@inproceedings{87, abstract = {{Management of complex network services requires flexible and efficient service provisioning as well as optimized handling of continuous changes in the workload of the service.To adapt to changes in the demand, service components need to be replicated (scaling) and allocated to physical resources (placement) dynamically. In this paper, we propose a fullyautomated approach to the joint optimization problem of scaling and placement, enabling quick reaction to changes. We formalize the problem, analyze its complexity, and develop two algorithms to solve it. Extensive empirical results show the applicability andeffectiveness of the proposed approach.}}, author = {{Dräxler, Sevil and Karl, Holger and Mann, Zoltan Adam}}, booktitle = {{Proceedings of the 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2017)}}, title = {{{Joint Optimization of Scaling and Placement of Virtual Network Services}}}, doi = {{10.1109/CCGRID.2017.25}}, year = {{2017}}, } @inproceedings{981, abstract = {{Benchmarking and profiling virtual network functions (VNFs) generates input knowledge for resource management decisions taken by management and orchestration systems. Such VNFs are usually not executed in isolation but are often deployed as part of a service function chain (SFC) that connects single functions into complex structures. To manage such chains, isolated performance profiles of single functions have to be combined to get insights into the overall behavior of an SFC. This becomes particularly challenging in highly agile DevOps environments in which profiling processes need to be fully automated and detailed insights about a chain's internal structures are not always available. In this paper, we introduce a fully automatable, flexible, and platform-agnostic profiling system that allows to profile entire SFCs at once. This obviates manual modeling procedures to combine profiling results from single VNFs to reflect SFC performance. We use a case study with different SFC configurations to show that it is hard to model the resulting SFC performance based on single-VNF measurements and that performance interactions between real, non-trivial functions that are deployed in a chain exist. }}, author = {{Peuster, Manuel and Karl, Holger}}, booktitle = {{IEEE Conference on Network Function Virtualisation and Software Defined Networks (NFV-SDN)}}, location = {{Berlin}}, title = {{{Profile Your Chains, Not Functions. Automated Network Service Profiling in DevOps Environments}}}, doi = {{10.1109/NFV-SDN.2017.8169826}}, year = {{2017}}, } @article{9862, abstract = {{In order to improve the credibility of modern simulation tools, uncertainties of different kinds have to be considered. This work is focused on epistemic uncertainties in the framework of continuum mechanics, which are taken into account by fuzzy analysis. The underlying min-max optimization problem of the extension principle is approximated by α-discretization, resulting in a separation of minimum and maximum problems. To become more universal, so-called quantities of interest are employed, which allow a general formulation for the target problem of interest. In this way, the relation to parameter identification problems based on least-squares functions is highlighted. The solutions of the related optimization problems with simple constraints are obtained with a gradient-based scheme, which is derived from a sensitvity analysis for the target problem by means of a variational formulation. Two numerical examples for the fuzzy analysis of material parameters are concerned with a necking problem at large strain elastoplasticity and a perforated strip at large strain hyperelasticity to demonstrate the versatility of the proposed variational formulation. }}, author = {{Mahnken, Rolf}}, issn = {{ 2325-3444}}, journal = {{Mathematics and Mechanics of complex systems}}, keywords = {{fuzzy analysis, α-level optimization, quantities of interest, optimization with simple constraints, large strain elasticity, large strain elastoplasticity}}, number = {{3-4}}, title = {{{"A variational formulation for fuzzy analysis in continuum mechanics"}}}, volume = {{5}}, year = {{2017}}, } @inproceedings{5594, abstract = {{Design science is a fundamental research stream that contends its position in the information systems discipline. While ongoing debates address the relative importance of design science contributions in the information systems community, insights into the scientific impact of design science research (DSR) are missing and this lack of understanding arguably poses challenges to an informed discourse. To identify the most influential papers and those factors that explain their scientific impact, this paper presents an exploratory study of the scientific impact of DSR papers published in the AIS Senior Scholars' Basket of Journals. We uncover the current DSR landscape by taking stock of influential papers and theories and develop a model to explain the scientific impact of DSR papers. Our findings show that scientific impact is significantly explained by theorization and novelty. We discuss how the implications of our work can be projected on the overarching discourse on DSR.