Eigenkapitalverzehr und Substanzbesteuerung deutscher Unternehmen durch eine Vermögensteuer – eine empirische Analyse - Hoppe, Thomas; Maiterth, Ralf; Sureth-Sloane, Caren (Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung)
Die (Wieder-)Einführung einer Vermögensteuer ist in den vergangenen Jahren erneut in den Fokus der politischen Diskussion gerückt. Der vorliegende Beitrag vermittelt einen Eindruck von den Belastungswirkungen, die aus der Umsetzung von aktuell vorliegenden Besteuerungskonzepten resultieren würden. Auf der Basis von realen Jahresabschlussdaten wird eine mehrperiodige Veranlagungssimulation durchgeführt, die insbesondere ermöglicht, den zu erwartenden Eigenkapitalverzehr sowie den Anstieg der Steuerbelastung für die betrachtete Stichprobe zu quantifizieren. Von besonderem Interesse sind hierbei Unternehmen, deren laufende Erträge nicht ausreichen, um die Belastungen durch die Vermögensteuer zu tragen und damit einem Substanzverzehr ausgesetzt sind. Es zeigt sich, dass etwa die Hälfte der Unternehmen im Untersuchungszeitraum von sechs Jahren in mindestens einem Jahr einen Substanzverzehr erfährt. Der Vermögensteuer kommt somit keinesfalls der vielfach postulierte Charakter einer eher mäßig belastenden und im Wesentlichen substanzverschonenden Steuer zu. Zusatzbelastungen von knapp 100 bis zu 300 % der Ertragsteuerlast sind keine Seltenheit und veranschaulichen das Gefährdungspotenzial dieser Steuer für den Wirtschaftsstandort Deutschland.
Keywords: Steuerbelastung; Substanzbesteuerung; Ungleiche Vermögensverteilung; Veranlagungssimulation; Vermögensteuer

Do talented women shy away from competition? - Hoyer, Britta; van Huizen, Tomas; Keijzer, Linda; Rezai Khavas, Tahere; Rosenkranz, Stephanie
We study the willingness to compete in a cognitive task among an entire cohort of fresh man business and economics students. Combining data from a lab-in-thefield experiment with university admissions data, we trace the gender gap in competitiveness at different levels of high school performance. Our results confirm that, on average, men choose to compete more often. The gender gap disappears, however, among students with above average high school performance. Female high school top performers are equally competitive as their male counterparts. In fact, the overall gender gap is entirely driven by the group of female high school underperformers who shied away from competition, even when they performed well in our task. Overall, our findings suggest that high school grades are more than just a signal of cognitive abilities, because they seem to influence the receivers selfperception of his or her performance in a competitive environment involved in later on in life.

Erkunden mathematischer Strukturen anstatt Interpretation in Modellen – Ein innermathematischer Zugang zu negativen Zahlen - Schumacher, Jan (Beiträge zum Mathematikunterricht 2016)

Projekt KUBAS: Koordination ungebundener Vor-Ort-Helfer - Rauchecker, Gerhard; Schryen, Guido (Im Einsatz)

DevOps for network function virtualisation: an architectural approach - Karl, Holger; Dräxler, Sevil; Peuster, Manuel; Galis, Alex; Bredel, Michael; Ramos, Aurora; Martrat, Josep; Siddiqui, Muhammad Shuaib; van Rossem, Steven; Tavernier, Wouter; Xilouris, George (Transactions on Emerging Telecommunications Technologies)
The Service Programming and Orchestration for Virtualised Software Networks (SONATA) project targets both the flexible programmability of software networks and the optimisation of their deployments by means of integrating Development and Operations in order to accelerate industry adoption of software networks and reduce time-to-market for networked services. SONATA supports network function chaining and orchestration, making service platforms modular and easier to customise to the needs of different service providers, and introduces a specialised Development and Operations model for supporting developers.

Understand Your Chains: Towards Performance Profile-Based Network Service Management - Peuster, Manuel; Karl, Holger (Fifth European Workshop on Software-Defined Networks, EWSDN 2016, Den Haag, The Netherlands, October 10-11, 2016)
Allocating resources to virtualized network functions and services to meet service level agreements is a challenging task for NFV management and orchestration systems. This becomes even more challenging when agile development methodologies, like DevOps, are applied. In such scenarios, management and orchestration systems are continuously facing new versions of functions and services which makes it hard to decide how much resources have to be allocated to them to provide the expected service performance. One solution for this problem is to support resource allocation decisions with performance behavior information obtained by profiling techniques applied to such network functions and services. In this position paper, we analyze and discuss the components needed to generate such performance behavior information within the NFV DevOps workflow. We also outline research questions that identify open issues and missing pieces for a fully integrated NFV profiling solution. Further, we introduce a novel profiling mechanism that is able to profile virtualized network functions and entire network service chains under different resource constraints before they are deployed on production infrastructure.

