901 resultados para algebraic attacks


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We investigate the differences --- conceptually and algorithmically --- between affine and projective frameworks for the tasks of visual recognition and reconstruction from perspective views. It is shown that an affine invariant exists between any view and a fixed view chosen as a reference view. This implies that for tasks for which a reference view can be chosen, such as in alignment schemes for visual recognition, projective invariants are not really necessary. We then use the affine invariant to derive new algebraic connections between perspective views. It is shown that three perspective views of an object are connected by certain algebraic functions of image coordinates alone (no structure or camera geometry needs to be involved).

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The pea aphid, Acyrthosiphon pisum Harris (Hemiptera: Aphididae) is found in red and green color morphs. Previous work has suggested that the aphidiine parasitoid Aphidius ervi Haliday preferentially attacks green pea aphids in the field. It is not clear whether these results reflect a real preference, or some unknown clonal difference, such as in immunity, between the aphids used in the previous studies. We used three susceptibility-matched pairs of red and green morph pea aphid clones to test for preferences. In a no-choice situation, the parasitoids attacked equal proportions of each color morph. When provided with a choice, A. ervi was significantly more likely to oviposit into colonies formed from green morphs when the neighboring colony was formed from red morph aphids. In contrast, red morphs were less likely to be attacked when their neighboring colony was of the green morph. By preferentially attacking green colonies, A. ervi may reduce the likelihood of intraguild predation, as it is suggested that visually foraging predators preferentially attack red aphid colonies. Furthermore, if this host choice behavior is replicated in the field, we speculate that color morphs of the pea aphid may interact indirectly through their shared natural enemies, leading to intraspecific apparent competition.

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An extensive set of machine learning and pattern classification techniques trained and tested on KDD dataset failed in detecting most of the user-to-root attacks. This paper aims to provide an approach for mitigating negative aspects of the mentioned dataset, which led to low detection rates. Genetic algorithm is employed to implement rules for detecting various types of attacks. Rules are formed of the features of the dataset identified as the most important ones for each attack type. In this way we introduce high level of generality and thus achieve high detection rates, but also gain high reduction of the system training time. Thenceforth we re-check the decision of the user-to- root rules with the rules that detect other types of attacks. In this way we decrease the false-positive rate. The model was verified on KDD 99, demonstrating higher detection rates than those reported by the state- of-the-art while maintaining low false-positive rate.

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This paper describes a method for the state estimation of nonlinear systems described by a class of differential-algebraic equation models using the extended Kalman filter. The method involves the use of a time-varying linearisation of a semi-explicit index one differential-algebraic equation. The estimation technique consists of a simplified extended Kalman filter that is integrated with the differential-algebraic equation model. The paper describes a simulation study using a model of a batch chemical reactor. It also reports a study based on experimental data obtained from a mixing process, where the model of the system is solved using the sequential modular method and the estimation involves a bank of extended Kalman filters.

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An iterative procedure is described for solving nonlinear optimal control problems subject to differential algebraic equations. The procedure iterates on an integrated modified simplified model based problem with parameter updating in such a manner that the correct solution of the original nonlinear problem is achieved.

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A novel iterative procedure is described for solving nonlinear optimal control problems subject to differential algebraic equations. The procedure iterates on an integrated modified linear quadratic model based problem with parameter updating in such a manner that the correct solution of the original non-linear problem is achieved. The resulting algorithm has a particular advantage in that the solution is achieved without the need to solve the differential algebraic equations . Convergence aspects are discussed and a simulation example is described which illustrates the performance of the technique. 1. Introduction When modelling industrial processes often the resulting equations consist of coupled differential and algebraic equations (DAEs). In many situations these equations are nonlinear and cannot readily be directly reduced to ordinary differential equations.

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Since the terrorist attacks of September 11, 2001, international law has had to grapple with the fundamental challenges that large-scale violence carried out by non-State actors poses to the traditional inter- State orientation of international law. Questions related to the “adequacy” and “effectiveness” of international humanitarian law, international human rights law and the law related to the use of force have been particularly pronounced. This paper focuses on the international humanitarian law implications of American drone attacks in northwest Pakistan. A highly-advanced modality of modern warfare, armed drones highlight the possibilities, problems, prospects and pitfalls of high-tech warfare. How is the battlefield to be defined and delineated geographically and temporally? Who can be targeted, and by whom? Ultimately, this paper concludes that American drone attacks in northwest Pakistan are not unlawful as such under international humanitarian law, though, like any tactical decision in the context of asymmetric warfare, they should be continuously and closely monitored according to the dictates of law with sensitivity to facts on the ground.