Abstract
Background: Over the past few years, various new types of technologies have been introduced, which have been tailored to meet the specific needs of older adults by incorporating gerontological design principles (i.e., "gerontechnologies"). However, it has been difficult to motivate older adults to adopt and use these new technologies. Therefore, it is crucial to better understand not only the role of personal drivers but also the family influences on older adults. Objective: This research goes beyond traditional technology acceptance theories by investigating the role of personal (e.g., inherent novelty seeking) and interpersonal drivers (e.g., influence of family) in stimulating older adults to use gerontechnologies. Nine hypotheses, building on traditional and new technology acceptance theories, were developed and tested. Methods: This research applies a cross-sectional study design. Therefore, a face-to-face survey instrument was developed building on a qualitative pilot study and validated scales. Three hundred and four older adults (minimum age = 70 years) were willing to participate as well as one of their family members. Structural equation modeling was applied to analyze the hypothesized conceptual model. Results: Our results extend the seminal technology acceptance theories by adding personal (i.e., inherent novelty seeking p = 0.017) and interpersonal drivers. More specifically, it was found that the attitude toward gerontechnologies was influenced by family tech savviness (i.e., people who often use technology), as this relationship is fully mediated through the social norms of older adults (p = 0.014). The same was found for older adults' trust in the family member's technology knowledge (p <= 0.001). Here, the relationship with older adults' attitude toward gerontechnologies was partially mediated by the older adults' trust in technology. Conclusion: This study identified important personal and interpersonal drivers that influence attitudes toward and intentions to use gerontechnologies. To foster technology acceptance among older adults, it was found that it is important to strengthen the trust in and the attitude toward gerontechnologies. Furthermore, family members' knowledge and beliefs in technology were the keys to promoting the actual use of gerontechnologies among older adults. Furthermore, the families' trust in gerontechnologies and the provision of access to technology can improve their attitudes toward technology and usage intentions for the older relative.

Abstract
The predawn leaf water potential (psi(pd)) is an eco-physiological indicator widely used for assessing vines water status and thus supporting irrigation management in several wine regions worldwide. However, the.pd is measured in a short time period before sunrise and the collection of a large sample of points is necessary to adequately represent a vineyard, which constitute operational constraints. In the present study, an alternative method based on hyperspectral data derived from a handheld spectroradiometer and machine learning algorithms was tested and validated for assessing grapevine water status. Two test sites in Douro wine region, integrating three grapevine cultivars, were studied for the years of 2014, 2015, and 2017. Four machine learning regression algorithms were tested for predicting the psi(pd) as a continuous variable, namely Random Forest (RF), Bagging Trees (BT), Gaussian Process Regression (GPR), and Variational Heteroscedastic Gaussian Process Regression (VH-GPR). Three predicting variables, including two vegetation indices (NRI554,561 and WI900,970) and a time-dynamic variable based on the psi(pd) (psi(pd_0)), were applied for modelling the response variable (psi(pd)). Additionally, the predicted values of psi(pd) were aggregated into three classes representing different levels of water deficit (low, moderate, and high) and compared with the corresponding classes of.pd observed values. A root mean square error (RMSE) and a mean absolute error (MAE) lower or equal than 0.15 MPa and 0.12 MPa, respectively, were obtained with an external validation data set (n= 71 observations) for the various algorithms. When the modelling results were assessed through classes of values, a high overall accuracy was obtained for all the algorithms (82-83%), with prediction accuracy by class ranging between 79% and 100%. These results show a good performance of the predictive models, which considered a large variability of climatic, environmental, and agronomic conditions, and included various grape cultivars. By predicting both continuous values of.pd and classes of psi(pd), the approach presented in this study allowed obtaining 2-levels of accurate information about vines water status, which can be used to feed management decisions of different types of stakeholders.

Abstract
Active learning (AL) methods create an optimized labeled training set from unlabeled data. We introduce a novel online active deep learning method for medical image analysis. We extend our MedAL AL framework to present new results in this paper. A novel sampling method queries the unlabeled examples that maximize the average distance to all training set examples. Our online method enhances performance of its underlying baseline deep network. These novelties contribute to significant performance improvements, including improving the model's underlying deep network accuracy by 6.30%, using only 25% of the labeled dataset to achieve baseline accuracy, reducing backpropagated images during training by as much as 67%, and demonstrating robustness to class imbalance in binary and multiclass tasks. This article is categorized under: Technologies > Machine Learning Technologies > Classification Application Areas > Health Care

Abstract
The industrial growth of the last decades created a need for intelligent and autonomous systems that can propose solutions to scheduling problems efficiently. The job shop scheduling problem (JSSP) is the most common formulation of these real-world scheduling problems and can be found in complex fields, such as transportation or industrial assemblies, where the ability to quickly adapt to unforeseen events is critical. Using the Markov decision process mathematical framework, this paper details a formulation of the JSSP as a reinforcement learning (RL) problem. The formulation is part of a proposal of a novel environment where RL agents can interact with JSSPs that is detailed on this paper, including a comprehensive explanation of the design process, the decisions that were made and the key lessons learnt. Considering the need for better scheduling approaches on modern manufacturing environments, the limitations that current techniques have and the major breakthroughs that are being made on the field of machine learning, the environment proposed on this paper intends to be a major contribution to the JSSP landscape, enabling academics from different areas to focus on the development of new algorithms and effortlessly test them on academic and real-world benchmarks. © 2020 MIR Labs.

Abstract
AbstractThe world is suffering from a pandemic called COVID-19, caused by the SARS-CoV-2 virus. The different national governments have problems evaluating the reach of the epidemic, having limited resources and tests at their disposal. Hence, any means to evaluate the number of persons with symptoms compatible with COVID-19 with reasonable level of accuracy is useful. In this paper we present the initial results of the @CoronaSurveys project. The objective of this project is the collection and publication of data concerning the number of people that show symptoms compatible with COVID-19 in different countries using open anonymous surveys. While this data may be biased, we conjecture that it is still useful to estimate the number of infected persons with the COVID-19 virus at a given point in time in these countries, and the evolution of this number over time. We show here the initial results of the @CoronaSurveys project in Spain and Portugal.

Abstract
Scientific impact is commonly associated with the number of citations received. However, an author can easily boost his own citation count by (i) publishing articles that cite his own previous work (self-citations), (ii) having co-authors citing his work (co-author citations), or (iii) exchanging citations with authors from other research groups (reciprocated citations). Even though these friendly citations inflate an author's perceived scientific impact, author ranking algorithms do not normally address them. They, at most, remove self-citations. Here we present Friends-Only Citations AnalySer (FOCAS), a method that identifies friendly citations and reduces their negative effect in author ranking algorithms. FOCAS combines the author citation network with the co-authorship network in order to measure author proximity and penalises citations between friendly authors. FOCAS is general and can be regarded as an independent module applied while running (any) PageRank-like author ranking algorithm. FOCAS can be tuned to use three different criteria, namely authors' distance, citation frequency, and citation recency, or combinations of these. We evaluate and compare FOCAS against eight state-of-the-art author ranking algorithms. We compare their rankings with a ground-truth of best paper awards. We test our hypothesis on a citation and co-authorship network comprised of seven Information Retrieval top-conferences. We observed that FOCAS improved author rankings by 25% on average and, in one case, leads to a gain of 46%.