Abstract
Sugarcane is an important product to the Brazilian economy because it is the primary ingredient of ethanol which is used as a gasoline substitute. Sugarcane is aflected by many factors which can be modelled in a Bayesian Graph. This paper describes a technique to build a Causal Bayesian Network from information in news stories. The technique: extracts causal relations from news stories, converts them into an event graph, removes irrelevant information, solves structure problems, and clusters the event graph by topic distribution. Finally, the paper describes a method for generating inferences from the graph based upon evidence in agricultural news stories. The graph is evaluated through a manual inspection and with a comparison with the EMBRAPA sugarcane taxonomy. © ACM 2016.

Abstract
Security is becoming an essential problem for integrated circuits (ICs). Various attacks, such as reverse engineering and dumping on-chip data, have been reported to undermine IC security. IEEE 1149.1, also known as JTAG, is primarily used for IC manufacturing test but inevitably provides a "backdoor" that can be exploited to attack ICs. Encryption has been used extensively as an effective mean to protect ICs through authentication, but a few weaknesses subsist, such as key leakage. Signature-based techniques ensure security using a database that includes known attacks, but fail to detect attacks that are not contained by the database. To overcome these drawbacks, a two-layer learning-based protection scheme is proposed. Specifically, the scheme monitors the execution of JTAG instructions and uses support vector machines (SVM) to identify abnormal instruction sequences. The use of machine learning enables the detection of unseen attacks without the need for key-based authentication. The experiments based on the OpenSPARC T2 platform demonstrate that the proposed scheme improves the accuracy of detecting unseen attacks by 50% on average when compared to previous work.

Abstract
The iMPAcT tool combines the benefits of existing user recurring behaviour (User Interface Patterns) on mobile applications to facilitate the test automation of Android mobile applications. It uses an automatic exploration process combined with reverse engineering to identify the existing user interface patterns on a mobile application and then tests those patterns with generic test strategies (designated Test Patterns). The Test Patterns are defined in a catalogue that can be reused for testing other applications. However the results obtained by the iMPAcT tool depend on the exploration mode and on the order in which the test strategies are applied. This paper describes an experiment conducted to evaluate the impact of using different exploration modes and of changing the order by which UI patterns are searched and subsequently tested on the failures found and on the number of events fired. (C) 2016 The Authors. Published by Elsevier B.V.

Abstract
In this paper, an agent-based model is proposed to improve market efficiency by using different Demand Response Programs (DRPs) in the day-ahead electricity market. To this end, both incentive-based and price-based DRPs are considered. On this basis, time of use, real time pricing, emergency demand response program, interruptible/curtailable services and critical peak pricing are investigated. The tariffs of the considered price-based programs and the amount of incentive in the incentive-based programs are optimized through the proposed model. Furthermore, a market power index, i.e., Share Weighted Average Lerner Index (SWALI) and the operation cost are used to evaluate the market efficiency and the market power. The proposed model optimizes the DRPs to improve the electricity market efficiency by using a multi-attribute decision-making approach. The results show that the market operator can mitigate the potential occurrence of market power in a power system by finding the optimal DRP. © 2016 IEEE.

Abstract
The R package micompr implements a procedure for assessing if two or more multivariate samples are drawn from the same distribution. The procedure uses principal component analysis to convert multivariate observations into a set of linearly uncorrelated statistical measures, which are then compared using a number of statistical methods. This technique is independent of the distributional properties of samples and automatically selects features that best explain their differences. The procedure is appropriate for comparing samples of time series, images, spectrometric measures or similar high-dimension multivariate observations.

Abstract
The current urge on using Internet for both social and professional tasks, alongside with the existence of over 80 million European citizens with some sort of disability as led to the need for an accessible Web [1]. Given, the also increasing thrive for searching the web for health related information and the assumption of heath care institutions as the creators of this type of information, a research study was developed in order to evaluate the accessibility levels of the Iberian health care institutions. From this study, that used ACESSWEB as the evaluation platform, it was possible to perceive that none of the evaluated websites was WCAG 2.0 compliant and that, despite the poor results, Spanish websites presented more accessibility concerns than the Portuguese. Hence, the situation is critical and a significant effort must be done in order to allow citizens with disabilities to have access to the much needed health related information.