Frailty was assessed from: age >70 years, body mass index <18.5, hematocrit <35%, albumin <3.4 g/dL, and creatinine >2.0 mg/dL. The association between complexity indices and frailty was explored by logistic regression and predictive ability by C-statistics. In total, 190 (52%) patients had frailty. The complexity index (MSED median (quartile 1, quartile 3) of SAP and MAP time series decreased significantly in frail patients (SAP: 8.32 [7.27, 9.24] v 9.13 [8.00, 9.72], p < 0.001 and MAP: 8.56 [7.56; 9.27] v 9.18 [8.26; 9.83], p < 0.001). MSE (Sigma) demonstrated a fair predictive ability of frailty (C-statistic: SAP 0.62 and MAP 0.64). Conclusion: Preoperative BP complexity indices correlate and predict frailty. Impaired autonomic control is the underlying mechanism to explain this finding. A simple automated measure of preoperative BP complexity in the surgeon's office has the potential to reliably assess frailty.

Abstract
Individual tree segmentation is a challenging task due to the labour-intensive and time-consuming work required. Remote sensing data acquired from sensors coupled in unmanned aerial vehicles (UAV) constitutes a viable alternative to provide a quicker data acquisition, covering broader areas in a shorter period of time. This study aims to use UAV-based multispectral imagery to automatically identify individual trees in a chestnut stand. Tree parameters were estimated allowing its characterization. The leaf area index (LAI) was measured and was correlated with the estimated parameters. A good correlation was found for NDVI (R-2 = 0.76), while this relationship was less evident in the tree crown area and tree height. This way, our results indicate that the use of UAV-based multispectral imagery is a quick and reliable way to determine canopy structural parameters and LAI of chestnut trees.

Abstract
Audio content-based processing has become a pervasive methodology for techno-fluent musicians. System architectures typically create thumbnail audio descriptions, based on signal processing methods, to visualize, retrieve and transform musical audio efficiently. Towards enhanced usability of these descriptor-based frameworks for the music community, the paper advances a minimal content-based audio description scheme, rooted on primary musical notation attributes at the threefold sound object, meso and macro hierarchies. Multiple perceptually-guided viewpoints from rhythmic, harmonic, timbral and dynamic attributes define a discrete and finite alphabet with minimal formal and subjective assumptions using unsupervised and user-guided methods. The Factor Oracle automaton is then adopted to model and visualize temporal morphology. The generative musical applications enabled by the descriptor-based framework at multiple structural hierarchies are discussed. © 2020, Steering Committee of the International Conference on New Interfaces for Musical Expression. All rights reserved.

Abstract
Agricultural robotics is nowadays a complex, challenging, and exciting research topic. Some agricultural environments present harsh conditions to robotics operability. In the case of steep slope vineyards, there are several challenges: terrain irregularities, characteristics of illumination, and inaccuracy/unavailability of signals emitted by the Global Navigation Satellite System (GNSS). Under these conditions, robotics navigation becomes a challenging task. To perform these tasks safely and accurately, the extraction of reliable features or landmarks from the surrounding environment is crucial. This work intends to solve this issue, performing accurate, cheap, and fast landmark extraction in steep slope vineyard context. To do so, we used a single camera and an Edge Tensor Processing Unit (TPU) provided by Google & x2019;s USB Accelerator as a small, high-performance, and low power unit suitable for image classification, object detection, and semantic segmentation. The proposed approach performs object detection using Deep Learning (DL)-based Neural Network (NN) models on this device to detect vine trunks. To train the models, Transfer Learning (TL) is used on several pre-trained versions of MobileNet V1 and MobileNet V2. A benchmark between the two models and the different pre-trained versions is performed. The models are pre-trained in a built in-house dataset, that is publicly available containing 336 different images with approximately 1,600 annotated vine trunks. There are considered two vineyards, one using camera images with the conventional infrared filter and others with an infrablue filter. Results show that this configuration allows a fast vine trunk detection, with MobileNet V2 being the most accurate retrained detector, achieving an overall Average Precision of 52.98 & x0025;. We briefly compare the proposed approach with the state-of-the-art Tiny YOLO-V3 running on Jetson TX2, showing the outperformance of the adopted system in this work. Additionally, it is also shown that the proposed detectors are suitable for the Localization and Mapping problems.

Abstract
Governance has a significant impact on the success of programs and projects. However, governance of collaborative university-industry projects and programs in literature, is a rather scarce topic. Based on an ethnographic study of a large university-industry collaboration, this paper proposes a conceptual framework of Organizational Enablers (OEs) to improve the governance of collaborative university-industry RD programs. An exploratory research was carried out, aiming to learn from the experience of program and project managers and other program stakeholders of the case under study. Qualitative data was collected using participant observation and document analysis. The framework highlights nine OEs: 'Established governance policies and values', 'Formal Governance support structures', 'Flexible organization structures', 'Standardization of program and project management practices', 'Different management approaches to fit the project needs', 'Clearly defined roles and responsibilities', 'Different means of communication and interaction', 'Top management Support' and 'Projects strategic alignment within the industry and university roadmaps'. © 2020 IEEE.