OpenForensics:a digital forensics GPU pattern matching approach for the 21st century

Pattern matching is a crucial component employed in many digital forensic (DF) analysis techniques, such as file-carving. The capacity of storage available on modern consumer devices has increased substantially in the past century, making pattern matching approaches of current generation DF tools increasingly ineffective in performing timely analyses on data seized in a DF investigation. As pattern matching is a trivally parallelisable problem, general purpose programming on graphic processing units (GPGPU) is a natural fit for this problem. This paper presents a pattern matching framework - OpenForensics - that demonstrates substantial performance improvements from the use of modern parallelisable algorithms and graphic processing units (GPUs) to search for patterns within forensic images and local storage devices

Distributed resource discovery using a context sensitive infrastructure

Distributed Resource Discovery in a World Wide Web environment using full-text indices will never scale. The distinct properties of WWW information (volume, rate of change, topical diversity) limits the scaleability of traditional approaches to distributed Resource Discovery. An approach combining metadata clustering and query routing can, on the other hand, be proven to scale much better. This paper presents the Content-Sensitive Infrastructure, which is a design building on these results. We also present an analytical framework for comparing scaleability of different distribution strategies

Detecting and deterring public computer misuse : the FRILLS project

This presentation looks at forensic readiness for local libraries in Scotland (FRILLS). FRILLS aims to develop simple, low-cost techniques to provide a basic forensic readiness regime for public access ICT facilities, in order to deter misuse of those facilities by better detection of misuse

Exploring More-Coherent Quantum Annealing

In the quest to reboot computing, quantum annealing (QA) is an interesting candidate for a new capability. While it has not demonstrated an advantage over classical computing on a real-world application, many important regions of the QA design space have yet to be explored. In IARPA's Quantum Enhanced Optimization (QEO) program, we have opened some new lines of inquiry to get to the heart of QA, and are designing testbed superconducting circuits and conducting key experiments. In this paper, we discuss recent experimental progress related to one of the key design dimensions: qubit coherence. Using MIT Lincoln Laboratory's qubit fabrication process and extending recent progress in flux qubits, we are implementing and measuring QA-capable flux qubits. Achieving high coherence in a QA context presents significant new engineering challenges. We report on techniques and preliminary measurement results addressing two of the challenges: crosstalk calibration and qubit readout. This groundwork enables exploration of other promising features and provides a path to understanding the physics and the viability of quantum annealing as a computing resource.Comment: 7 pages, 3 figures. Accepted by the 2018 IEEE International Conference on Rebooting Computing (ICRC

Temperature perturbation model of the opto-galvanic effect in CO2-laser discharges

A detailed discharge model of the opto-galvanic effect in molecular laser gas mixtures is developed based on the temperature perturbation or discharge cooling mechanism of Smith and Brooks (1979). Excellent agreement between the model and experimental results in CO2 laser gas mixtures is obtained. The model should be applicable to other molecular systems where the OGE is being used for laser stabilisation and as a spectroscopic tool

An Afro-Asian nexus: South African multinational firm experiences in Chinese labour markets - key focus areas

This exploratory study examines perspectives of multinational corporations (MNCs) from South Africa (SA) in respect of the variables considered important in product and labour markets in China. These include how MNCs first interpret and understand cultural, human capital, regulatory factors and employment practices, before considering how they might adapt to or seek to influence them. A survey of thirteen SA firms operating or trading in these markets and interviews with South Africans who had undertaken exploratory assignments in China, were done. Key factors were identified and evaluated based on relevant literature and research. The following six focus areas were found to be important for business effectiveness in this market: understanding its market complexity, importance of joint venture partners, guanxi relationship networks, human capital, language and culture, and regulatory environment

The Luminosity Function of Galaxies in Compact Groups

From R-band images of 39 Hickson compact groups (HCGs), we use galaxy counts to determine a luminosity function extending to M_R=-14.0, approximately two magnitudes deeper than previous compact group luminosity functions. We find that a single Schechter function is a poor fit to the data, so we fit a composite function consisting of separate Schechter functions for the bright and faint galaxies. The bright end is best fit with M^*=-21.6 and alpha=-0.52 and the faint end with M^*=-16.1 and alpha=-1.17. The decreasing bright end slope implies a deficit of intermediate luminosity galaxies in our sample of HCGs and the faint end slope is slightly steeper than that reported for earlier HCG luminosity functions. Furthermore, luminosity functions of subsets of our sample reveal more substantial dwarf populations for groups with x-ray halos, groups with tidal dwarf candidates, and groups with a dominant elliptical or lenticular galaxy. Collectively, these results support the hypothesis that within compact groups, the initial dwarf galaxy population is replenished by "subsequent generations" formed in the tidal debris of giant galaxy interactions.Comment: 26 pages, to be published in The Astrophysical Journal, 8 greyscale plates (figures 1 and 2) can be retrieved at http://www.astro.psu.edu/users/sdh/pubs.htm

