Self-Assessment of Adherence to Medication: A Case Study in Campania Region Community-Dwelling Population.

Objectives. The aim of the study was to assess self-reported medication adherence measure in patients selected during a health education and health promotion focused event held in the Campania region. The study also assessed sociodemographic determinants of adherence. Methods. An interviewer assisted survey was conducted to assess adherence using the Italian version of the 8-item Morisky Medication Adherence Scale (MMAS-8). Participants older than 18 years were interviewed by pharmacists while waiting for free-medical checkup. Results. A total of 312 participants were interviewed during the Health Campus event. A total of 187 (59.9%) had low adherence to medications. Pearson's bivariate correlation showed positive association between the MMAS-8 score and gender, educational level and smoking (P < 0.05). A multivariable analysis showed that the level of education and smoking were independent predictors of adherence. Individuals with an average level of education (odds ratio (OR), 2.21, 95% confidence interval (CI), 1.08-4.52) and nonsmoker (odds ratio (OR) 1.87, 95% confidence interval (CI), 1.04-3.35) were found to be more adherent to medication than those with a lower level of education and smoking. Conclusion. The analysis showed very low prescription adherence levels in the interviewed population. The level of education was a relevant predictor associated with that result

On crowdsourcing relevance magnitudes for information retrieval evaluation

4siMagnitude estimation is a psychophysical scaling technique for the measurement of sensation, where observers assign numbers to stimuli in response to their perceived intensity. We investigate the use of magnitude estimation for judging the relevance of documents for information retrieval evaluation, carrying out a large-scale user study across 18 TREC topics and collecting over 50,000 magnitude estimation judgments using crowdsourcing. Our analysis shows that magnitude estimation judgments can be reliably collected using crowdsourcing, are competitive in terms of assessor cost, and are, on average, rank-aligned with ordinal judgments made by expert relevance assessors. We explore the application of magnitude estimation for IR evaluation, calibrating two gain-based effectiveness metrics, nDCG and ERR, directly from user-reported perceptions of relevance. A comparison of TREC system effectiveness rankings based on binary, ordinal, and magnitude estimation relevance shows substantial variation; in particular, the top systems ranked using magnitude estimation and ordinal judgments differ substantially. Analysis of the magnitude estimation scores shows that this effect is due in part to varying perceptions of relevance: different users have different perceptions of the impact of relative differences in document relevance. These results have direct implications for IR evaluation, suggesting that current assumptions about a single view of relevance being sufficient to represent a population of users are unlikely to hold.partially_openopenMaddalena, Eddy; Mizzaro, Stefano; Scholer, Falk; Turpin, AndrewMaddalena, Eddy; Mizzaro, Stefano; Scholer, Falk; Turpin, Andre

Background modeling for video sequences by stacked denoising autoencoders

Nowadays, the analysis and extraction of relevant information in visual data flows is of paramount importance. These images sequences can last for hours, which implies that the model must adapt to all kinds of circumstances so that the performance of the system does not decay over time. In this paper we propose a methodology for background modeling and foreground detection, whose main characteristic is its robustness against stationary noise. Thus, stacked denoising autoencoders are applied to generate a set of robust characteristics for each region or patch of the image, which will be the input of a probabilistic model to determine if that region is background or foreground. The evaluation of a set of heterogeneous sequences results in that, although our proposal is similar to the classical methods existing in the literature, the inclusion of noise in these sequences causes drastic performance drops in the competing methods, while in our case the performance stays or falls slightly.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Towards building a standard dataset for Arabic keyphrase extraction evaluation

Keyphrases are short phrases that best represent a document content. They can be useful in a variety of applications, including document summarization and retrieval models. In this paper, we introduce the first dataset of keyphrases for an Arabic document collection, obtained by means of crowdsourcing. We experimentally evaluate different crowdsourced answer aggregation strategies and validate their performances against expert annotations to evaluate the quality of our dataset. We report about our experimental results, the dataset features

Developing knowledge on languages and cultures in intercomprehensive multilingual chatrooms: which role in the enhancement of multilingual education?

Since Doyé’s Reference Study (2005), the concept of intercomprehension gained clear relevance in Language Education and the amount of publications, projects, networks, platforms are an evidence of that (Araújo e Sá, 2011). It became a complex concept which represents its richness and flexibility, but threatens the immediate understanding of its potential as a language education approach. Intercomprehension is aligned with the most recent language and education policies. However, it is still struggling to be recognized and operationalized in the educational context, due to its methodological inconsistency (De Carlo, 2013). Thus, the Référentiel de compétences de communication plurilingue en intercompréhension (REFIC) (De Carlo, 2015) was recently developed 1 This work is financially supported by National Funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., under the project UID/CED/00194/2013 and under a PhD grant SFRH/BD/103005/2014. and one of its aims is to allow a consistent evaluation of the plurilingual and intercultural communicative competences involved and developed in an intercomprehensive experience. In this study, an interactional perspective of intercomprehension is favoured. It is assumed that the interaction between students that live in different languages and cultures allows them to develop plurilingual and intercultural competences, which is intensified by new forms of communication associated to virtual mobility, such as chats, forums or blogs. In order to contribute to the piloting and further validation of REFIC, which is still in a conceptualized version, we will be analyzing the multilingual chats and forums from the session “Poliglotta? No, plurilingue!” from the Galanet platform using its categories. We will be focusing on a cognitive dimension and the research questions guiding this study will be: which knowledge on languages and cultures and how is it mobilized and developed in multilingual chatrooms in a pedagogical session on intercomprehension? The “Poliglotta? No, plurilingue!” session took place in 2014, between February 3rd and April 10th, and was attended by 168 Secondary School students from Spain, Italy, France and Portugal, whose participation is directly related and connected with their language learning at school. There were 7 multilingual chat sessions and 64 discussion forums, which will be analyzed bearing in mind the categories from “II. Les langues et les cultures” from REFIC. We expect to find evidence on the development of knowledge on different dimensions of language and culture, such as organization and social usage of a language and role of culture in communication. This approach puts multilingual virtual communication and interaction in the centre of learning, which is one of the trends in multilingual education that can only be fully recognized once a framework of competences in intercomprehension in validated and adopted

Multidimensional news quality: A comparison of crowdsourcing and nichesourcing

In the age of fake news and of filter bubbles, assessing the quality of information is a compelling issue: it is important for users to understand the quality of the information they consume online. We report on our experiment aimed at understanding if workers from the crowd can be a suitable alternative to

Background modeling by shifted tilings of stacked denoising autoencoders

The effective processing of visual data without interruption is currently of supreme importance. For that purpose, the analysis system must adapt to events that may affect the data quality and maintain its performance level over time. A methodology for background modeling and foreground detection, whose main characteristic is its robustness against stationary noise, is presented in the paper. The system is based on a stacked denoising autoencoder which extracts a set of significant features for each patch of several shifted tilings of the video frame. A probabilistic model for each patch is learned. The distinct patches which include a particular pixel are considered for that pixel classification. The experiments show that classical methods existing in the literature experience drastic performance drops when noise is present in the video sequences, whereas the proposed one seems to be slightly affected. This fact corroborates the idea of robustness of our proposal, in addition to its usefulness for the processing and analysis of continuous data during uninterrupted periods of time.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech