Abstract
Network data volumes have seen a substantial increase in recent years, in part due to the massive use of mobile devices, the dissemination of streaming services and the rise of concepts such as IoT. This growing trend highlights the need to improve network monitoring systems to cope with challenges related with performance, flexibility and security. Software-Defined Networking (SDN) and traffic sampling techniques can be combined to provide a toolset that can be used for enhancing network management activities and performance evaluation. In this context, this paper presents a proposal for supporting time-based sampling techniques in SDN, providing network statistics at the controller level and allowing the self-configuration of traffic sampling in network devices. The proposed solution, designed to improve the efficiency and flexibility of network measurement systems, takes into account the underlying need to establish a balance between the reliability of the collected data and the computational effort involved in the sampling process. The proof-of-concept results emphasize the potential of applying and configuring different time-based sampling techniques through a SDN framework and a small set of standard OpenFlow messages. Comparative results on the accuracy and overhead of each technique when sampling real traffic traces are also provided.

Abstract
The multitude of Wireless Sensor Networks (WSNs) environments, being typically resource-constrained, clearly benefit from properties such as adaptiveness and energy-awareness, in particular, in presence of demanding data gathering applications. This paper proposes a self-adaptive, energy-aware sensing scheme for WSNs (e-LiteSense), which aims at self-adjusting the data gathering process to each specific WSN context, capturing accurately the behaviour of physical parameters of interest yet reducing the sensing overhead. The adaptive scheme relies on a set of low-complexity rules capable of auto-regulate the sensing frequency according to the parameters variability and energy levels. The proof-of-concept resorts to real-world datasets to provide evidence of e-LiteSense ability to optimise the data gathering process according to energy levels, improving the trade-off between accuracy and WSN lifetime.

Abstract

Abstract
Computer-aided engineering simulations, in particular, Computational Fluid Dynamics, have become a fundamental design and analysis tool in product development. Over time, a demand for larger problem sizes and higher accuracy has led to huge computational workloads requiring extended compute capabilities. Increasing computing capabilities requirements, however, drive a fast-growing power consumption. In order to deal with increasing power demand, hardware and software solutions' reevaluation in terms of power-efficiency becomes of paramount importance. Establishing a power budget and reducing the compute units operating frequency in order to comply with such budget is becoming common practice. However, in the presence of heterogeneous compute units and dynamic workloads, a static and uniform reduction across compute units leads to a potentially severe impact on performance. This paper proposes a run-time heterogeneity-aware power-adaptive schedule that provides power consumption optimization, targeting heterogeneous parallel distributed systems in the context of CFD simulations. The proposed approach is integrated into OpenFOAM computational library and explores power migration and reduction across nodes, considering runtime workload imbalances and node performances. Results reveal not only a substantial reduction in power usage but also significant performance gains relative to the uniform static approach. To the best of authors' knowledge, this is the first implementation and integration of power management solutions in OpenFOAM.

Abstract
CFD simulations are a fundamental engineering application, implying huge workloads, often with dynamic behaviour due to runtime mesh refinement. Parallel processing over heterogeneous distributed memory clusters is often used to process such workloads. The execution of dynamic workloads over a set of heterogeneous resources leads to load imbalances that severely impacts execution time, when static uniform load distribution is used. This paper proposes applying dynamic, heterogeneity aware, load balancing techniques within CFD simulations. nSharma, a software package that fully integrates with OpenFOAM, is presented and assessed. Performance gains are demonstrated, achieved by reducing busy times standard deviation among resources, i.e., heterogeneous computing resources are kept busy with useful work due to an effective workload distribution. To best of authors’ knowledge, nSharma is the first implementation and integration of heterogeneity aware load balancing in OpenFOAM and will be made publicly available in order to foster its adoption by the large community of OpenFOAM users.

Abstract
Despite great advances in computer-assisted proof systems, writing formal proofs using a traditional computer is still challenging due to mouse-and-keyboard interaction. This leads to scientists often resorting to pen and paper to write their proofs. However, when handwriting a proof, there is no formal guarantee that the proof is correct. In this paper we address this issue and present the initial steps towards a system that allows users to handwrite proofs using a pen-based device and that communicates with an external theorem prover to support the users throughout the proof writing process. We focus on calculational proofs, whereby a theorem is proved by a chain of formulae, each transformed in some way into the next. We present the implementation of a proof-of-concept prototype that can formally verify handwritten calculational proofs without the need to learn the specific syntax of theorem provers.