MOTIVATION: There is a need for easily accessible implementations that measure the strength of both linear and non-linear relationships between metabolites in biological systems as an approach for data-driven network development. While multiple tools implement linear Pearson and Spearman methods, there are no such tools that assess distance correlation. RESULTS: We present here SIgned Distance COrrelation (SiDCo). SiDCo is a GUI platform for calculation of distance correlation in omics data, measuring linear and non-linear dependencies between variables, as well as correlation between vectors of different lengths, e.g. different sample sizes. By combining the sign of the overall trend from Pearson's correlation with distance correlation values, we further provide a novel "signed distance correlation" of particular use in metabolomic and lipidomic analyses. Distance correlations can be selected as one-to-one or one-to-all correlations, showing relationships between each feature and all other features one at a time or in combination. Additionally, we implement "partial distance correlation," calculated using the Gaussian Graphical model approach adapted to distance covariance. Our platform provides an easy-to-use software implementation that can be applied to the investigation of any dataset. AVAILABILITY AND IMPLEMENTATION: The SiDCo software application is freely available at https://complimet.ca/sidco. Supplementary help pages are provided at https://complimet.ca/sidco. Supplementary Material shows an example of an application of SiDCo in metabolomics.

Machine learning (ML) models are vulnerable to adversarial machine learning (AML) attacks. Unlike other contexts, the fraud detection domain is characterized by inherent challenges that make conventional approaches hardly applicable. In this article, we extend the application of AML techniques to the fraud detection task by studying poisoning attacks and their possible countermeasures. First, we present a novel approach for performing poisoning attacks that overcomes the fraud detection domain-specific constraints. It generates fraudulent candidate transactions and tests them against a machine learning-based Oracle , which simulates the target fraud detection system aiming at evading it. Misclassified fraudulent candidate transactions are then integrated into the target detection system’s training set, poisoning its model and shifting its decision boundary. Second, we propose a novel approach that extends the adversarial training technique to mitigate AML attacks: During the training phase of the detection system, we generate artificial frauds by modifying random original legitimate transactions; then, we include them in the training set with the correct label. By doing so, we instruct our model to recognize evasive transactions before an attack occurs. Using two real bank datasets, we evaluate the security of several state-of-the-art fraud detection systems by deploying our poisoning attack with different degrees of attacker’s knowledge and attacking strategies. The experimental results show that our attack works even when the attacker has minimal knowledge of the target system. Then, we demonstrate that the proposed countermeasure can mitigate adversarial attacks by reducing the stolen amount of money up to 100%.

In this work, the problem of the estimation of parameters in case of mixtures of models composed of the sum of multiple Gaussians is considered. It will be shown how this estimation can be performed efficiently by using the Generalized Hough Transform (GHT). The theoretical results will be applied to a corner-based object tracking application considering, in particular, the case of two or more objects that come into proximity and occlude each other. Quantitative results show the performances of the derived algorithm both on synthetically generated data and real tracking sequences.

Even if the problem of human action categorization from videos has received a lot of attention during the past decade, it remains a challenging problem in operative conditions due to camera motion, occlusion, moving background, illumination changes and the variations of human appearance and postures. In this paper a new motion descriptor, based on a sparse optical flow computed by interest point tracking is presented. This motion descriptor is by design invariant to scale, camera motion and is not affected by non stationary background. The results of the recognition method are computed using a standard database and are compared to other approaches in literature.