Fast Algorithms for the Computation of Ranklets

Ranklets are orientation selective rank features with applications to tracking, face detection, texture and medical imaging. We introduce efficient algorithms that reduce their computational complexity from O(N logN) to O(!N + k), where N is the area of the filter. Timing tests show a speedup of one order of magnitude for typical usage, which should make Ranklets attractive for real-time applications

On the probabilistic logical modelling of quantum and geometrically-inspired IR

Information Retrieval approaches can mostly be classed into probabilistic, geometric or logic-based. Recently, a new unifying framework for IR has emerged that integrates a probabilistic description within a geometric framework, namely vectors in Hilbert spaces. The geometric model leads naturally to a predicate logic over linear subspaces, also known as quantum logic. In this paper we show the relation between this model and classic concepts such as the Generalised Vector Space Model, highlighting similarities and differences. We also show how some fundamental components of quantum-based IR can be modelled in a descriptive way using a well-established tool, i.e. Probabilistic Datalog

Cybersecurity Games and Investments: A Decision Support Approach

Abstract. In this paper we investigate how to optimally invest in cyber-security controls. We are particularly interested in examining cases where the organization suffers from an underinvestment problem or inefficient spending on cybersecurity. To this end, we first model the cybersecurity environment of an organization. We then model non-cooperative cyber-security control-games between the defender which abstracts all defense mechanisms of the organization and the attacker which can exploit dif-ferent vulnerabilities at different network locations. To implement our methodology we use the SANS Top 20 Critical Security Controls and the 2011 CWE/SANS top 25 most dangerous software errors. Based on the profile of an organization, which forms its preferences in terms of indirect costs, its concerns about different kinds of threats and the im-portance of the assets given their associated risks we derive the Nash Equilibria of a series of control-games. These game solutions are then handled by optimization techniques, in particular multi-objective, multi-ple choice Knapsack to determine the optimal cybersecurity investment. Our methodology provides security effective and cost efficient solutions especially against commodity attacks. We believe our work can be used to advise security managers on how they should spend an available cy-bersecurity budget given their organization profile

Decision support approaches for cyber security investment

When investing in cyber security resources, information security managers have to follow effective decisionmaking strategies. We refer to this as the cyber security investment challenge.In this paper, we consider three possible decision support methodologies for security managers to tackle this challenge. We consider methods based on game theory, combinatorial optimisation, and a hybrid of the two. Our modelling starts by building a framework where we can investigate the effectiveness of a cyber security control regarding the protection of different assets seen as targets in presence of commodity threats. As game theory captures the interaction between the endogenous organisation’s and attackers’ decisions, we consider a 2-person control game between the security manager who has to choose among different implementation levels of a cyber security control, and a commodity attacker who chooses among different targets to attack. The pure game theoretical methodology consists of a large game including all controls and all threats. In the hybrid methodology the game solutions of individual control-games along with their direct costs (e.g. financial) are combined with a Knapsack algorithm to derive an optimal investment strategy. The combinatorial optimisation technique consists of a multi-objective multiple choice Knapsack based strategy. To compare these approaches we built a decision support tool and a case study regarding current government guidelines. The endeavour of this work is to highlight the weaknesses and strengths of different investment methodologies for cyber security, the benefit of their interaction, and the impact that indirect costs have on cyber security investment. Going a step further in validating our work, we have shown that our decision support tool provides the same advice with the one advocated by the UK government with regard to the requirements for basic technical protection from cyber attacks in SMEs

GEOexplorer: a webserver for gene expression analysis and visualisation

Gene Expression Omnibus (GEO) is a database repository hosting a substantial proportion of publicly available high throughput gene expression data. Gene expression analysis is a powerful tool to gain insight into the mechanisms and processes underlying the biological and phenotypic differences between sample groups. Despite the wide availability of gene expression datasets, their access, analysis, and integration are not trivial and require specific expertise and programming proficiency. We developed the GEOexplorer webserver to allow scientists to access, integrate and analyse gene expression datasets without requiring programming proficiency. Via its user-friendly graphic interface, users can easily apply GEOexplorer to perform interactive and reproducible gene expression analysis of microarray and RNA-seq datasets, while producing a wealth of interactive visualisations to facilitate data exploration and interpretation, and generating a range of publication ready figures. The webserver allows users to search and retrieve datasets from GEO as well as to upload user-generated data and combine and harmonise two datasets to perform joint analyses. GEOexplorer, available at https://geoexplorer.rosalind.kcl.ac.uk, provides a solution for performing interactive and reproducible analyses of microarray and RNA-seq gene expression data, empowering life scientists to perform exploratory data analysis and differential gene expression analysis on-the-fly without informatics proficiency

Ranklets: Orientation Selective Non-Parametric Features Applied to Face Detection

We introduce a family of multiscale, orientation-selective, non-parametric features ("ranklets") modelled on Haar wavelets. We clarify their relation to the Wilcoxon ranksum test and the rank transform and provide an efficient scheme for computation based on the Mann-Whitney statistics. Finally, we show that ranklets outperform other rank features, Haar wavelets, SNoW and linear SVMs (based on independently published results) in face detection experiments over the 24 045 test images in the MITCBCL database