E-State: Distributed state management in elastic network function deployments - Peuster, Manuel; Karl, Holger (IEEE NetSoft Conference and Workshops, NetSoft 2016, Seoul, South Korea, June 6-10, 2016)
Elastic deployments of virtualized network functions~(VNF) can automatically scale the amount of used resources in relation to their workload. This is often done by starting new VNF instances or stopping old ones. A problem of these scale operations is that most network functions are stateful and their internal state is not automatically migrated when traffic is redistributed in the deployment. As a result, mechanisms are needed to exchange or migrate internal network function state between VNF instances. This paper presents a state management framework that creates a logically distributed state store on top of elastically deployed virtual network functions. We also introduce a novel programming model that provides both a local and a global view of the state to each VNF instance. We discuss the integration of our framework into existing network function virtualization architectures and compare the performance of our prototype to a centralized and a distributed state store solution.

MeDICINE: Rapid Prototyping of Production-Ready Network Services in Multi-PoP Environments - Peuster, Manuel; Karl, Holger; van Rossem, Steven (IEEE Conference on Network Function Virtualization and Software Defined Networks (NFV-SDN))
Virtualized network services consisting of multiple individual network functions are already today deployed across multiple sites, so called multi-PoP (points of presence) environments. This allows to improve service performance by optimizing its placement in the network. But prototyping and testing of these complex distributed software systems becomes extremely challenging. The reason is that not only the network service as such has to be tested but also its integration with management and orchestration systems. Existing solutions, like simulators, basic network emulators, or local cloud testbeds, do not support all aspects of these tasks. To this end, we introduce MeDICINE, a novel NFV prototyping platform that is able to execute production-ready network functions, provided as software containers, in an emulated multi-PoP environment. These network functions can be controlled by any third-party management and orchestration system that connects to our platform through standard interfaces. Based on this, a developer can use our platform to prototype and test complex network services in a realistic environment running on his laptop.

Factors Affecting the Scientific Impact of Literature Reviews: A Scientometric Study - Wagner, Gerit; Prester, Julian; Roche, Maria; Benlian, Alexander; Schryen, Guido (International Conference on Information Systems)

Development of two novel face-recognition CAPTCHAs: a security and usability study - Schryen, Guido; Wagner, Gerit; Schlegel, Alexander (Computers & Security)
CAPTCHAs are challenge-response tests that aim at preventing unwanted machines, including bots, from accessing web services while providing easy access for humans. Recent advances in artificial-intelligence based attacks show that the level of security provided by many state-of-the-art text-based CAPTCHAs is declining. At the same time, techniques for distorting and obscuring the text, which are used to maintain the level of security, make text-based CAPTCHAs diffcult to solve for humans, and thereby further degrade usability. The need for developing alternative types of CAPTCHAs which improve both, the current security and usability levels, has been emphasized by several researchers. With this study, we contribute to research through (1) the development of two new face recognition CAPTCHAs (Farett-Gender and Farett-Gender&Age), (2) the security analysis of both procedures, and (3) the provision of empirical evidence that one of the suggested CAPTCHAs (Farett-Gender) is similar to Google's reCAPTCHA and better than KCAPTCHA concerning effectiveness (error rates), superior to both regarding learnability and satisfaction but not effciency.
Keywords: CAPTCHA; Usability; Facial features; Gender classiffcation; Age classification; Face recognition reverse Turing test

Determination of the material properties of polymers using laser-generated broadband ultrasound - Claes, Leander; Meyer, Thorsten; Bause, Fabian; Rautenberg, Jens; Henning, Bernd (Journal of Sensors and Sensor Systems)

Modeling Cross-Device Systems with Use Case Diagrams - Wolters, Dennis; Gerth, Christian; Engels, Gregor (Proceedings of the CAiSE'18 Forum at the 28th International Conference on Advanced Information Systems Engineering (CAiSE'16))

Placement of Services with Flexible Structures Specified by a YANG Data Model - Dräxler, Sevil; Karl, Holger (Proceedings of the 2nd International IEEE Conference on Network Softwarization (NetSoft))
Network function virtualization and software-defined networking allow services consisting of virtual network functions to be designed and implemented with great flexibility by facilitating automatic deployments, migrations, and reconfigurations for services and their components. For extended flexibility, we go beyond seeing services as a fixed chain of functions. We present a YANG model for describing the service structure in deployment requests in a flexible way that enables changing the order of functions in case the order of traversing them does not affect the functionality of the service. Upon receiving such requests, the network orchestration system can choose the optimal composition of service components that gives the best results for placement of services in the network. This introduces new complexities to the placement problem by greatly increasing the number of possible ways a service can be composed. In this paper, we describe a heuristic solution that selects a Pareto set of the possible compositions of a service as well as possible combinations of different services, with respect to different resource requirements of the services. Our evaluations show that the selected combinations consist of representative samples of possible structures and requirements and therefore, can result in optimal or close-to-optimal placement results.

