Graphical User Interface (GUI) component recognition is one of the most widely used services in mobile test automation. Several industrial and academic solutions are available for GUI component identification. However, the state-of-the-art works are still limited respecting the variety of widgets that can be recognized. Furthermore, most solutions are restricted to widget classification. In this work, we propose SWR, a fully Computer Vision-based method for Smart Widget Recognition. It can recognize 106 GUI component categories including 97 icon classes. Our main contribution is that SWR can generate a detailed description for each identified widget regardless of the component class.

BACKGROUND: Dengue is a common vector-borne disease in tropical countries caused by the Dengue virus. This virus may trigger a disease with several symptoms like fever, headache, nausea, vomiting, and muscle pain. Indeed, dengue illness may also present more severe and life-threatening conditions like hemorrhagic fever and dengue shock syndrome. The causes that lead hosts to develop severe infections are multifactorial and not fully understood. However, it is hypothesized that different viral genome signatures may partially contribute to the disease outcome. Therefore, it is plausible to suggest that deeper DENV genetic information analysis may bring new clues about genetic markers linked to severe illness. METHOD: Pattern recognition in very long protein sequences is a challenge. To overcome this difficulty, we map protein chains onto matrix data structures that reveal patterns and allow us to classify dengue proteins associated with severe illness outcomes in human hosts. Our analysis uses co-occurrence of amino acids to build the matrices and Random Forests to classify them. We then interpret the classification model using SHAP Values to identify which amino acid co-occurrences increase the likelihood of severe outcomes. RESULTS: We trained ten binary classifiers, one for each dengue virus protein sequence. We assessed the classifier performance through five metrics: PR-AUC, ROC-AUC, F1-score, Precision and Recall. The highest score on all metrics corresponds to the protein E with a 95% confidence interval. We also compared the means of the classification metrics using the Tukey HSD statistical test. In four of five metrics, protein E was statistically different from proteins M, NS1, NS2A, NS2B, NS3, NS4A, NS4B and NS5, showing that E markers has a greater chance to be associated with severe dengue. Furthermore, the amino acid co-occurrence matrix highlight pairs of amino acids within Domain 1 of E protein that may be associated with the classification result. CONCLUSION: We show the co-occurrence patterns of amino acids present in the protein sequences that most correlate with severe dengue. This evidence, used by the classification model and verified by statistical tests, mainly associates the E protein with the severe outcome of dengue in human hosts. In addition, we present information suggesting that patterns associated with such severe cases can be found mostly in Domain 1, inside protein E. Altogether, our results may aid in developing new treatments and being the target of debate on new theories regarding the infection caused by dengue in human hosts.

The Amazon Rainforest degradation is a worldwide concern. The rainforest has been endangered by the illegal wood extraction without control even in the preservation areas. Due to the large geography extension prevent these crimes with an unmanned aerial vehicle (UAV) is not always possible. The Wireless Acoustics Sensor Network (WASNs) technology can alleviate this problem. Here, we present an acoustical framework to detect the sounds produced by several chainsaws. Our framework was developed to be embedded in a sensor node, combining the Mel-Fourier Cepstral Coefficients (MFCCs) with One-class classification technique. This classification method, that is based on a kernel density approach, allows us to recognize only chainsaw sounds rejecting all the other possible environmental sounds, such as: animal's calls, weather noises or boat engines. In the experiments, we varied the number MFCCs coefficients and the Kernel bandwidth performing a leave-one-out cross validation to find the best combination. Finally, we found that the best parameter combination achieve 98% of accuracy showing a low FNR and a high TPR, fact that enhances the credibility of the system avoiding false alarms and making it an optimal choice for an WSN application.

In this work, we evaluate the performance of a distributed classification system in a Wireless Sensor Network for monitoring anurans. Our aim is to study how to take advantage of the collaborative nature of the sensor network to improve the recognition of anuran calls. To accomplish this, we evaluate four low-cost techniques (majority vote, weighted majority vote, arithmetic and geometric combinators) to combine three classifiers commonly used in sensor applications (Quadratic Discriminant Analysis, Naive Bayes, and Decision Trees) and trained to identify anuran calls. We investigate how the environment perceptions of the sensors can be used to discard confusing scenarios, i.e., scenarios in which there are multiple calls from different species at same time. Our best combination strategy achieved a gain of about 11% over a sensor taken in isolation. We also found that, by using the entropy of the species estimates, the sensor committee is able to effectively identify confusing scenarios, increasing gains over the isolated sensor to about 20%.

Wildlife sounds provide relevant information for non-intrusive environmental monitoring when Wireless Sensor Networks (WSNs) are used. Thus, collecting such audio data, while maximizing the network lifetime, is a key challenge for WSNs. In this work, we propose a methodology that applies Compressive Sensing (CS) aiming at collecting as little data as possible to allow the signal reconstruction, so that the reconstructed signal is still representative. The key issue is to determine a sparse base that best represents the audio information used for identifying the target species. As a proof-of-concept, we focus on anuran (frogs and toads) calls, but the methodology can be applied for other animal families and species. The reason for that choice is that long-term anuran monitoring has been used by biologists as an early indicator for ecological stress. By using real wild anuran calls, we show that 98% classification rate can be achieved by using as little as 10% of the original data. We also use simulation to evaluate the impact of our solution on the network performance (energy consumption, delivery rate, and network delay).

Anurans (frogs or toads) are commonly used by biologists as early indicators of ecological stress. The reason is that anurans are closely related to the ecosystem. Although several sources of data may be used for monitoring these animals, anuran calls lead to a non-intrusive data acquisition strategy. Moreover, wireless sensor networks (WSNs) may be used for such a task, resulting in more accurate and autonomous system. However, it is essential save resources to extend the network lifetime. In this paper, we evaluate the impact of reducing data dimension for automatic classification of bioacoustic signals when a WSN is involved. Such a reduction is achieved through a wrapper-based feature subset selection strategy that uses genetic algorithm (GA). We use GA to find the subset of features that maximizes the cost-benefit ratio. In addition, we evaluate the impact of reducing the original feature space, when sampling frequencies are also reduced. Experimental results indicate that we can reduce the number of features, while increasing classification rates (even when smaller sampling frequencies of transmission are used).

Wireless Sensor Networks consist of a powerful technology for monitoring the physical world. Particularly, in-network data fusion techniques are very important to applications such as target classification and tracking to reduce the communication burden in these constrained networks. However, the efficiency of the solution can be affected by the data correlation among several sensor nodes. Thus, the application of value fusion (for clusters of nodes with correlated measurements) and decision fusion (combining the local decisions of the clusters) is a common strategy. In this work, we propose an algorithm for properly selecting the groups of nodes with correlated measurements. Experiments show that our algorithm is 30% better than a solution that considers only the spatial coherence regions.