Abstract
The Apriori algorithm is considered a classic in the association rules extraction field. This algorithm makes recursive searches in a dataset looking for frequent sets that satisfy given minimum support. Apriori has several properties to optimize its performance, such as reducing the number of generated itemsets and its parallelization by multiple processors. These features have led to the emergence of several studies that present parallel versions of Apriori. However, these proposals do not explore the heterogeneous capabilities of each machine, which causes a significant part of the algorithm's processing time to be spent on I/O processes and not exactly on the execution of the algorithm. In this sense, this study proposes a mathematical modeling of the Apriori algorithm in which heterogeneous machines are considered. The findings identified a better performance of this algorithm when compared to the original and parallel versions of Apriori, but in which all processors are considered homogeneous. The findings reveal the time reducing rate increases with the growth in the number of itemsets and the number of considered processors.

Abstract
Human integration in cyber-physical systems (CPS) is playing a crucial role in the era of the digital transformation, notably because humans are seen as the most flexible driver in an automated system. Two main reference models for human activities in production systems are usually considered, namely Human-in-the-Loop (HitL) and Human-in-the-Mesh (HitM), which present different requirements and challenges. This paper aims to overview the different activities related to the human integration in CPS, particularly discussing the requirements that can be found in HitL and HitM models for the different phases of the decision-making process, namely detect, determine, develop and describe; and analyzing the technologies and computational tools to support these human activities. The human integration in CPS is illustrated through three examples, where humans playing the operator and manager roles are integrated in the PERFoRM and FAR-EDGE ecosystems, covering different phases of the decision-making process. Copyright

Abstract
Wavefront sensors (WFSs) encode phase information of an incoming wavefront into an intensity pattern that can be measured on a camera. Several kinds of WFSs are used in astronomical adaptive optics. Among them, Fourier-based WFSs perform a filtering operation on the wavefront in the focal plane. The most well-known example of a WFS of this kind is the Zernike WFS. The pyramid WFS also belongs to this class. Based on this same principle, WFSs can be proposed, such as the n-faced pyramid (which ultimately becomes an axicon) or the flattened pyramid, depending on whether the image formation is incoherent or coherent. To test such concepts, the LAM/ONERA on-sky pyramid sensor (LOOPS) adaptive optics testbed hosted at the Laboratoire d'Astrophysique de Marseille has been upgraded by adding a spatial light modulator (SLM). This device, placed in a focal plane produces high-definition phase masks that mimic otherwise bulk optic devices. We first present the optical design and upgrades made to the experimental setup of the LOOPS bench. Then, we focus on the generation of the phase masks with the SLM and the implications of having such a device in a focal plane. Finally, we present the first closed-loop results in either static or dynamic mode with different WFS applied on the SLM.

Abstract
This paper address the use of deep learning approaches for visual based navigation in confined underwater environments. State-of-the-art algorithms have shown the tremendous potential deep learning architectures can have for visual navigation implementations, though they are still mostly outperformed by classical feature-based techniques. In this work, we apply current state-of-the-art deep learning methods for visual-based robot navigation to the more challenging underwater environment, providing both an underwater visual dataset acquired in real operational mission scenarios and an assessment of state-of-the-art algorithms on the underwater context. We extend current work by proposing a novel pose optimization architecture for the purpose of correcting visual odometry estimate drift using a Visual-Inertial fusion network, consisted of a neural network architecture anchored on an Inertial supervision learning scheme. Our Visual-Inertial Fusion Network was shown to improve results an average of 50% for trajectory estimates, also producing more visually consistent trajectory estimates for both our underwater application scenarios.

Abstract
Recent Low Power Wide Area Networks (LPWAN) protocols are receiving increased attention from industry and academia to offer accessibility for Internet of Things (IoT) connected remote sensors and actuators. In this work, we present a formal study of LoRaWAN security, an increasingly popular technology, which defines the structure and operation of LPWAN networks based on the LoRa physical layer. There are previously known security vulnerabilities in LoRaWAN that lead to the proposal of several improvements, some already incorporated into the latest protocol specification. Our analysis of LoRaWAN security uses Scyther, a formal security analysis tool and focuses on the key exchange portion of versions 1.0 (released in 2015) and 1.1 (the latest, released in 2017). For version 1.0, which is still the most widely deployed version of LoRaWAN, we show that our formal model allowed to uncover weaknesses that can be related to previously reported vulnerabilities. Our model did not find weaknesses in the latest version of the protocol (v1.1), and we discuss what this means in practice for the security of LoRaWAN as well as important aspects of our model and tools employed that should be considered. The Scyther model developed provides realistic models for LoRaWAN v1.0 and v1.1 that can be used and extended to formally analyze, inspect, and explore the security features of the protocols. This, in turn, can clarify the methodology for achieving secrecy, integrity, and authentication for designers and developers interested in these LPWAN standards. We believe that our model and discussion of the protocols security properties are beneficial for both researchers and practitioners. To the best of our knowledge, this is the first work that presents a formal security analysis of LoRaWAN.

Abstract
For extremely large telescopes, adaptive optics will be required to correct the Earth’s turbulent atmosphere. The performance of tomographic adaptive optics is strongly dependent on the vertical distribution (profile) of this turbulence. An important way in which this manifests is the tomographic error, arising from imperfect measurement and reconstruction of the turbulent phase at altitude. Conventionally, a small number of reference profiles are used to obtain this error in simulation; however these profiles are not constructed to be representative in terms of tomographic error. It is therefore unknown whether these simulations are providing realistic performance estimates. Here, we employ analytical adaptive optics simulation that drastically reduces computation times to compute tomographic error for 10 691 measurements of the turbulence profile gathered by the Stereo-SCIDAR instrument at ESO Paranal. We assess for the first time the impact of the profile on tomographic error in a statistical manner. We find, in agreement with previous work, that the tomographic error is most directly linked with the distribution of turbulence into discrete, stratified layers. Reference profiles are found to provide mostly higher tomographic error than expected, which we attribute to the fact that these profiles are primarily composed of averages of many measurements resulting in unrealistic, continuous distributions of turbulence. We propose that a representative profile should be defined with respect to a particular system, and that as such simulations with a large statistical sample of profiles must be an important step in the design process.