}}, author = {{Wagner, Gerit and Prester, Julian and Schryen, Guido}}, booktitle = {{38th International Conference on Information Systems}}, location = {{Seoul, South Korea}}, title = {{{Exploring the Scientific Impact of Information Systems Design Science Research: A Scientometric Study}}}, year = {{2017}}, } @article{5633, abstract = {{Literature reviews (LRs) are recognized for their increasing impact in the information systems literature. Methodologists have drawn attention to the question of how we can leverage the value of LRs to preserve and generate knowledge. The panelists who participated in the discussion of ?Standalone Literature Reviews in IS Research: What Can Be Learnt from the Past and Other Fields?? at ICIS 2016 in Dublin acknowledged this significant issue and debated a) what the IS field can learn from other fields and where IS-specific challenges occur, b) how the IS field should move forward to foster the genre of LRs, and c) what best practices are to train doctoral IS students in publishing LRs. This article reports the key takeaways of this panel discussion. Guidance for IS scholars is provided on how to conduct LRs that contribute to the cumulative knowledge development within and across the IS field to best prepare the next generation of IS scholars.}}, author = {{Schryen, Guido and Benlian, Alexander and Rowe, Frantz and Shirley, Gregor and Larsen, Kai and Petter, Stacie and Par{\'e}, Guy and Wagner, Gerit and Haag, Steffi and Yasasin, Emrah}}, issn = {{1529-3181}}, journal = {{Communications of the AIS}}, keywords = {{Literature Review, Review Methodology, Research Methodology, Doctoral Training}}, pages = {{557 -- 569}}, publisher = {{Association for Information Systems (AIS)}}, title = {{{Literature Reviews in IS Research: What Can Be Learnt from the Past and Other Fields?}}}, volume = {{40}}, year = {{2017}}, } @article{5671, abstract = {{Multi-attribute value theory (MAVT)-based recommender systems have been proposed for dealing with issues of existing recommender systems, such as the cold-start problem and changing preferences. However, as we argue in this paper, existing MAVT-based methods for measuring attribute importance weights do not fit the shopping tasks for which recommender systems are typically used. These methods assume well-trained decision makers who are willing to invest time and cognitive effort, and who are familiar with the attributes describing the available alternatives and the ranges of these attribute levels. Yet, recommender systems are most often used by consumers who are usually not familiar with the available attributes and ranges and who wish to save time and effort. Against this background, we develop a new method, based on a product configuration process, which is tailored to the characteristics of these particular decision makers. We empirically compare our method to SWING, ranking-based conjoint analysis and TRADEOFF in a between-subjects laboratory experiment with 153 participants. Results indicate that our proposed method performs better than TRADEOFF and CONJOINT and at least as well as SWING in terms of recommendation accuracy, better than SWING and TRADEOFF and at least as well as CONJOINT in terms of cognitive load, and that participants were faster with our method than with any other method. We conclude that our method is a promising option to help support consumers' decision processes in e-commerce shopping tasks.