Abstract
Saliency maps have been used as one possibility to interpret deep neural networks. This method estimates the relevance of each pixel in the image classification, with higher values representing pixels which contribute positively to classification. The goal of this study is to understand how the complexity of the network affects the interpretabilty of the saliency maps in classification tasks. To achieve that, we investigate how changes in the regularization affects the saliency maps produced, and their fidelity to the overall classification process of the network. The experimental setup consists in the calculation of the fidelity of five saliency map methods that were compare, applying them to models trained on the CIFAR-10 dataset, using different levels of weight decay on some or all the layers. Achieved results show that models with lower regularization are statistically (significance of 5%) more interpretable than the other models. Also, regularization applied only to the higher convolutional layers or fully-connected layers produce saliency maps with more fidelity.

Abstract
Saliency maps have been used as one possibility to interpret deep neural networks. This method estimates the relevance of each pixel in the image classification, with higher values representing pixels which contribute positively to classification.The goal of this study is to understand how the complexity of the network affects the interpretabilty of the saliency maps in classification tasks. To achieve that, we investigate how changes in the regularization affects the saliency maps produced, and their fidelity to the overall classification process of the network.The experimental setup consists in the calculation of the fidelity of five saliency map methods that were compare, applying them to models trained on the CIFAR-10 dataset, using different levels of weight decay on some or all the layers.Achieved results show that models with lower regularization are statistically (significance of 5%) more interpretable than the other models. Also, regularization applied only to the higher convolutional layers or fully-connected layers produce saliency maps with more fidelity. © 2020 IEEE.

Abstract

Abstract
Cyber-security is the practice of protecting computing systems and networks from digital attacks, which are a rising concern in the Information Age. With the growing pace at which new attacks are developed, conventional signature based attack detection methods are often not enough, and machine learning poses as a potential solution. Adversarial machine learning is a research area that examines both the generation and detection of adversarial examples, which are inputs specially crafted to deceive classifiers, and has been extensively studied specifically in the area of image recognition, where minor modifications are performed on images that cause a classifier to produce incorrect predictions. However, in other fields, such as intrusion and malware detection, the exploration of such methods is still growing. The aim of this survey is to explore works that apply adversarial machine learning concepts to intrusion and malware detection scenarios. We concluded that a wide variety of attacks were tested and proven effective in malware and intrusion detection, although their practicality was not tested in intrusion scenarios. Adversarial defenses were substantially less explored, although their effectiveness was also proven at resisting adversarial attacks. We also concluded that, contrarily to malware scenarios, the variety of datasets in intrusion scenarios is still very small, with the most used dataset being greatly outdated.

Abstract
e13070 Background: Bone is one of the main sites of breast cancer metastasis. Staging of this kind of disease spread can be performed in locally advanced cases with PET-CT in conjunction with Bone Scintigraphy. The purpose of this work is to compare the efficiency of bone metastasis detection between PET-CT and bone scintigraphy. Methods: Prospective analysis of locally advanced breast cancer patients treated in a Comprehensive Cancer Center between 2014 and 2019 that performed PET-CT and Bone Scintigraphy in the staging. Interval between the two exams could not exceed 2 months. Clinical and pathological characteristics of the disease were collected from electronic files and independently clinical images reports were considered to evaluate the ability of each imaging modalities to identify bone disease. In discrepancy cases a re-analysis of the images by two independent nuclear physicians was performed to validate the findings. Results: We analyzed 204 cases. The majority of them had ductal carcinomas (72.5%), cT2/3 (70%), cN1/2(61.8%) and G2/3 (94.6%), luminal B- like, HER2 positive disease (49.2%). In this cohort, bone metastasis was documented in 52 (25.5%) patients. PET-CT presented 97.0% of accuracy, surpassing the 94.1% presented by Bone Scintigraphy. The latter failed to correctly detect bone metastasis in 11 (5.4%) patients and only outperformed PET-CT in 3 (1.5%) patients. The main difference between the two modalities was the non-detection of cranium lesions in PET-CT images. Conclusions: PET-CT showed higher efficiency in bone metastasis detection than Bone Scintigraphy, probably because it detects lytic lesions. The non-detection of cranium ones can be harmful and so modifications in the image acquisition are required to improve the quality of PET-CT, avoiding other exams in bone staging.

Abstract