RESUMEN
Social networks are increasingly used for discussing all kinds of topics, including those related to politics, serving as a virtual arena. Consequently, analysing online conversations, for example, to predict election outcomes, is becoming a popular and challenging research area. On social networking sites, citizens express themselves spontaneously regarding political topics, often driven by specific events in social life. Real-time analysis of social media can provide valuable feedback and insights to both politicians and news agencies. In this paper, we discuss the design and implementation of a system for tracking and analysing social media. The SocMINT system provides an easy-to-use, visual dashboard to monitor the discussion on specific topics, to capture trends in communities and, by iteratively applying multidimensional data analysis and filtering, to deeply analyse posts and influencers. SocMINT aggregates data from multiple social sources and performs sentiment analysis on textual, visual and mixed content via a specifically designed neural network architecture. The system was applied in a real context by administrative staff of a political party to effectively analyse candidates' political communication on Facebook, Instagram and Twitter and the related online community reactions and discussion. In the paper, we report on this real-world case study, showing how the system meaningfully captures trends in public opinion, comparing the main KPIs provided by SocMINT with the outcomes of traditional polls.
RESUMEN
In the Cultural Heritage (CH) context, art galleries and museums employ technology devices to enhance and personalise the museum visit experience. However, the most challenging aspect is to determine what the visitor is interested in. In this work, a novel Visual Attentive Model (VAM) has been proposed that is learned from eye tracking data. In particular, eye-tracking data of adults and children observing five paintings with similar characteristics have been collected. The images are selected by CH experts and are-the three "Ideal Cities" (Urbino, Baltimore and Berlin), the Inlaid chest in the National Gallery of Marche and Wooden panel in the "Studiolo del Duca" with Marche view. These pictures have been recognized by experts as having analogous features thus providing coherent visual stimuli. Our proposed method combines a new coordinates representation from eye sequences by using Geometric Algebra with a deep learning model for automated recognition (to identify, differentiate, or authenticate individuals) of people by the attention focus of distinctive eye movement patterns. The experiments were conducted by comparing five Deep Convolutional Neural Networks (DCNNs), yield high accuracy (more than 80 %), demonstrating the effectiveness and suitability of the proposed approach in identifying adults and children as museums' visitors.
RESUMEN
BACKGROUND: Diabetic nephropathy may be related to an abnormal metabolism of glycosaminoglycans (GAG) in the glomerular basement membrane (GBM). The first manifestation of nephropathy is microalbuminuria, whose appearance indicates a loss of GBM selectivity. The present study evaluated whether GAG excretion becomes abnormal in parallel with microalbuminuria, and whether such abnormalities are also present in normoalbuminuric diabetic patients. METHODS: We measured urinary GAG excretion in 60 patients with type 1 (insulin-dependent) diabetes mellitus and in 22 healthy subjects. GAG were isolated from 24-h urine using ion-exchange chromatography on DEAE Sephacel. GAG composition was determined by cellulose acetate electrophoresis and expressed as percentages by densitometric scanning of Alcian Blue stained strips. RESULTS: On subgrouping for albuminuric status and glyco-metabolic control, we found high urinary GAG concentrations in all except the normoalbuminuric patients with good glyco-metabolic control. The urinary GAG electrophoretic pattern showed alterations in chondroitin sulphate (CS) and heparan sulphate (HS) relative contents. A higher frequency of low sulphated chondroitin sulphate-proteoglycan (LSC-PG) was observed in all patients, including those with normoalbuminuria and good glyco-metabolic control. CONCLUSIONS: This urinary pattern may be indicative of an abnormal GBM metabolism. Since GAG play an important role in GBM permeability, these anomalies might consequently represent a first step towards selective changes of GBM in type 1 diabetes mellitus.