Methodology for the Automated Metadata-Based Classification of Incriminating Digital Forensic Artefacts

The ever increasing volume of data in digital forensic investigation is one of the most discussed challenges in the field. Usually, most of the file artefacts on seized devices are not pertinent to the investigation. Manually retrieving suspicious files relevant to the investigation is akin to finding a needle in a haystack. In this paper, a methodology for the automatic prioritisation of suspicious file artefacts (i.e., file artefacts that are pertinent to the investigation) is proposed to reduce the manual analysis effort required. This methodology is designed to work in a human-in-the-loop fashion. In other words, it predicts/recommends that an artefact is likely to be suspicious rather than giving the final analysis result. A supervised machine learning approach is employed, which leverages the recorded results of previously processed cases. The process of features extraction, dataset generation, training and evaluation are presented in this paper. In addition, a toolkit for data extraction from disk images is outlined, which enables this method to be integrated with the conventional investigation process and work in an automated fashion

Water at an electrochemical interface - a simulation study

The results of molecular dynamics simulations of the properties of water in an aqueous ionic solution close to an interface with a model metallic electrode are described. In the simulations the electrode behaves as an ideally polarizable hydrophilic metal, supporting image charge interactions with charged species, and it is maintained at a constant electrical potential with respect to the solution so that the model is a textbook representation of an electrochemical interface through which no current is passing. We show how water is strongly attracted to and ordered at the electrode surface. This ordering is different to the structure that might be imagined from continuum models of electrode interfaces. Further, this ordering significantly affects the probability of ions reaching the surface. We describe the concomitant motion and configurations of the water and ions as functions of the electrode potential, and we analyze the length scales over which ionic atmospheres fluctuate. The statistics of these fluctuations depend upon surface structure and ionic strength. The fluctuations are large, sufficiently so that the mean ionic atmosphere is a poor descriptor of the aqueous environment near a metal surface. The importance of this finding for a description of electrochemical reactions is examined by calculating, directly from the simulation, Marcus free energy profiles for transfer of charge between the electrode and a redox species in the solution and comparing the results with the predictions of continuum theories. Significant departures from the electrochemical textbook descriptions of the phenomenon are found and their physical origins are characterized from the atomistic perspective of the simulations.Comment: 29 pages, 15 figure

The Crystal Structure of the Extracellular 11-heme Cytochrome UndA Reveals a Conserved 10-heme Motif and Defined Binding Site for Soluble Iron Chelates

Members of the genus Shewanella translocate deca- or undeca-heme cytochromes to the external cell surface thus enabling respiration using extracellular minerals and polynuclear Fe(III) chelates. The high resolution structure of the first undeca-heme outer membrane cytochrome, UndA, reveals a crossed heme chain with four potential electron ingress/egress sites arranged within four domains. Sequence and structural alignment of UndA and the deca-heme MtrF reveals the extra heme of UndA is inserted between MtrF hemes 6 and 7. The remaining UndA hemes can be superposed over the heme chain of the decaheme MtrF, suggesting that a ten heme core is conserved between outer membrane cytochromes. The UndA structure has also been crystallographically resolved in complex with substrates, an Fe(III)-nitrilotriacetate dimer or an Fe(III)-citrate trimer. The structural resolution of these UndA-Fe(III)-chelate complexes provides a rationale for previous kinetic measurements on UndA and other outer membrane cytochromes

A Network Approach to Understanding Narcissistic Grandiosity via the Narcissistic Admiration and Rivalry Questionnaire and the Narcissistic Personality Inventory

The narcissistic admiration and rivalry concept (NARC) model of grandiose narcissism posits that striving for uniqueness, grandiose fantasies, and charmingness define narcissistic admiration, whereas striving for supremacy, devaluation, and aggressiveness define narcissistic rivalry. Given these complex interrelationships, we explored the structure of grandiose narcissism using the Narcissistic Admiration and Rivalry Questionnaire (NARQ) and Narcissistic Personality Inventory (NPI) via network analysis in four separate samples which allowed us to assess the extent to which these networks replicated across these samples (total N = 3,868). Overall, grandiose cognitions from the NARQ emerged as a highly central node in each network, providing compound evidence for its replicability and generalizability as an important feature of grandiose narcissism within the NARC model. Charmingness from the NARQ emerged as a central node throughout Samples 1, 2, and 3, with strong connections to features of narcissistic admiration and narcissistic rivalry (e.g., grandiose fantasies and aggressiveness), but was less central in Sample 4. To our knowledge, this is the first research to examine the replicability of the network structure of grandiose narcissism across various samples. These findings add to an increasingly important dialogue regarding replicability in psychological network science

