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Face verification approaches determine whether two given faces are from the same person. Recently, a new demand for face verification application which has become popular in commercial applications is the self-portrait and ID face matching, in which we compare the faces of a selfie shot by a subject and the face in a picture of her identification document. In this work, we proposed a novel approach for face verification in a cross-domain scenario, assuming we have only two images for each subject in the dataset. The method is based on siamese architecture with triplet-loss function. Experiments show the proposed model reaches good effectiveness for cross-domain face verification with low error rates, in comparison to other works of the literature.

Since the start of the COVID-19 pandemic many studies investigated the correlation between climate variables such as air quality, humidity and temperature and the lethality of COVID-19 around the world. In this work we investigate the use of climate variables, as additional features to train a data-driven multivariate forecast model to predict the short-term expected number of COVID-19 deaths in Brazilian states and major cities. The main idea is that by adding these climate features as inputs to the training of data-driven models, the predictive performance improves when compared to equivalent single input models. We use a Stacked LSTM as the network architecture for both the multivariate and univariate model. We compare both approaches by training forecast models for the COVID-19 deaths time series of the city of São Paulo. In addition, we present a previous analysis based on grouping K-means on AQI curves. The results produced will allow achieving the application of transfer learning, once a locality is eventually added to the task, regressing out using a model based on the cluster of similarities in the AQI curve. The experiments show that the best multivariate model is more skilled than the best standard data-driven univariate model that we could find, using as evaluation metrics the average fitting error, average forecast error, and the profile of the accumulated deaths for the forecast. These results show that by adding more useful features as input to a multivariate approach could further improve the quality of the prediction models.

The adverse effects of fine particulate matter (PM) and many volatile organic compounds (VOCs) on human health are well known. Fine particles are, in fact, those most capable of penetrating in depth into the respiratory system. People spend most of their time indoors where concentrations of some pollutants are sometimes higher than outdoors. Therefore, there is the need to ensure a healthy indoor environment and for this purpose the use of an air purifier can be a valuable aid especially now since it was demonstrated that indoor air quality has a high impact on spreading of viral infections such as that due to SARS-COVID19. In this study, we tested a commercial system that can be used as an air purifier. In particular it was verified its efficiency in reducing concentrations of PM10 (particles with aerodynamic diameter less than 10 μm), PM2.5 (particles with aerodynamic diameter less than 2.5 μm), PM1 (particles with aerodynamic diameter less than 1 μm), and particles number in the range 0.3 μm–10 μm. Furthermore, its capacity in reducing VOCs concentration was also checked. PM measurements were carried out by means of a portable optical particle counter (OPC) instrument simulating the working conditions typical of a household environment. In particular we showed that the tested air purifier significantly reduced both PM10 and PM2.5 by 16.8 and 7.25 times respectively that corresponds to a reduction of about 90% and 80%. A clear reduction of VOCs concentrations was also observed since a decrease of over 50% of these gaseous substances was achieved., SCOPUS: ar.j, info:eu-repo/semantics/published

The contribution of this paper is twofold. First, a new data driven approach for predicting the Covid-19 pandemic dynamics is introduced. The second contribution consists in reporting and discussing the results that were obtained with this approach for the Brazilian states, with predictions starting as of 4 May 2020. As a preliminary study, we first used an Long Short Term Memory for Data Training-SAE (LSTM-SAE) network model. Although this first approach led to somewhat disappointing results, it served as a good baseline for testing other ANN types. Subsequently, in order to identify relevant countries and regions to be used for training ANN models, we conduct a clustering of the world&rsquo, s regions where the pandemic is at an advanced stage. This clustering is based on manually engineered features representing a country&rsquo, s response to the early spread of the pandemic, and the different clusters obtained are used to select the relevant countries for training the models. The final models retained are Modified Auto-Encoder networks, that are trained on these clusters and learn to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks and number of confirmed cases. Finally, curve fitting is carried out to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Predicted numbers reach a total of more than one million infected Brazilians, distributed among the different states, with S&atilde, o Paulo leading with about 150 thousand confirmed cases predicted. The results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated in the second half of May 2020. The estimated end of the pandemics (97% of cases reaching an outcome) spread between June and the end of August 2020, depending on the states.

This paper has a twofold contribution. The first is a data driven approach for predicting the Covid-19 pandemic dynamics, based on data from more advanced countries. The second is to report and discuss the results obtained with this approach for Brazilian states, as of May 4th, 2020. We start by presenting preliminary results obtained by training an LSTM-SAE network, which are somewhat disappointing. Then, our main approach consists in an initial clustering of the world regions for which data is available and where the pandemic is at an advanced stage, based on a set of manually engineered features representing a country’s response to the early spread of the pandemic. A Modified Auto-Encoder network is then trained from these clusters and learns to predict future data for Brazilian states. These predictions are used to estimate important statistics about the disease, such as peaks. Finally, curve fitting is carried out on the predictions in order to find the distribution that best fits the outputs of the MAE, and to refine the estimates of the peaks of the pandemic. Results indicate that the pandemic is still growing in Brazil, with most states peaks of infection estimated between the 25th of April and the 19th of May 2020. Predicted numbers reach a total of 240 thousand infected Brazilians, distributed among the different states, with São Paulo leading with almost 65 thousand estimated, confirmed cases. The estimated end of the pandemics (with 97% of cases reaching an outcome) starts as of May 28th for some states and rests through August 14th, 2020.

This paper describes the implementation of a shift-register based Built-In Self-Test (BIST) architecture for FPGA global interconnection resources testing. Through this, it is possible to configure FPGA resources that need to be tested in order to obtain high reliability FPGA-based systems. The proposed BIST approach takes advantage of FPGA low-level resources in order to generate cyclic test patterns, analyse testing response and store test results in a simple way. Additionally, the same BIST configuration set is capable of diagnosing the tested interconnection resources with no additional configurations thereby reducing time requirements. This paper presents the proposed BIST architecture and its diagnosis scheme, its implementation on a Xilinx FPGA, and experimental results.

We propose an approach for the synchronization of video streams using correntropy. Essentially, the time offset is calculated on the basis of the instantaneous transfer rates of the video streams that are extracted in the form of a univariate signal known as variable bit-rate (VBR). The state-of-the-art approach uses a window segmentation strategy that is based on consensual zero-mean normalized cross-correlation (ZNCC). This strategy has an elevated computational complexity, making its application to synchronizing online data streaming difficult. Hence, our proposal uses a different window strategy that, together with the correntropy function, allows the synchronization to be performed for online applications. This provides equivalent synchronization scores with a rapid offset determination as the streams come into the system. The efficiency of our approach has been verified through experiments that demonstrate its viability with values that are as precise as those obtained by ZNCC. The proposed approach scored 81 % in time reference classification against the equivalent 81 % of the state-of-the-art approach, requiring much less computational power.