}}, author = {{Scholz, Michael and Dorner, Verena and Schryen, Guido and Benlian, Alexander}}, journal = {{European Journal of Operational Research}}, keywords = {{E-Commerce, Recommender System, Attribute Weights, Configuration System, Decision Support}}, number = {{1}}, pages = {{205 -- 215}}, publisher = {{Elsevier}}, title = {{{A configuration-based recommender system for supporting e-commerce decisions}}}, volume = {{259}}, year = {{2017}}, } @article{680, author = {{Peter, Manuel and Hildebrandt, Andre and Schlickriede, Christian and Gharib, Kimia and Zentgraf, Thomas and Förstner, Jens and Linden, Stefan}}, issn = {{1530-6984}}, journal = {{Nano Letters}}, keywords = {{tet_topic_opticalantenna}}, number = {{7}}, pages = {{4178--4183}}, publisher = {{American Chemical Society (ACS)}}, title = {{{Directional Emission from Dielectric Leaky-Wave Nanoantennas}}}, doi = {{10.1021/acs.nanolett.7b00966}}, volume = {{17}}, year = {{2017}}, } @phdthesis{10594, abstract = {{Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. Since – in contrast to the solution of a single objective optimization problem – the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging. This is even more the case when many problems have to be solved, when the number of objectives is high, or when the objectives are costly to evaluate. Consequently, this thesis is devoted to the identification and exploitation of structure both in the Pareto set and the dynamics of the underlying model as well as to the development of efficient algorithms for solving problems with additional parameters, with a high number of objectives or with PDE-constraints. These three challenges are addressed in three respective parts. In the first part, predictor-corrector methods are extended to entire Pareto sets. When certain smoothness assumptions are satisfied, then the set of parameter dependent Pareto sets possesses additional structure, i.e. it is a manifold. The tangent space can be approximated numerically which yields a direction for the predictor step. In the corrector step, the predicted set converges to the Pareto set at a new parameter value. The resulting algorithm is applied to an example from autonomous driving. In the second part, the hierarchical structure of Pareto sets is investigated. When considering a subset of the objectives, the resulting solution is a subset of the Pareto set of the original problem. Under additional smoothness assumptions, the respective subsets are located on the boundary of the Pareto set of the full problem. This way, the “skeleton” of a Pareto set can be computed and due to the exponential increase in computing time with the number of objectives, the computations of these subsets are significantly faster which is demonstrated using an example from industrial laundries. In the third part, PDE-constrained multiobjective optimal control problems are addressed by reduced order modeling methods. Reduced order models exploit the structure in the system dynamics, for example by describing the dynamics of only the most energetic modes. The model reduction introduces an error in both the function values and their gradients, which has to be taken into account in the development of algorithms. Both scalarization and set-oriented approaches are coupled with reduced order modeling. Convergence results are presented and the numerical benefit is investigated. The algorithms are applied to semi-linear heat flow problems as well as to the Navier-Stokes equations. }}, author = {{Peitz, Sebastian}}, title = {{{ Exploiting structure in multiobjective optimization and optimal control}}}, doi = {{10.17619/UNIPB/1-176}}, year = {{2017}}, } @techreport{1083, abstract = {{In actual school choice applications the theoretical underpinnings of the Boston School Choice Mechanism (BM) (complete information and rationality of the agents) are often not given. We analyze the actual behavior of agents in such a matching mechanism, using data from the matching mechanism currently used in a clearinghouse at a faculty of Business Administration and Economics at a German university, where a variant of the BM is used, and supplement this data with data generated in a survey among students who participated in the clearinghouse. We find that under the current mechanism over 70% of students act strategically. Controlling for students' limited information, we find that they do act rationally in their decision to act strategically. While students thus seem to react to the incentives to act strategically under the BM, they do not seem to be able to use this to their own advantage. However, those students acting in line with their beliefs manage a significantly better personal outcome than those who do not. We also run simulations by using a variant of the deferred acceptance algorithm, adapted to our situation, to show that the use of a different algorithm may be to the students' advantage.