A network-based ranking system for American college football

American college football faces a conflict created by the desire to stage national championship games between the best teams of a season when there is no conventional playoff system to decide which those teams are. Instead, ranking of teams is based on their record of wins and losses during the season, but each team plays only a small fraction of eligible opponents, making the system underdetermined or contradictory or both. It is an interesting challenge to create a ranking system that at once is mathematically well-founded, gives results in general accord with received wisdom concerning the relative strengths of the teams, and is based upon intuitive principles, allowing it to be accepted readily by fans and experts alike. Here we introduce a one-parameter ranking method that satisfies all of these requirements and is based on a network representation of college football schedules.Comment: 15 pages, 3 figure

A Multi-Armed Bandit to Smartly Select a Training Set from Big Medical Data

With the availability of big medical image data, the selection of an adequate training set is becoming more important to address the heterogeneity of different datasets. Simply including all the data does not only incur high processing costs but can even harm the prediction. We formulate the smart and efficient selection of a training dataset from big medical image data as a multi-armed bandit problem, solved by Thompson sampling. Our method assumes that image features are not available at the time of the selection of the samples, and therefore relies only on meta information associated with the images. Our strategy simultaneously exploits data sources with high chances of yielding useful samples and explores new data regions. For our evaluation, we focus on the application of estimating the age from a brain MRI. Our results on 7,250 subjects from 10 datasets show that our approach leads to higher accuracy while only requiring a fraction of the training data.Comment: MICCAI 2017 Proceeding

Clinical Review : The suggested management pathway for urticaria in primary care

ACKNOWLEDGEMENTS There was no funding and all the work was done by the authors.Peer reviewedPublisher PD

Radio-frequency methods for Majorana-based quantum devices: fast charge sensing and phase diagram mapping

Radio-frequency (RF) reflectometry is implemented in hybrid semiconductor-superconductor nanowire systems designed to probe Majorana zero modes. Two approaches are presented. In the first, hybrid nanowire-based devices are part of a resonant circuit, allowing conductance to be measured as a function of several gate voltages ~40 times faster than using conventional low-frequency lock-in methods. In the second, nanowire devices are capacitively coupled to a nearby RF single-electron transistor made from a separate nanowire, allowing RF detection of charge, including charge-only measurement of the crossover from 2e inter-island charge transitions at zero magnetic field to 1e transitions at axial magnetic fields above 0.6 T, where a topological state is expected. Single-electron sensing yields signal-to-noise exceeding 3 and visibility 99.8% for a measurement time of 1 {\mu}s

Clines, Clusters, and the Effect of Study Design on the Inference of Human Population Structure

Previously, we observed that without using prior information about individual sampling locations, a clustering algorithm applied to multilocus genotypes from worldwide human populations produced genetic clusters largely coincident with major geographic regions. It has been argued, however, that the degree of clustering is diminished by use of samples with greater uniformity in geographic distribution, and that the clusters we identified were a consequence of uneven sampling along genetic clines. Expanding our earlier dataset from 377 to 993 markers, we systematically examine the influence of several study design variables—sample size, number of loci, number of clusters, assumptions about correlations in allele frequencies across populations, and the geographic dispersion of the sample—on the “clusteredness” of individuals. With all other variables held constant, geographic dispersion is seen to have comparatively little effect on the degree of clustering. Examination of the relationship between genetic and geographic distance supports a view in which the clusters arise not as an artifact of the sampling scheme, but from small discontinuous jumps in genetic distance for most population pairs on opposite sides of geographic barriers, in comparison with genetic distance for pairs on the same side. Thus, analysis of the 993-locus dataset corroborates our earlier results: if enough markers are used with a sufficiently large worldwide sample, individuals can be partitioned into genetic clusters that match major geographic subdivisions of the globe, with some individuals from intermediate geographic locations having mixed membership in the clusters that correspond to neighboring regions