A Priori SNR Estimation Using a Generalized Decision Directed Approach - Chinaev, Aleksej; Haeb-Umbach, Reinhold (INTERSPEECH 2016, San Francisco, USA)
In this contribution we investigate a priori signal-to-noise ratio (SNR) estimation, a crucial component of a single-channel speech enhancement system based on spectral subtraction. The majority of the state-of-the art a priori SNR estimators work in the power spectral domain, which is, however, not confirmed to be the optimal domain for the estimation. Motivated by the generalized spectral subtraction rule, we show how the estimation of the a priori SNR can be formulated in the so called generalized SNR domain. This formulation allows to generalize the widely used decision directed (DD) approach. An experimental investigation with different noise types reveals the superiority of the generalized DD approach over the conventional DD approach in terms of both the mean opinion score - listening quality objective measure and the output global SNR in the medium to high input SNR regime, while we show that the power spectrum is the optimal domain for low SNR. We further develop a parameterization which adjusts the domain of estimation automatically according to the estimated input global SNR. Index Terms: single-channel speech enhancement, a priori SNR estimation, generalized spectral subtraction

A Priori SNR Estimation Using Weibull Mixture Model - Chinaev, Aleksej; Heitkaemper, Jens; Haeb-Umbach, Reinhold (12. ITG Fachtagung Sprachkommunikation (ITG 2016))
This contribution introduces a novel causal a priori signal-to-noise ratio (SNR) estimator for single-channel speech enhancement. To exploit the advantages of the generalized spectral subtraction, a normalized ?-order magnitude (NAOM) domain is introduced where an a priori SNR estimation is carried out. In this domain, the NAOM coefficients of noise and clean speech signals are modeled by a Weibull distribution and aWeibullmixturemodel (WMM), respectively. While the parameters of the noise model are calculated from the noise power spectral density estimates, the speechWMM parameters are estimated from the noisy signal by applying a causal Expectation-Maximization algorithm. Further a maximum a posteriori estimate of the a priori SNR is developed. The experiments in different noisy environments show the superiority of the proposed estimator compared to the well-known decision-directed approach in terms of estimation error, estimator variance and speech quality of the enhanced signals when used for speech enhancement.

Noise-Presence-Probability-Based Noise PSD Estimation by Using DNNs - Chinaev, Aleksej; Heymann, Jahn; Drude, Lukas; Haeb-Umbach, Reinhold (12. ITG Fachtagung Sprachkommunikation (ITG 2016))
A noise power spectral density (PSD) estimation is an indispensable component of speech spectral enhancement systems. In this paper we present a noise PSD tracking algorithm, which employs a noise presence probability estimate delivered by a deep neural network (DNN). The algorithm provides a causal noise PSD estimate and can thus be used in speech enhancement systems for communication purposes. An extensive performance comparison has been carried out with ten causal state-of-the-art noise tracking algorithms taken from the literature and categorized acc. to applied techniques. The experiments showed that the proposed DNN-based noise PSD tracker outperforms all competing methods with respect to all tested performance measures, which include the noise tracking performance and the performance of a speech enhancement system employing the noise tracking component.

Blind Speech Separation based on Complex Spherical k-Mode Clustering - Drude, Lukas; Boeddeker, Christoph; Haeb-Umbach, Reinhold (Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP))

On the appropriateness of complex-valued neural networks for speech enhancement - Drude, Lukas; Raj, Bhiksha; Haeb-Umbach, Reinhold (INTERSPEECH 2016, San Francisco, USA)
Although complex-valued neural networks (CVNNs) â?? networks which can operate with complex arithmetic â?? have been around for a while, they have not been given reconsideration since the breakthrough of deep network architectures. This paper presents a critical assessment whether the novel tool set of deep neural networks (DNNs) should be extended to complex-valued arithmetic. Indeed, with DNNs making inroads in speech enhancement tasks, the use of complex-valued input data, specifically the short-time Fourier transform coefficients, is an obvious consideration. In particular when it comes to performing tasks that heavily rely on phase information, such as acoustic beamforming, complex-valued algorithms are omnipresent. In this contribution we recapitulate backpropagation in CVNNs, develop complex-valued network elements, such as the split-rectified non-linearity, and compare real- and complex-valued networks on a beamforming task. We find that CVNNs hardly provide a performance gain and conclude that the effort of developing the complex-valued counterparts of the building blocks of modern deep or recurrent neural networks can hardly be justified.

Factor Graph Decoding for Speech Presence Probability Estimation - Glarner, Thomas; Mahdi Momenzadeh, Mohammad; Drude, Lukas; Haeb-Umbach, Reinhold (12. ITG Fachtagung Sprachkommunikation (ITG 2016))
This paper is concerned with speech presence probability estimation employing an explicit model of the temporal and spectral correlations of speech. An undirected graphical model is introduced, based on a Factor Graph formulation. It is shown that this undirected model cures some of the theoretical issues of an earlier directed graphical model. Furthermore, we formulate a message passing inference scheme based on an approximate graph factorization, identify this inference scheme as a particular message passing schedule based on the turbo principle and suggest further alternative schedules. The experiments show an improved performance over speech presence probability estimation based on an IID assumption, and a slightly better performance of the turbo schedule over the alternatives.

Neural Network Based Spectral Mask Estimation for Acoustic Beamforming - Heymann, Jahn; Drude, Lukas; Haeb-Umbach, Reinhold (Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP))