Exploring the Space of Adversarial Images

Adversarial examples have raised questions regarding the robustness and security of deep neural networks. In this work we formalize the problem of adversarial images given a pretrained classifier, showing that even in the linear case the resulting optimization problem is nonconvex. We generate adversarial images using shallow and deep classifiers on the MNIST and ImageNet datasets. We probe the pixel space of adversarial images using noise of varying intensity and distribution. We bring novel visualizations that showcase the phenomenon and its high variability. We show that adversarial images appear in large regions in the pixel space, but that, for the same task, a shallow classifier seems more robust to adversarial images than a deep convolutional network.Comment: Copyright 2016 IEEE. This manuscript was accepted at the IEEE International Joint Conference on Neural Networks (IJCNN) 2016. We will link the published version as soon as the DOI is availabl

A Broad Class of Discrete-Time Hypercomplex-Valued Hopfield Neural Networks

In this paper, we address the stability of a broad class of discrete-time hypercomplex-valued Hopfield-type neural networks. To ensure the neural networks belonging to this class always settle down at a stationary state, we introduce novel hypercomplex number systems referred to as real-part associative hypercomplex number systems. Real-part associative hypercomplex number systems generalize the well-known Cayley-Dickson algebras and real Clifford algebras and include the systems of real numbers, complex numbers, dual numbers, hyperbolic numbers, quaternions, tessarines, and octonions as particular instances. Apart from the novel hypercomplex number systems, we introduce a family of hypercomplex-valued activation functions called $\mathcal{B}$-projection functions. Broadly speaking, a $\mathcal{B}$-projection function projects the activation potential onto the set of all possible states of a hypercomplex-valued neuron. Using the theory presented in this paper, we confirm the stability analysis of several discrete-time hypercomplex-valued Hopfield-type neural networks from the literature. Moreover, we introduce and provide the stability analysis of a general class of Hopfield-type neural networks on Cayley-Dickson algebras

Data Augmentation for Skin Lesion Analysis

Deep learning models show remarkable results in automated skin lesion analysis. However, these models demand considerable amounts of data, while the availability of annotated skin lesion images is often limited. Data augmentation can expand the training dataset by transforming input images. In this work, we investigate the impact of 13 data augmentation scenarios for melanoma classification trained on three CNNs (Inception-v4, ResNet, and DenseNet). Scenarios include traditional color and geometric transforms, and more unusual augmentations such as elastic transforms, random erasing and a novel augmentation that mixes different lesions. We also explore the use of data augmentation at test-time and the impact of data augmentation on various dataset sizes. Our results confirm the importance of data augmentation in both training and testing and show that it can lead to more performance gains than obtaining new images. The best scenario results in an AUC of 0.882 for melanoma classification without using external data, outperforming the top-ranked submission (0.874) for the ISIC Challenge 2017, which was trained with additional data.Comment: 8 pages, 3 figures, to be presented on ISIC Skin Image Analysis Worksho

Implementación de la plataforma “evolution”

A call center is a business unit whose epicenter is the people and where the focus lies in successful communication. We live in an era when technology is revolutionizing communications, and most successful call centers use a wide range of applications which range from PBX and Automatic Call Distribution Systems (ACD) that route calls, answer Audio Systems (IVR) and voice mail systems that provide an automated response capability and self-service; systems with Relationship Management (CRM), that the agents use to access and work with customer records and a range of reporting systems, supervision and quality monitoring; that managers can use to manage the operation of call center and measure its performance. The reason that we chose to implement this new platform, was the current need to expand the company has operations in the Ecuadorian market. In the particular case of the company, within a wide range of possibilities it was decided by the implementation of Call Center Evolution of minimum costs to be incurred, as the company has all required hardware, database licenses data telecommunications infrastructure for the marking of the calls. Other applications are used for implementation are licensed under GP