Abstract
There are currently several techniques for automatic calibration of inertial sensors. This paper describes a subset of these algorithms that could be used in a manipulator and should allow for its prompt use. A robotic manipulator specifically developed for the study of over-sensored systems is used to realistically test the performance of the implemented methods. The results of these methods show that the accelerometers and the gyroscopes were properly calibrated. However, the magnetometers suffer from variable interferences and therefore could not be calibrated.

Abstract
The recent Covid-19 pandemic and the fact that wearing masks in public is now mandatory in several countries, created challenges in the use of face recognition systems (FRS). In this work, we address the challenge of masked face recognition (MFR) and focus on evaluating the verification performance in FRS when verifying masked vs unmasked faces compared to verifying only unmasked faces. We propose a methodology that combines the traditional triplet loss and the mean squared error (MSE) intending to improve the robustness of an MFR system in the masked-unmasked comparison mode. The results obtained by our proposed method show improvements in a detailed step-wise ablation study. The conducted study showed significant performance gains induced by our proposed training paradigm and modified triplet loss on two evaluation databases.

Abstract
By using Dynamic Binary Translation, instruction traces from pre-compiled applications can be offloaded, at runtime, to FPGA-based accelerators, such as Coarse-Grained Loop Accelerators, in a transparent way. However, scheduling onto coarse-grain accelerators is challenging, with two of current known issues being the density of computations that can be mapped, and the effects of memory accesses on performance. Using an in-house framework for analysis of instruction traces, we explore the effect of different window sizes when applying list scheduling, to map the window operations to a coarse-grain loop accelerator model that has been previously experimentally validated. For all window sizes, we vary the number of ALUs and memory ports available in the model, and comment how these parameters affect the resulting latency. For a set of benchmarks taken from the PolyBench suite, compiled for the 32-bit MicroBlaze softcore, we have achieved an average iteration speedup of 5.10x for a basic block repeated 5 times and scheduled with 8 ALUs and memory ports, and an average speedup of 5.46x when not considering resource constraints. We also identify which benchmarks contribute to the difference between these two speedups, and breakdown their limiting factors. Finally, we reflect on the impact memory dependencies have on scheduling.

Abstract
Reinforcement Learning is at the core of a recent revolution in Artificial Intelligence. Simultaneously, we are witnessing the emergence of a new field: Quantum Machine Learning. In the context of these two major developments, this work addresses the interplay between Quantum Computing and Reinforcement Learning. Learning by interaction is possible in the quantum setting using the concept of oraculization of environments. The paper extends previous oracular instances to address more general stochastic environments. In this setting, we developed a novel quantum algorithm for near-optimal decision-making based on the Reinforcement Learning paradigm known as Sparse Sampling. The proposed algorithm exhibits a quadratic speedup compared to its classical counterpart. To the best of the authors' knowledge, this is the first quantum planning algorithm exhibiting a time complexity independent of the number of states of the environment, which makes it suitable for large state space environments, where planning is otherwise intractable.

Abstract
We propose E-down arrow((D) over right arrow)-logic as a formal foundation for the specification and development of event-based systems with data states. The framework is presented as an institution in the sense of Goguen and Burstall and the logic itself is parametrised by an underlying institution (D) over right arrow whose structures are used to model data states. E-down arrow((D) over right arrow)-logic is intended to cover a broad range of abstraction levels from abstract requirements specifications up to constructive specifications. It uses modal diamond and box operators over complex actions adopted from dynamic logic. Atomic actions are pairs e/psi where e is an event and psi a state transition predicate capturing the allowed reactions to the event. Towrite concrete specifications of recursive process structureswe integrate (control) state variables and binders of hybrid logic. The semantic interpretation relies on event/data transition systems. For the presentation of constructive specifications we propose operational event/data specifications allowing for familiar, diagrammatic representations by state transition graphs. We show that E-down arrow((D) over right arrow)-logic is powerful enough to characterise the semantics of an operational specification by a single E-down arrow((D) over right arrow)-sentence. Thus the whole (formal) development process for event/data-based systems relies on E-down arrow((D) over right arrow)-logic and its semantics as a common basis. It is supported by a variety of implementation constructors which can express, among others, event refinement and parallel composition. Due to the genericity of the approach, it is also possible to change a data state institution during system development when needed. All steps of our formal treatment are illustrated by a running example.

Abstract
Purpose This study applies an Employee Satisfaction Index (ESI) model to quantify the level of job satisfaction and explores the factors that influence employee satisfaction, performance and loyalty to an information technology recruitment and outsourcing organization in Portugal. Design/methodology/approach As an instrument of data collection, questionnaire was applied to the company's employees, which was divided into two parts: the first part consisted of a previous questionnaire, with questions related to sociodemographic characterization; the second part consisted of the ESI. The company operates only in the information technology (IT) market, and there are currently 300 consultants with different skills. Findings The results confirm that the company's employees are globally satisfied, and this satisfaction contributes positively and significantly to the reinforcement of contextual performance and to their loyalty to this organization. Originality/value Job satisfaction takes on a growing interest in understanding quality of life, strategic management and organizational performance. Job satisfaction contributes to the professional finding, that is, in employees' activity and in adopting positive attitudes toward customer satisfaction, thus promoting organizational performance.