Rechtliche Fragen zur Praxis IT-forensischer Analysen in Organisationen

Der Einsatz von Informationstechnologie (IT) in Organisationen bietet viele M?glichkeiten des Missbrauchs. Besch?ftigte k?nnen sich die besonderen Eigenschaften der IT zunutze machen, um sich etwa in gesch?ftssch?digender Weise Vorteile zu verschaffen. Gleichzeitig erm?glicht IT aber auch, verbotene Handlungen aufzuspüren und zu belegen (IT-Forensik). Der Beitrag beschreibt die technische Praxis von IT-forensischen Analysen und geht auf damit verbundene rechtliche Fragestellungen ein.     

Decidability of operation problems for T0L languages and subclasses

We investigate the decidability of the operation problem for T0L languages and subclasses. Fix an operation on formal languages. Given languages from the family considered (0L languages, T0L languages, or their propagating variants), is the application of this operation to the given languages still a language that belongs to the same language family? Observe, that all the Lindenmayer language families in question are anti-AFLs, that is, they are not closed under homomorphisms, inverse homomorphisms, intersection with regular languages, union, concatenation, and Kleene closure. Besides these classical operations we also consider intersection and substitution, since the language families under consideration are not closed under these operations, too. We show that for all of the above mentioned language operations, except for the Kleene closure, the corresponding operation problems of 0L and T0L languages and their propagating variants are not even semidecidable. The situation changes for unary 0L languages. In this case we prove that the operation problems with respect to Kleene star, complementation, and intersection with regular sets are decidable.     

Asymmetric multivariate normal mixture GARCH

An asymmetric multivariate generalization of the recently proposed class of normal mixture GARCH models is developed. Issues of parametrization and estimation are discussed. Conditions for covariance stationarity and the existence of the fourth moment are derived, and expressions for the dynamic correlation structure of the process are provided. In an application to stock market returns, it is shown that the disaggregation of the conditional (co)variance process generated by the model provides substantial intuition. Moreover, the model exhibits a strong performance in calculating out-of-sample Value-at-Risk measures.     

Rule cubes for causal investigations

With the complexity of modern vehicles tremendously increasing, quality engineers play a key role within today’s automotive industry. Field data analysis supports corrective actions in development, production and after sales support. We decompose the requirements and show that association rules, being a popular approach to generating explanative models, still exhibit shortcomings. Interactive rule cubes, which have been proposed recently, are a promising alternative. We extend this work by introducing a way of intuitively visualizing and meaningfully ranking them. Moreover, we present methods to interactively factorize a problem and validate hypotheses by ranking patterns based on expectations, and by browsing a cube-based network of related influences. All this is currently in use as an interactive tool for warranty data analysis in the automotive industry. A real-world case study shows how engineers successfully use it in identifying root causes of quality issues.

Semisimple algebras of almost minimal rank over the reals

A famous lower bound for the bilinear complexity of the multiplication in associative algebras is the Alder–Strassen bound. Algebras for which this bound is tight are called algebras of minimal rank. After 25 years of research, these algebras are now well understood. Here we start the investigation of the algebras for which the Alder–Strassen bound is off by one. As a first result, we completely characterize the semisimple algebras over R$\mathbb{R}$ whose bilinear complexity is by one larger than the Alder–Strassen bound. Furthermore, we characterize all algebras A$A$ (with radical) of minimal rank plus one over R$\mathbb{R}$ for which A/radA$A/radA$ has minimal rank plus one. The other possibility is that A/radA$A/radA$ has minimal rank. For this case, we only present a partial result.     

Improving convergence of Iterative Feedback Tuning

Iterative Feedback Tuning constitutes an attractive control loop tuning method for processes in the absence of an accurate process model. It is a purely data driven approach aiming at optimizing the closed loop performance. The standard formulation ensures an unbiased estimate of the loop performance cost function gradient with respect to the control parameters. This gradient is important in a search algorithm. The extension presented in this paper further ensures informative data to improve the convergence properties of the method and hence reduce the total number of required plant experiments especially when tuning for disturbance rejection. Informative data is achieved through application of an external probing signal in the tuning algorithm. The probing signal is designed based on a constrained optimization which utilizes an approximate black box model of the process. This model estimate is further used to guarantee nominal stability and to improve the parameter update using a line search algorithm for determining the iteration step size. The proposed algorithm is compared to the classical formulation in a simulation study of a disturbance rejection problem. This type of problem is notoriously difficult for Iterative Feedback Tuning due to the lack of excitation from the reference.     

Where are you going? Using human locomotion models for target estimation

To explore virtual environments that are larger than the available physical tracking space by real walking, it is necessary to use so-called redirected walking. Redirection techniques allow the user to explore an unlimited virtual environment in a limited tracking space by introducing a small mismatch between a user’s real and virtual movement, thus preventing the user from colliding with the physical walls of the tracking space. Steering algorithms are used to select the most suitable redirection technique at any given time, depending on the geometry of the real and virtual environment. Together with prediction of a user’s future walking path, these algorithms select the best redirection strategy by an optimal control scheme. In this paper, a new approach for the prediction of a person’s locomotion target is presented. We use various models of human locomotion together with a set of possible targets to create a set of expected paths. These paths are then compared to the real path the user already traveled to calculate the probability of a certain target being the one the user is heading for. A new approach for comparing paths with each other is introduced and is compared to three others. For describing the human’s path to a given target, four different models are used and compared. To gather data for the comparison of the models against the real path, a user study was conducted. Based on the results of the user study, the paper concludes with a discussion on the prediction performance of the different approaches.