}}, author = {{Hoyer, Britta and Stroh-Maraun, Nadja}}, publisher = {{CIE Working Paper Series, Paderborn University}}, title = {{{Matching Strategies of Heterogeneous Agents under Incomplete Information in a University Clearinghouse}}}, volume = {{110}}, year = {{2017}}, } @misc{109, author = {{Pauck, Felix}}, publisher = {{Universität Paderborn}}, title = {{{Cooperative static analysis of Android applications}}}, year = {{2017}}, } @inproceedings{1095, abstract = {{Many university students struggle with motivational problems, and gamification has the potential to address these problems. However, using gamification currently is rather tedious and time-consuming for instructors because current approaches to gamification require instructors to engage in the time-consuming preparation of course contents (e.g., for quizzes or mini-games). In reply to this issue, we propose a “lean” approach to gamification, which relies on gamifying learning activities rather than learning contents. The learning activities that are gamified in the lean approach can typically be drawn from existing course syllabi (e.g., attend certain lectures, hand in assignments, read book chapters and articles). Hence, compared to existing approaches, lean gamification substantially lowers the time requirements posed on instructors for gamifying a given course. Drawing on research on limited attention and the present bias, we provide the theoretical foundation for the lean gamification approach. In addition, we present a mobile application that implements lean gamification and outline a mixed-methods study that is currently under way for evaluating whether lean gamification does indeed have the potential to increase students’ motivation. We thereby hope to allow more students and instructors to benefit from the advantages of gamification. }}, author = {{John, Thomas and Feldotto, Matthias and Hemsen, Paul and Klingsieck, Katrin and Kundisch, Dennis and Langendorf, Mike}}, booktitle = {{Proceedings of the 25th European Conference on Information Systems (ECIS)}}, pages = {{2970--2979}}, title = {{{Towards a Lean Approach for Gamifying Education}}}, year = {{2017}}, } @inproceedings{11717, abstract = {{In this work, we address the limited availability of large annotated databases for real-life audio event detection by utilizing the concept of transfer learning. This technique aims to transfer knowledge from a source domain to a target domain, even if source and target have different feature distributions and label sets. We hypothesize that all acoustic events share the same inventory of basic acoustic building blocks and differ only in the temporal order of these acoustic units. We then construct a deep neural network with convolutional layers for extracting the acoustic units and a recurrent layer for capturing the temporal order. Under the above hypothesis, transfer learning from a source to a target domain with a different acoustic event inventory is realized by transferring the convolutional layers from the source to the target domain. The recurrent layer is, however, learnt directly from the target domain. Experiments on the transfer from a synthetic source database to the reallife target database of DCASE 2016 demonstrate that transfer learning leads to improved detection performance on average. However, the successful transfer to detect events which are very different from what was seen in the source domain, could not be verified.}}, author = {{Arora, Prerna and Haeb-Umbach, Reinhold}}, booktitle = {{IEEE 19th International Workshop on Multimedia Signal Processing (MMSP)}}, title = {{{A Study on Transfer Learning for Acoustic Event Detection in a Real Life Scenario}}}, year = {{2017}}, } @techreport{11735, abstract = {{This report describes the computation of gradients by algorithmic differentiation for statistically optimum beamforming operations. Especially the derivation of complex-valued functions is a key component of this approach. Therefore the real-valued algorithmic differentiation is extended via the complex-valued chain rule. In addition to the basic mathematic operations the derivative of the eigenvalue problem with complex-valued eigenvectors is one of the key results of this report. The potential of this approach is shown with experimental results on the CHiME-3 challenge database. There, the beamforming task is used as a front-end for an ASR system. With the developed derivatives a joint optimization of a speech enhancement and speech recognition system w.r.t. the recognition optimization criterion is possible.