Embedded systems (ES) are wide-spread in our world and responsible for many critical systems.More recently, machine learning (ML) tools have become a well-established solution for data-intensive tasks, but their application in embedded systems is still gaining traction and their real-time performance is often unclear.We provide a (non-extensive) review of the ML tools that may be suited for deployment in ES, from which we selected two representative tools -the wellestablished Python-based Scikit-Learn, and the interoperabilityoriented ONNX Runtime -to compare their response time.Using archetypal datasets and four pre-trained ML models, we measure the prediction time for each sample, for each model, in Scikit-Learn and ONNX Runtime in a standard desktop (to compare performance of the tools in the same platform), and for ONNX Runtime in a representative ES, a Raspberry Pi v4 (to compare performance of the same tool across platforms).We report that ONNX considerably improves over Scikit-Learn, and experiences a negligible performance degradation when ported to the RPi.

Robust grasping is a major, and still unsolved, problem in robotics. Information about the 3D shape of an object can be obtained either from prior knowledge (e.g., accurate models of known objects or approximate models of familiar objects) or real-time sensing (e.g., partial point clouds of unknown objects) and can be used to identify good potential grasps. However, due to modeling and sensing inaccuracies, local exploration is often needed to refine such grasps and successfully apply them in the real world. The recently proposed unscented Bayesian optimization technique can make such exploration safer by selecting grasps that are robust to uncertainty in the input space (e.g., inaccuracies in the grasp execution). Extending our previous work on 2D optimization, in this paper we propose a 3D haptic exploration strategy that combines unscented Bayesian optimization with a novel collision penalty heuristic to find safe grasps in a very efficient way: while by augmenting the search-space to 3D we are able to find better grasps, the collision penalty heuristic allows us to do so without increasing the number of exploration steps.

Vision-based grasping for humanoid robots is a challenging problem due to a multitude of factors. First, humanoid robots use an “eye-to-hand” kinematics configuration that, on the contrary to the more common “eye-in-hand” configuration, demands a precise estimate of the position of the robot's hand. Second, humanoid robots have a long kinematic chain from the eyes to the hands, prone to accumulate the calibration errors of the kinematics model, which offsets the measured hand-to-object relative pose from the real one. In this paper, we propose a method able to solve these two issues jointly. A robust pose estimation of the robot's hand is achieved via a 3D model-based stereo-vision algorithm, using an edge-based distance transform metric and synthetically generated images of a robot's arm-hand internal computer-graphics model (kinematics and appearance). Then, a particle-based optimization method adapts on-line the robot's internal model to match the real and the synthetically generated images, effectively compensating the kinematics calibration errors. We evaluate the proposed approach using a position-based visual-servoing method on the iCub robot, showing the importance of the continuous visual feedback in humanoid grasping tasks.

Due to increasing requirements, standards and tight oversight from governments, along with the immediate need to effectively manage the increasing business and operational risks inherent to competing in a complex global market, integrated Governance, Risk and Compliance (GRC) is becoming one of the most important business requirements for organizations. In particular, IT requirements, standards and best practices play a crucial role in IT organizations/departments. The lack of guidance in this domain, namely scientific research, results in unaided attempts to improve efficiency and effectiveness in organizations. In this paper we propose a business architecture that describes the integration of the main processes for IT Governance, IT Risk Management and IT Compliance (IT GRC). Based on a process model for IT GRC and a conceptual model for GRC, we use ArchiMate to model the behavioural, structural and informational structure of the business viewpoint - business processes, roles and business objects respectively. To end with, we discuss the final result and draw some conclusions about the constructed artifact.

A total order algorithm is a fundamental building block in the construction of distributed fault-tolerant applications. Unfortunately, the implementation of such a primitive can be expensive both in terms of communication steps and of number of messages exchanged. This problem is exacerbated in large-scale systems, where the performance of the algorithm may be limited by the presence of high-latency links. Typically, the most efficient total order algorithms do not provide uniform delivery and assume the availability of a perfect failure detector. Such algorithms may provide inconsistent results if the system assumptions do not hold. On the other hand, algorithms that assume an unreliable failure detector always provide consistent results but exhibit higher costs. This paper presents a new algorithm that combines the advantages of both approaches. On good periods, when the system is stable and processes are not suspected, the algorithm operates as if a perfect failure detector is assumed. Yet, the algorithm is indulgent, since it never violates consistency, even in runs where processes are suspected.