Abstract
Forest fires can have devastating consequences if not detected and fought before they spread. This paper presents an automatic fire detection system designed to identify forest fires, preferably, in their early stages. The system pipeline processes images of the forest environment and is able to detect the presence of smoke or flames. Additionally, the system is able to produce an estimation of the area under ignition so that its size can be evaluated. In the process of classification of a fire image, one Deep Convolutional Neural Network was used to extract, from the images, the descriptors which are then applied to a Logistic Regression classifier. At a later stage of the pipeline, image analysis and processing techniques at color level were applied to assess the area under ignition. In order to better understand the influence of specific image features in the classification task, the organized dataset, composed by 882 images, was associated with relevant image metadata (eg presence of flames, smoke, fog, clouds, human elements). In the tests, the system obtained a classification accuracy of 94.1% in 695 images of daytime scenarios and 94.8% in 187 images of nighttime scenarios. It presents good accuracy in estimating the flame area when compared with other approaches in the literature, substantially reducing the number of false positives and nearly keeping the same false negatives stats. © Springer Nature Switzerland AG 2019.

Abstract
The longevity of the population is the result of important scientific breakthroughs in recent years. However, living longer with quality, also brings new challenges to governments, and to the society as a whole. One of the most significant consequences will be the increasing pressure on the healthcare services. Ambient Assisted Living (AAL) systems can greatly improve healthcare scalability and reach while keeping the user in their home environment. The work presented in this paper specifies, implements, and validates a smart environment system that aggregates Automation and Artificial Intelligence (AI). The specification includes a reference architecture, composed by three modules, whose tasks are to automate and standardize the collection of data, to relate and give meaning to that data and to learn from it. The system is able to identify daily living activities with different levels of complexity using a temporal logic. It enables a real time response to emergency situations and also a long term analysis of the user daily routine useful to induce healthier lifestyles. The implementation addresses the applications and techniques used in the development of a functional prototype. To demonstrate the system operation three use cases with increasing levels of complexity are proposed and validated. A discussion on related projects is also included, specifically on automation applications, Knowledge Representation (KR) and Machine Learning (ML). © Springer Nature Switzerland AG 2019.

Abstract

Abstract
Abstract The world population is facing several difficulties related to an aging society. In particular, the widespread increase of chronic and incapacitating diseases is overwhelming for traditional healthcare services. Ambient assisted living (AAL) systems can greatly improve healthcare scalability and scope while keeping people in the comfort of their home environments. This chapter focuses precisely on presenting the fundamental key aspects (cf. processing and sensing, integration and management, communication and coordination, intelligence and reasoning) to promote safety and support for outpatients living autonomously in AAL settings. Furthermore, for each key issue, a set of practical technological solutions are reported and detailed, showing real applicability of ubicomp technology to the integration and management of AAL systems specially designed for supporting daily living activities of people with progressive loss of capacities. © 2019 John Wiley & Sons, Ltd.

Abstract
Retailers need demand forecasts at different levels of aggregation in order to support a variety of decisions along the supply chain. To ensure aligned decision-making across the hierarchy, it is essential that forecasts at the most disaggregated level add up to forecasts at the aggregate levels above. It is not clear if these aggregate forecasts should be generated independently or by using an hierarchical forecasting method that ensures coherent decision-making at the different levels but does not guarantee, at least, the same accuracy. To give guidelines on this issue, our empirical study investigates the relative performance of independent and reconciled forecasting approaches, using real data from a Portuguese retailer. We consider two alternative forecasting model families for generating the base forecasts; namely, state space models and ARIMA. Appropriate models from both families are chosen for each time-series by minimising the bias-corrected Akaike information criteria. The results show significant improvements in forecast accuracy, providing valuable information to support management decisions. It is clear that reconciled forecasts using the Minimum Trace Shrinkage estimator (MinT-Shrink) generally improve on the accuracy of the ARIMA base forecasts for all levels and for the complete hierarchy, across all forecast horizons. The accuracy gains generally increase with the horizon, varying between 1.7% and 3.7% for the complete hierarchy. It is also evident that the gains in forecast accuracy are more substantial at the higher levels of aggregation, which means that the information about the individual dynamics of the series, which was lost due to aggregation, is brought back again from the lower levels of aggregation to the higher levels by the reconciliation process, substantially improving the forecast accuracy over the base forecasts.

Abstract
Security is now becoming a well-established challenge for integrated circuits (ICs). Various types of IC attacks have been reported, including reverse engineering IPs, dumping on-chip data, and controlling/modifying IC operation. IEEE 1149.1, commonly known as Joint Test Action Group (JTAG), is a standard for providing test access to an IC. JTAG is primarily used for IC manufacturing test, but also for in-field debugging and failure analysis since it gives access to internal subsystems of the IC. Because the JTAG needs to be left intact and operational after fabrication, it inevitably provides a "backdoor" that can be exploited outside its intended use. This paper proposes machine learning-based approaches to detect illegitimate use of the JTAG. Specifically, JTAG operation is characterized using various features that are then classified as either legitimate or attack. Experiments using the OpenSPARC T2 platform demonstrate that the proposed approaches can classify legitimate JTAG operation and known attacks with significantly high accuracy. Experiments also demonstrate that unknown and disguised attacks can be detected with high accuracy as well (99% and 94%, respectively).