Heart tissue repair and cardioprotection using drug delivery systems.

The capacity of the heart to heal after a myocardial infarction is not enough to restore normal cardiac function. Fortunately, delivery of therapeutics such as stem cells, growth factors, exosomes and small interfering ribonucleic acid (siRNA), among other bioactive molecules, has been shown to enhance heart repair and improve cardiac function. Furthermore, new delivery systems for these therapeutic agents have enhanced their regenerative and cardioprotective potential.

Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems.

Prior work has shown that multibiometric systems are vulnerable to presentation attacks, assuming that their matching score distribution is identical to that of genuine users, without fabricating any fake trait. We have recently shown that this assumption is not representative of current fingerprint and face presentation attacks, leading one to overestimate the vulnerability of multibiometric systems, and to design less effective fusion rules. In this paper, we overcome these limitations by proposing a statistical meta-model of face and fingerprint presentation attacks that characterizes a wider family of fake score distributions, including distributions of known and, potentially, unknown attacks.

Randomized Prediction Games for Adversarial Machine Learning.

In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits.

On Reducing the Effect of Covariate Factors in Gait Recognition: A Classifier Ensemble Method.

Robust human gait recognition is challenging because of the presence of covariate factors such as carrying condition, clothing, walking surface, etc. In this paper, we model the effect of covariates as an unknown partial feature corruption problem. Since the locations of corruptions may differ for different query gaits, relevant features may become irrelevant when walking condition changes.

Adversarial Feature Selection Against Evasion Attacks.

Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion, and malware detection, although their security against well-crafted attacks that aim to evade detection by manipulating data at test time has not yet been thoroughly assessed. While previous work has been mainly focused on devising adversary-aware classification algorithms to counter evasion attempts, only few authors have considered the impact of using reduced feature sets on classifier security against the same attacks. An interesting, preliminary result is that classifier security to evasion may be even worsened by the application of feature selection.

Diversified Sensitivity-Based Undersampling for Imbalance Classification Problems.

Undersampling is a widely adopted method to deal with imbalance pattern classification problems. Current methods mainly depend on either random resampling on the majority class or resampling at the decision boundary. Random-based undersampling fails to take into consideration informative samples in the data while resampling at the decision boundary is sensitive to class overlapping.

A theoretical and experimental analysis of linear combiners for multiple classifier systems.

In this paper, a theoretical and experimental analysis of linear combiners for multiple classifier systems is presented. Although linear combiners are the most frequently used combining rules, many important issues related to their operation for pattern classification tasks lack a theoretical basis. After a critical review of the framework developed in works by Tumer and Ghosh on which our analysis is based, we focus on the simplest and most widely used implementation of linear combiners, which consists of assigning a nonnegative weight to each individual classifier.