1,720,973 research outputs found
Specie vegetali selvatiche e coltivate di uso medicinale nella tradizione popolare delle Apuane settentrionali.
No full textMapping and demography of endangered plants in Apuan Alps NW Tuscany, Italy. Bocconea 21:27-44
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Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Schede per una Lista Rossa della Flora vascolare e crittogamica italiana. Athamanta cortiana Ferrarini
No full textCNN-Based Camera-less User Attention Detection for Smartphone Power Management
Get PDFThe many sensors hosted by mobile electronic devices are commonly used to recognize user activities and context, in order to provide new functionalities, such as tracking physical activity and sleep cycles. Despite its potential, such context recognition is only employed for power management purposes in very specific scenarios (e.g. in-pocket detection). In this work we present a novel context recognition system able to reliably identify whether a mobile device is not being looked at, and to consequently trigger power management actions such as turning off the display and moving to suspended mode. Our method takes as input the readings from common low-power sensors present in virtually all mobile devices and classifies them using a Convolutional Neural Network. Most importantly, the power-hungry camera sub-system is not used, resulting in an extremely energy-efficient detection strategy. Results show that our system is able to identify scenarios in which a device is not being used with 95.6% accuracy, thus reducing the energy overheads by 91% compared to a standard timeout-based power management and by 58% compared to a system relying on the camera
Fitogeografia apuana. Il genere Saxifraga sulle Alpi Apuane: status delle conoscenze e aspetti di conservazione
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