Deep learning based classification of sheep behaviour from accelerometer data with imbalance

Classification of sheep behaviour from a sequence of tri-axial accelerometer data has the potential to enhance sheep management. Sheep behaviour is inherently imbalanced (e.g., more ruminating than walking) resulting in underperforming classification for the minority activities which hold importance. Existing works have not addressed class imbalance and use traditional machine learning techniques, e.g., Random Forest (RF). We investigated Deep Learning (DL) models, namely, Long Short Term Memory (LSTM) and Bidirectional LSTM (BLSTM), appropriate for sequential data, from imbalanced data. Two data sets were collected in normal grazing conditions using jaw-mounted and ear-mounted sensors. Novel to this study, alongside typical single classes, e.g., walking, depending on the behaviours, data samples were labelled with compound classes, e.g., walking_grazing. The number of steps a sheep performed in the observed 10 s time window was also recorded and incorporated in the models. We designed several multi-class classification studies with imbalance being addressed using synthetic data. DL models achieved superior performance to traditional ML models, especially with augmented data (e.g., 4-Class + Steps: LSTM 88.0%, RF 82.5%). DL methods showed superior generalisability on unseen sheep (i.e., F1-score: BLSTM 0.84, LSTM 0.83, RF 0.65). LSTM, BLSTM and RF achieved sub-millisecond average inference time, making them suitable for real-time applications. The results demonstrate the effectiveness of DL models for sheep behaviour classification in grazing conditions. The results also demonstrate the DL techniques can generalise across different sheep. The study presents a strong foundation of the development of such models for real-time animal monitoring

The Evolution of Early-type Field Galaxies Selected from a NICMOS Map of the Hubble Deep Field North

The redshift distribution of well-defined samples of distant early-type galaxies offers a means to test the predictions of monolithic and hierarchical galaxy formation scenarios. NICMOS maps of the entire Hubble Deep Field North in the F110W and F160W filters, when combined with the available WFPC2 data, allow us to calculate photometric redshifts and determine the morphological appearance of galaxies at rest-frame optical wavelengths out to z ~ 2.5. Here we report results for two subsamples of early-type galaxies, defined primarily by their morphologies in the F160W band, which were selected from the NICMOS data down to H160_{AB} < 24.0. The observed redshift distributions of our two early-type samples do not match that predicted by a monolithic collapse model, which shows an overabundance at z > 1.5. A hierarchical formation model better matches the redshift distribution of the HDF-N early-types at z > 1.5, but still does not adequately describe the observed early-types. The hierarchical model predicts significantly bluer colors on average than the observed early-type colors, and underpredicts the observed number of early-types at z < 1. [abridged]Comment: Accepted for publication in the Astronomical Journal; 54 pages, 21 figures. Figures 10 and 11 are included separately in JPEG forma

Morphology and evolution of emission line galaxies in the Hubble Ultra Deep Field

We investigate the properties and evolution of a sample of galaxies selected to have prominent emission lines in low-resolution grism spectra of the Hubble Ultra Deep Field (HUDF). These objects, eGRAPES, are late type blue galaxies, characterized by small proper sizes (R_50 < 2 kpc) in the 4350A rest-frame, low masses (5x10^9 M_sun), and a wide range of luminosities and surface brightnesses. The masses, sizes and volume densities of these objects appear to change very little up to a redshift of z=1.5. On the other hand, their surface brightness decreases significantly from z=1.5 to z=0 while their mass-to-light ratio increases two-folds. This could be a sign that most of low redshift eGRAPES have an older stellar population than high redshift eGRAPES and hence that most eGRAPES formed at higher redshifts. The average volume density of eGRAPES is (1.8 \pm 0.3)x10^{-3} Mpc^{-3} between 0.3 < z < 1.5. Many eGRAPES would formally have been classified as Luminous Compact Blue Galaxies (LCBGs) if these had been selected based on small physical size, blue intrinsic color, and high surface brightness, while the remainder of the sample discussed in this paper forms an extension of LCBGs towards fainter luminosities.Comment: Accepted, to appear in Ap