Parameterizable QR core

The design of a generic QR core for adaptive beamforming is presented. The work relies on an existing mapping technique that can be applied to a triangular QR array in such a way to allow the generation of a range of QR architectures. All scheduling of data inputs and retiming to include processor latency has been included within the generic representation.

nero- a post-maximum supernova radiation transport code

The interpretation of supernova (SN) spectra is essential for deriving SN ejecta properties such as density and composition, which in turn can tell us about their progenitors and the explosion mechanism. A very large number of atomic processes are important for spectrum formation. Several tools for calculating SN spectra exist, but they mainly focus on the very early or late epochs. The intermediate phase, which requires a non-local thermodynamic equilibrium (NLTE) treatment of radiation transport has rarely been studied. In this paper, we present a new SN radiation transport code, nero, which can look at those epochs. All the atomic processes are treated in full NLTE, under a steady-state assumption. This is a valid approach between roughly 50 and 500days after the explosion depending on SN type. This covers the post-maximum photospheric and the early and the intermediate nebular phase. As a test, we compare nero to the radiation transport code of Jerkstrand, Fransson & Kozma and to the nebular code of Mazzali et al. All three codes have been developed independently and a comparison provides a valuable opportunity to investigate their reliability. Currently, nero is one-dimensional and can be used for predicting spectra of synthetic explosion models or for deriving SN properties by spectral modelling. To demonstrate this, we study the spectra of the 'normal' Type Ia supernova (SN Ia) 2005cf between 50 and 350 days after the explosion and identify most of the common SN Ia line features at post-maximum epochs. © 2011 The Authors Monthly Notices of the Royal Astronomical Society © 2011 RAS.

Accelerating code on multi-cores with FastFlow

FastFlow is a programming framework specifically targeting cache-coherent shared-memory multi-cores. It is implemented as a stack of C++ template libraries built on top of lock-free (and memory fence free) synchronization mechanisms. Its philosophy is to combine programmability with performance. In this paper a new FastFlow programming methodology aimed at supporting parallelization of existing sequential code via offloading onto a dynamically created software accelerator is presented. The new methodology has been validated using a set of simple micro-benchmarks and some real applications. © 2011 Springer-Verlag.

Analysis of Bayesian classification-based approaches for Android malware detection

Mobile malware has been growing in scale and complexity spurred by the unabated uptake of smartphones worldwide. Android is fast becoming the most popular mobile platform resulting in sharp increase in malware targeting the platform. Additionally, Android malware is evolving rapidly to evade detection by traditional signature-based scanning. Despite current detection measures in place, timely discovery of new malware is still a critical issue. This calls for novel approaches to mitigate the growing threat of zero-day Android malware. Hence, the authors develop and analyse proactive machine-learning approaches based on Bayesian classification aimed at uncovering unknown Android malware via static analysis. The study, which is based on a large malware sample set of majority of the existing families, demonstrates detection capabilities with high accuracy. Empirical results and comparative analysis are presented offering useful insight towards development of effective static-analytic Bayesian classification-based solutions for detecting unknown Android malware.

Fast Moving Window Algorithm for QR and Cholesky Decompositions

This paper proposes a fast moving window algorithm for QR and Cholesky decompositions by simultaneously applying data updating and downdating. The developed procedure is based on inner products and entails a similar downdating to that of the Chambers’ approach. For adding and deleting one row of data from the original matrix, a detailed analysis shows that the proposed algorithm outperforms existing ones in terms or computational efficiency, if the number of columns exceeds 7. For a large number of columns, the proposed algorithm is numerically superior compared to the traditional sequential technique.

A spectral synthesis code for rapid modelling of supernovae

We present TARDIS-an open-source code for rapid spectral modelling of supernovae (SNe). Our goal is to develop a tool that is sufficiently fast to allow exploration of the complex parameter spaces of models for SN ejecta. This can be used to analyse the growing number of highquality SN spectra being obtained by transient surveys. The code uses Monte Carlo methods to obtain a self-consistent description of the plasma state and to compute a synthetic spectrum. It has a modular design to facilitate the implementation of a range of physical approximations that can be compared to assess both accuracy and computational expediency. This will allow users to choose a level of sophistication appropriate for their application. Here, we describe the operation of the code and make comparisons with alternative radiative transfer codes of differing levels of complexity (SYN++, PYTHON and ARTIS). We then explore the consequence of adopting simple prescriptions for the calculation of atomic excitation, focusing on four species of relevance to Type Ia SN spectra-Si II, SII, MgII and Ca II. We also investigate the influence of three methods for treating line interactions on our synthetic spectra and the need for accurate radiative rate estimates in our scheme.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

Classifying pedestrian movement behaviour from GPS trajectories using visualization and clustering

The quantity and quality of spatial data are increasing rapidly. This is particularly evident in the case of movement data. Devices capable of accurately recording the position of moving entities have become ubiquitous and created an abundance of movement data. Valuable knowledge concerning processes occurring in the physical world can be extracted from these large movement data sets. Geovisual analytics offers powerful techniques to achieve this. This article describes a new geovisual analytics tool specifically designed for movement data. The tool features the classic space-time cube augmented with a novel clustering approach to identify common behaviour. These techniques were used to analyse pedestrian movement in a city environment which revealed the effectiveness of the tool for identifying spatiotemporal patterns. © 2014 Taylor & Francis.

Belfast Soundwalks: Experiences in Sound and Place through Locative Media

This article outlines the ongoing development of a locative smartphone app for iPhone and Android phones entitled The Belfast Soundwalks Project. Drawing upon a method known as soundwalking, the aim of this app is to engage the public in sonic art through the creation of up to ten soundwalks within the city of Belfast. This paper discusses the use of GPS enabled mobile devices in the creation of soundwalks in other cities. The authors identify various strategies for articulating an experience of listening in place as mediated by mobile technologies. The project aims to provide a platform for multiple artists to develop site-specific sound works which highlight the relationship between sound, place and community. The development of the app and the app interface are discussed, as are the methods employed to test and evaluate the project.