When investing in cyber security resources, information security managers have to follow effective decisionmaking\ud strategies. We refer to this as the cyber security investment challenge.In this paper, we consider\ud three possible decision support methodologies for security managers to tackle this challenge. We consider\ud methods based on game theory, combinatorial optimisation, and a hybrid of the two. Our modelling starts\ud by building a framework where we can investigate the effectiveness of a cyber security control regarding\ud the protection of different assets seen as targets in presence of commodity threats. As game theory captures\ud the interaction between the endogenous organisation’s and attackers’ decisions, we consider a 2-person\ud control game between the security manager who has to choose among different implementation levels of a\ud cyber security control, and a commodity attacker who chooses among different targets to attack. The pure\ud game theoretical methodology consists of a large game including all controls and all threats. In the hybrid\ud methodology the game solutions of individual control-games along with their direct costs (e.g. financial) are\ud combined with a Knapsack algorithm to derive an optimal investment strategy. The combinatorial optimisation\ud technique consists of a multi-objective multiple choice Knapsack based strategy. To compare these\ud approaches we built a decision support tool and a case study regarding current government guidelines. The\ud endeavour of this work is to highlight the weaknesses and strengths of different investment methodologies\ud for cyber security, the benefit of their interaction, and the impact that indirect costs have on cyber security\ud investment. Going a step further in validating our work, we have shown that our decision support tool provides\ud the same advice with the one advocated by the UK government with regard to the requirements for\ud basic technical protection from cyber attacks in SMEs
Abstract-We study spatial learning and navigation for autonomous agents. A state space representation is constructed by unsupervised Hebbian learning during exploration. As a result of learning, a representation of the continuous two-dimensional (2-D) manifold in the high-dimensional input space is found. The representation consists of a population of localized overlapping place fields covering the 2-D space densely and uniformly. This space coding is comparable to the representation provided by hippocampal place cells in rats. Place fields are learned by extracting spatio-temporal properties of the environment from sensory inputs. The visual scene is modeled using the responses of modified Gabor filters placed at the nodes of a sparse Log-polar graph. Visual sensory aliasing is eliminated by taking into account self-motion signals via path integration. This solves the hidden state problem and provides a suitable representation for applying reinforcement learning in continuous space for action selection. A temporal-difference prediction scheme is used to learn sensorimotor mappings to perform goal-oriented navigation. Population vector coding is employed to interpret ensemble neural activity. The model is validated on a mobile Khepera miniature robot.
The paper presents results of the face verification contest that was organized in conjunction with International Conference on Pattern Recognition 2000 [14]. Participants had to use identical data sets from a large, publicly available multimodal database XM2VTSDB. Training and evaluation was carried out according to an a priori known protocol ([7]). Verification results of all tested algorithms have been collected and made public on the XM2VTSDB website [15], facilitating large scale experiments on classifier combination and fusion. Tested methods included, among others, representatives of the most common approaches to face verificationelastic graph matching, Fisher's linear discriminant and Support vector machines.
Deficits in impulse control (difficulties in inhibition of a pre-potent response) are fundamental to a number of psychiatric disorders, but the molecular and cellular basis is poorly understood. Zebrafish offer a very useful model for exploring these mechanisms, but there is currently a lack of validated procedures for measuring impulsivity in fish. In mammals, impulsivity can be measured by examining rates of anticipatory responding in the 5-choice serial reaction time task (5-CSRTT), a continuous performance task where the subject is reinforced upon accurate detection of a briefly presented light in one of five distinct spatial locations. This paper describes the development of a fully-integrated automated system for testing impulsivity in adult zebrafish. We outline the development of our image analysis software and its integration with National Instruments drivers and actuators to produce the system. We also describe an initial validation of the system through a one-generation screen of chemically mutagenized zebrafish, where the testing parameters were optimized.
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