}}, author = {{Boeddeker, Christoph and Hanebrink, Patrick and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}}, title = {{{On the Computation of Complex-valued Gradients with Application to Statistically Optimum Beamforming}}}, year = {{2017}}, } @inproceedings{11736, abstract = {{In this paper we show how a neural network for spectral mask estimation for an acoustic beamformer can be optimized by algorithmic differentiation. Using the beamformer output SNR as the objective function to maximize, the gradient is propagated through the beamformer all the way to the neural network which provides the clean speech and noise masks from which the beamformer coefficients are estimated by eigenvalue decomposition. A key theoretical result is the derivative of an eigenvalue problem involving complex-valued eigenvectors. Experimental results on the CHiME-3 challenge database demonstrate the effectiveness of the approach. The tools developed in this paper are a key component for an end-to-end optimization of speech enhancement and speech recognition.}}, author = {{Boeddeker, Christoph and Hanebrink, Patrick and Drude, Lukas and Heymann, Jahn and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}}, title = {{{Optimizing Neural-Network Supported Acoustic Beamforming by Algorithmic Differentiation}}}, year = {{2017}}, } @inproceedings{11737, abstract = {{The benefits of both a logarithmic spectral amplitude (LSA) estimation and a modeling in a generalized spectral domain (where short-time amplitudes are raised to a generalized power exponent, not restricted to magnitude or power spectrum) are combined in this contribution to achieve a better tradeoff between speech quality and noise suppression in single-channel speech enhancement. A novel gain function is derived to enhance the logarithmic generalized spectral amplitudes of noisy speech. Experiments on the CHiME-3 dataset show that it outperforms the famous minimum mean squared error (MMSE) LSA gain function of Ephraim and Malah in terms of noise suppression by 1.4 dB, while the good speech quality of the MMSE-LSA estimator is maintained.}}, author = {{Chinaev, Alleksej and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}}, title = {{{A Generalized Log-Spectral Amplitude Estimator for Single-Channel Speech Enhancement}}}, year = {{2017}}, } @inproceedings{11754, abstract = {{Recent advances in discriminatively trained mask estimation networks to extract a single source utilizing beamforming techniques demonstrate, that the integration of statistical models and deep neural networks (DNNs) are a promising approach for robust automatic speech recognition (ASR) applications. In this contribution we demonstrate how discriminatively trained embeddings on spectral features can be tightly integrated into statistical model-based source separation to separate and transcribe overlapping speech. Good generalization to unseen spatial configurations is achieved by estimating a statistical model at test time, while still leveraging discriminative training of deep clustering embeddings on a separate training set. We formulate an expectation maximization (EM) algorithm which jointly estimates a model for deep clustering embeddings and complex-valued spatial observations in the short time Fourier transform (STFT) domain at test time. Extensive simulations confirm, that the integrated model outperforms (a) a deep clustering model with a subsequent beamforming step and (b) an EM-based model with a beamforming step alone in terms of signal to distortion ratio (SDR) and perceptually motivated metric (PESQ) gains. ASR results on a reverberated dataset further show, that the aforementioned gains translate to reduced word error rates (WERs) even in reverberant environments.}}, author = {{Drude, Lukas and Haeb-Umbach, Reinhold}}, booktitle = {{INTERSPEECH 2017, Stockholm, Schweden}}, title = {{{Tight integration of spatial and spectral features for BSS with Deep Clustering embeddings}}}, year = {{2017}}, } @inproceedings{11770, abstract = {{In this contribution we show how to exploit text data to support word discovery from audio input in an underresourced target language. Given audio, of which a certain amount is transcribed at the word level, and additional unrelated text data, the approach is able to learn a probabilistic mapping from acoustic units to characters and utilize it to segment the audio data into words without the need of a pronunciation dictionary. This is achieved by three components: an unsupervised acoustic unit discovery system, a supervisedly trained acoustic unit-to-grapheme converter, and a word discovery system, which is initialized with a language model trained on the text data. Experiments for multiple setups show that the initialization of the language model with text data improves the word segementation performance by a large margin.