Abstract

Abstract
The privacy of information is an increasing concern of software applications users. This concern was caused by attacks to cloud services over the last few years, that have leaked confidential information such as passwords, emails and even private pictures. Once the information is leaked, the users and software applications are powerless to contain the spread of information and its misuse. With databases as a central component of applications that store almost all of their data, they are one of the most common targets of attacks. However, typical deployments of databases do not leverage security mechanisms to stop attacks and do not apply cryptographic schemes to protect data. This issue has been tackled by multiple secure databases that provide trade-offs between security, query capabilities and performance. Despite providing stronger security guarantees, the proposed solutions still entrust their data to a single entity that can be corrupted or hacked. Secret sharing can solve this problem by dividing data in multiple secrets and storing each secret at a different location. The division is done in such a way that if one location is hacked, no information can be leaked. Depending on the protocols used to divide data, functions can be computed over this data through secure protocols that do not disclose information or actually know which values are being calculated. We propose a SQL database prototype capable of offering a trade-off between security and query latency by using a different secure protocol. An evaluation of the protocols is also performed, showing that our most relaxed protocol has an improvement of 5% on the query latency time over the original protocol. © 2017 ACM.

Abstract
A method to detect juxta-pleural nodules with radius smaller than 5mm is presented. The intensity difference between nodules and parenchymal tissue as well as the nodules' natural roundness are exploited. Solid nodules are detected by selecting an appropriate threshold over a sliding window, whereas sub-solid/non-solid nodules are enhanced using multi-scale Laplacian-of-Gaussian filters. The 2D-wise outputs are combined to 3D, producing a final candidate list. False positive reduction is achieved with fixed rules and supervised learning. The achieved sensitivity is 57% with 4 false positives/scan, increasing to 62% if only solid nodules are considered. © 2017 SPIE.

Abstract
In this work, the self-scheduling problem of a power producer in smart grids is addressed using a stochastic programming approach. Different uncertainties are considered as price uncertainties, forced-outage of the unit as well as generation reallocation. The conditional value-at-risk index is used for modeling of risk. The markets considered in this study are bilateral contracts, day-ahead and ancillary services, including spinning reserve and regulation, and spot market decisions, while an incomplete competitive market is considered. In this sense, an innovative method for bilateral contracts is proposed to increase the profit of the market without ignoring any regulatory rules. The Monte Carlo method is implemented together with a reduction scenario process to generate scenarios.

Abstract
Melanoma is the most dangerous and lethal form of human skin cancer and the early detection is a fundamental key for its successful management. In recent years the use of automatic classification algorithms in the context of Computer Aided Diagnosis (CAD) systems have been an important tool, by improving quantification metrics and also assisting in the decision regarding lesion management. This paper presents a novel and robust textured-based approach to detect melanomas among melanocytic images obtained by dermoscopy, using Local Binary Pattern Variance (LBPV) histograms after the Bidimensional Empirical Mode Decomposition (BEMD) scale-based decomposition methodology. The results show that it is possible to develop a robust CAD system for the classification of dermoscopy images obtained from different databases and acquired in diverse conditions. After the initial texture-scale based classification a post-processing refinement is proposed using reticular pattern and color achieving to 97.83, 94.44 and 96.00 for Sensitivity, Specificity and Accuracy.

Abstract