Updated Taxonomy for the Network and Service Management Research Field

Network and service management is an established research field within the general area of computer networks. A few years ago, an initial taxonomy, organizing a comprehensive list of terms and topics, was established through interviews with experts from both industry and academia. This taxonomy has since been used to better partition standardization efforts, identify classes of managed objects and improve the assignment of reviewers to papers submitted in the field. Because the field of network and service management is rapidly evolving, a biyearly update of the taxonomy was proposed. In this paper, a large-scale questionnaire is presented which was answered by experts in the field, evaluating the relevance of each individual topic for the next five years. Missing topics, which are likely to become relevant over the next few years, are identified as well. Furthermore, an analysis is performed of the records of papers submitted to major conferences in the area. Based on the obtained results, an updated version of the taxonomy is proposed.     

Concept drift detection and adaptation for federated and continual learning

Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment. This data is suitable for training machine learning models, which can significantly improve their behavior, and therefore, the user experience. Federated learning is a young and popular framework that allows multiple distributed devices to train deep learning models collaboratively while preserving data privacy. Nevertheless, this approach may not be optimal for scenarios where data distribution is non-identical among the participants or changes over time, causing what is known as concept drift. Little research has yet been done in this field, but this kind of situation is quite frequent in real life and poses new challenges to both continual and federated learning. Therefore, in this work, we present a new method, called Concept-Drift-Aware Federated Averaging (CDA-FedAvg). Our proposal is an extension of the most popular federated algorithm, Federated Averaging (FedAvg), enhancing it for continual adaptation under concept drift. We empirically demonstrate the weaknesses of regular FedAvg and prove that CDA-FedAvg outperforms it in this type of scenario.

     

Analysis of non-Markovian repairable fault trees through rare event simulation

Dynamic fault trees (DFTs) are widely adopted in industry to assess the dependability of safety-critical equipment. Since many systems are too large to be studied numerically, DFTs dependability is often analysed using Monte Carlo simulation. A bottleneck here is that many simulation samples are required in the case of rare events, e.g. in highly reliable systems where components seldom fail. Rare event simulation (RES) provides techniques to reduce the number of samples in the case of rare events. In this article, we present a RES technique based on importance splitting to study failures in highly reliable DFTs, more precisely, on a variant of repairable fault trees (RFT). Whereas RES usually requires meta-information from an expert, our method is fully automatic. For this, we propose two different methods to derive the so-called importance function. On the one hand, we propose to cleverly exploit the RFT structure to compositionally construct such function. On the other hand, we explore different importance functions derived in different ways from the minimal cut sets of the tree, i.e., the minimal units that determine its failure. We handle RFTs with Markovian and non-Markovian failure and repair distributions—for which no numerical methods exist—and implement the techniques on a toolchain that includes the RES engine FIG, for which we also present improvements. We finally show the efficiency of our approach in several case studies.

     

Compact Bioinspired Antenna for WLAN 5 GHz Application

Wireless Personal Communications - This paper presents the development of a compact monopole antenna bioinspired in the Opuntia ficus-indica plant-shape, generated by Gielis formula, built in...     

Portable real-time DCT-based steganography using OpenCL

In this paper, a steganographic method for real-time data hiding is proposed. The main goal of the research is to develop steganographic method with increased robustness to unintentional image processing attacks. In addition, we prove the validity of the method in real-time applications. The method is based on a discrete cosine transform (DCT) where the values of a DCT coefficients are modified in order to hide data. This modification is invisible to a human observer. We further the investigation by implementing the proposed method using different target architectures and analyze their performance. Results show that the proposed method is very robust to image compression, scaling and blurring. In addition, modification of the image is imperceptible even though the number of embedded bits is high. The steganalysis of the method shows that the detection of the modification of the image is unreliable for a lower relative payload size embedded. Analysis of OpenCL implementation of the proposed method on four different target architectures shows considerable speedups.     

Area‐Preserving Parameterizations for Spherical Ellipses

We present new methods for uniformly sampling the solid angle subtended by a disk. To achieve this, we devise two novel area‐preserving mappings from the unit square [0,1]² to a spherical ellipse (i.e. the projection of the disk onto the unit sphere). These mappings allow for low‐variance stratified sampling of direct illumination from disk‐shaped light sources. We discuss how to efficiently incorporate our methods into a production renderer and demonstrate the quality of our maps, showing significantly lower variance than previous work.     

Mixed Methods for a Stream-Function – Vorticity Formulation of the Axisymmetric Brinkman Equations

This paper is devoted to the numerical analysis of a family of finite element approximations for the axisymmetric, meridian Brinkman equations written in terms of the stream-function and vorticity. A mixed formulation is introduced involving appropriate weighted Sobolev spaces, where well-posedness is derived by means of the Babu?ka–Brezzi theory. We introduce a suitable Galerkin discretization based on continuous piecewise polynomials of degree \(k\ge 1\) for all the unknowns, where its solvability is established using the same framework as the continuous problem. Optimal a priori error estimates are derived, which are robust with respect to the fluid viscosity, and valid also in the pure Darcy limit. A few numerical examples are presented to illustrate the convergence and performance of the proposed schemes.     

A New Mixed Finite Element Method for Elastodynamics with Weak Symmetry

We provide a new mixed finite element analysis for linear elastodynamics with reduced symmetry. The problem is formulated as a second order system in time by imposing only the Cauchy stress tensor and the rotation as primary and secondary variables, respectively. We prove that the resulting variational formulation is well-posed and provide a convergence analysis for a class of \({\mathrm {H}}(\mathop {{\mathrm {div}}}\nolimits )\)-conforming semi-discrete schemes. In addition, we use the Newmark trapezoidal rule to obtain a fully discrete version of the problem and carry out the corresponding convergence analysis. Finally, numerical tests illustrating the performance of the fully discrete scheme are presented.