}}, author = {{Glarner, Thomas and Boenninghoff, Benedikt and Walter, Oliver and Haeb-Umbach, Reinhold}}, booktitle = {{INTERSPEECH 2017, Stockholm, Schweden}}, title = {{{Leveraging Text Data for Word Segmentation for Underresourced Languages}}}, year = {{2017}}, } @inproceedings{1180, abstract = {{These days, there is a strong rise in the needs for machine learning applications, requiring an automation of machine learning engineering which is referred to as AutoML. In AutoML the selection, composition and parametrization of machine learning algorithms is automated and tailored to a specific problem, resulting in a machine learning pipeline. Current approaches reduce the AutoML problem to optimization of hyperparameters. Based on recursive task networks, in this paper we present one approach from the field of automated planning and one evolutionary optimization approach. Instead of simply parametrizing a given pipeline, this allows for structure optimization of machine learning pipelines, as well. We evaluate the two approaches in an extensive evaluation, finding both approaches to have their strengths in different areas. Moreover, the two approaches outperform the state-of-the-art tool Auto-WEKA in many settings.}}, author = {{Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}}, booktitle = {{27th Workshop Computational Intelligence}}, location = {{Dortmund}}, title = {{{Automatic Machine Learning: Hierachical Planning Versus Evolutionary Optimization}}}, year = {{2017}}, } @inproceedings{11809, abstract = {{This paper presents an end-to-end training approach for a beamformer-supported multi-channel ASR system. A neural network which estimates masks for a statistically optimum beamformer is jointly trained with a network for acoustic modeling. To update its parameters, we propagate the gradients from the acoustic model all the way through feature extraction and the complex valued beamforming operation. Besides avoiding a mismatch between the front-end and the back-end, this approach also eliminates the need for stereo data, i.e., the parallel availability of clean and noisy versions of the signals. Instead, it can be trained with real noisy multichannel data only. Also, relying on the signal statistics for beamforming, the approach makes no assumptions on the configuration of the microphone array. We further observe a performance gain through joint training in terms of word error rate in an evaluation of the system on the CHiME 4 dataset.}}, author = {{Heymann, Jahn and Drude, Lukas and Boeddeker, Christoph and Hanebrink, Patrick and Haeb-Umbach, Reinhold}}, booktitle = {{Proc. IEEE Intl. Conf. on Acoustics, Speech and Signal Processing (ICASSP)}}, title = {{{BEAMNET: End-to-End Training of a Beamformer-Supported Multi-Channel ASR System}}}, year = {{2017}}, } @article{11811, abstract = {{Acoustic beamforming can greatly improve the performance of Automatic Speech Recognition (ASR) and speech enhancement systems when multiple channels are available. We recently proposed a way to support the model-based Generalized Eigenvalue beamforming operation with a powerful neural network for spectral mask estimation. The enhancement system has a number of desirable properties. In particular, neither assumptions need to be made about the nature of the acoustic transfer function (e.g., being anechonic), nor does the array configuration need to be known. While the system has been originally developed to enhance speech in noisy environments, we show in this article that it is also effective in suppressing reverberation, thus leading to a generic trainable multi-channel speech enhancement system for robust speech processing. To support this claim, we consider two distinct datasets: The CHiME 3 challenge, which features challenging real-world noise distortions, and the Reverb challenge, which focuses on distortions caused by reverberation. We evaluate the system both with respect to a speech enhancement and a recognition task. For the first task we propose a new way to cope with the distortions introduced by the Generalized Eigenvalue beamformer by renormalizing the target energy for each frequency bin, and measure its effectiveness in terms of the PESQ score. For the latter we feed the enhanced signal to a strong DNN back-end and achieve state-of-the-art ASR results on both datasets. We further experiment with different network architectures for spectral mask estimation: One small feed-forward network with only one hidden layer, one Convolutional Neural Network and one bi-directional Long Short-Term Memory network, showing that even a small network is capable of delivering significant performance improvements.}}, author = {{Heymann, Jahn and Drude, Lukas and Haeb-Umbach, Reinhold}}, journal = {{Computer Speech and Language}}, title = {{{A Generic Neural Acoustic Beamforming Architecture for Robust Multi-Channel Speech Processing}}}, year = {{2017}}, }