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Attack-defense trees extend attack trees with defense nodes. This richer formalism allows for a more precise modeling of a system's vulnerabilities, by representing interactions between possible attacks and corresponding defensive measures. In this paper we compare the computational complexity of both formalisms. We identify semantics for which extending attack trees with defense nodes does not increase the computational complexity. This implies that, for these semantics, every query that can be solved efficiently on attack trees can also be solved efficiently on attack-defense trees. Furthermore, every algorithm for attack trees can directly be used to process attack-defense trees. IntroductionSystems become more and more complex as technology is advancing faster and faster. This technological development goes along with more sophisticated attacks on systems. In 1999, Schneier [1] suggested attack trees as a visual method to evaluate the security of complex systems. An attack tree is an AND-OR structure detailing an attack scenario. Schneier advocated attack trees, but he was not the first to suggest such an approach. Weiss [2] and Amoroso [3] were two pioneers in the usage of trees in security analysis. But even as early as the 1960s, tree-like structures were used in risk analysis, see Vesely et al. [4]. In 2005, Mauw and Oostdijk [5] augmented attack trees with semantics, providing a solid, formal and methodological framework for security assessment. Since then, the attack tree methodology has been taken up by numerous researchers, see [6][7][8][9][10][11].Attack trees are widely used to evaluate vulnerabilities of systems. However, there are several important aspects of security that they cannot model. Besides the fact that the attack tree formalism only considers an attacker's point of view, it can neither capture the interaction between an attacker and a defender, nor is it well-suited to depict the evolution of attacks and subsequent defenses.To overcome these limitations, Kordy et al. recently extended the attack tree formalism with defensive measures, by introducing attack-defense trees, see [12]. A main difference between attack trees and attack-defense trees is that the latter

Background Assessment of psoriasis severity is strongly observer-dependent, and objective assessment tools are largely missing. The increasing number of patients receiving highly expensive therapies that are reimbursed only for moderate-to-severe psoriasis motivates the development of higher quality assessment tools. Objective To establish an accurate and objective psoriasis assessment method based on segmenting images by machine learning technology. Methods In this retrospective, non-interventional, single-centred, interdisciplinary study of diagnostic accuracy, 259 standardized photographs of Caucasian patients were assessed and typical psoriatic lesions were labelled. Two hundred and three of those were used to train and validate an assessment algorithm which was then tested on the remaining 56 photographs. The results of the algorithm assessment were compared with manually marked area, as well as with the affected area determined by trained dermatologists. Results Algorithm assessment achieved accuracy of more than 90% in 77% of the images and differed on average 5.9% from manually marked areas. The difference between algorithm-predicted and photograph-based estimated areas by physicians was 8.1% on average. Conclusion The study shows the potential of the evaluated technology. In contrast to the Psoriasis Area and Severity Index (PASI), it allows for objective evaluation and should therefore be developed further as an alternative method to human assessment.

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This paper develops a new uncertainty measure for the theory of hints that complies with the established semantics of statistical information theory and further satisfies all classical requirements for such a measure imposed in the literature. The proposed functional decomposes into conversant uncertainty measures and therefore discloses a new interpretation of the latters as well. By abstracting to equivalence classes of hints we transport the new measure to mass functions in Dempster-Shafer theory and analyse its relationship with the aggregate uncertainty, which currently is the only known functional for the DempsterShafer theory of evidence that satisfies the same set of properties. Moreover, the perspective of hints reveals that the standard independence notion in Dempster-Shafer theory called non-interactivity corresponds to an amalgamation of probabilistic independence and qualitative independence between frames of discernment. All results in this paper are developed for arbitrary families of compatible frames generalizing the very specialized multi-variate systems that are usually studied in information theory.