Applications of Evolutionary Computation - 19th European Conference, EvoApplications - Squillero, Giovanni; Burelli, Paolo; M. Mora, Antonio; Agapitos, Alexandros; S. Bush, William; Cagnoni, Stefano; Cotta, Carlos; De Falco, Ivanoe; Della Cioppa, Antonio; Divina, Federico; Eiben, A.E.; I. Esparcia-Alcázar, Anna; et al.

RCo-Design: New Visual Environment for Reconfigurable Embedded Systems - Ghribi, Ines; Ben Abdallah, Riadh; Khalgui, Mohamed; Platzner, Marco (Proceedings of the 30th European Simulation and Modelling Conference (ESM))

New Co-design Methodology for Real-time Embedded Systems - Ghribi, Ines; Ben Abdallah, Riadh; Khalgui, Mohamed; Platzner, Marco (Proceedings of the 11th International Conference on Software Engineering and Applications (ICSOFT-EA))

Efficient Statistical Parameter Selection for Nonlinear Modeling of Process/Performance Variation - Ghasemzadeh Mohammadi, Hassan; Gaillardon, Pierre-Emmanuel; De Micheli, Giovanni (IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems)

Custom Memory Controller for ReconOS - Hermansen, Sven

Preference Learning - Fürnkranz, J.; Hüllermeier, Eyke (Encyclopedia of Machine Learning and Data Mining)

Über die Rolle von Informationen in Verkehrsnetzwerken - Handirk, Tobias

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))

On the Bias of Direction of Arrival Estimation Using Linear Microphone Arrays - Jacob, Florian; Haeb-Umbach, Reinhold (12. ITG Fachtagung Sprachkommunikation (ITG 2016))
This contribution investigates Direction of Arrival (DoA) estimation using linearly arranged microphone arrays. We are going to develop a model for the DoA estimation error in a reverberant scenario and show the existence of a bias, that is a consequence of the linear arrangement and limited field of view (FoV) bias: First, the limited FoV leading to a clipping of the measurements, and, second, the angular distribution of the signal energy of the reflections being non-uniform. Since both issues are a consequence of the linear arrangement of the sensors, the bias arises largely independent of the kind of DoA estimator. The experimental evaluation demonstrates the existence of the bias for a selected number of DoA estimation methods and proves that the prediction from the developed theoretical model matches the simulation results.

Wide Residual BLSTM Network with Discriminative Speaker Adaptation for Robust Speech Recognition - Heymann, Jahn; Drude, Lukas; Haeb-Umbach, Reinhold (Computer Speech and Language)
We present a system for the 4th CHiME challenge which significantly increases the performance for all three tracks with respect to the provided baseline system. The front-end uses a bi-directional Long Short-Term Memory (BLSTM)-based neural network to estimate signal statistics. These then steer a Generalized Eigenvalue beamformer. The back-end consists of a 22 layer deep Wide Residual Network and two extra BLSTM layers. Working on a whole utterance instead of frames allows us to refine Batch-Normalization. We also train our own BLSTM-based language model. Adding a discriminative speaker adaptation leads to further gains. The final system achieves a word error rate on the six channel real test data of 3.48%. For the two channel track we achieve 5.96% and for the one channel track 9.34%. This is the best reported performance on the challenge achieved by a single system, i.e., a configuration, which does not combine multiple systems. At the same time, our system is independent of the microphone configuration. We can thus use the same components for all three tracks.

A summary of the REVERB challenge: state-of-the-art and remaining challenges in reverberant speech processing research - Kinoshita, Keisuke; Delcroix, Marc; Gannot, Sharon; Habets, Emanuel A. P.; Haeb-Umbach, Reinhold; Kellermann, Walter; Leutnant, Volker; Maas, Roland; Nakatani, Tomohiro; Raj, Bhiksha; Sehr, Armin; Yoshioka, Takuya (EURASIP Journal on Advances in Signal Processing)

Acoustic Microphone Geometry Calibration: An overview and experimental evaluation of state-of-the-art algorithms - Plinge, Axel; Jacob, Florian; Haeb-Umbach, Reinhold; Fink, Gernot A. (IEEE Signal Processing Magazine)
Today, we are often surrounded by devices with one or more microphones, such as smartphones, laptops, and wireless microphones. If they are part of an acoustic sensor network, their distribution in the environment can be beneficially exploited for various speech processing tasks. However, applications like speaker localization, speaker tracking, and speech enhancement by beamforming avail themselves of the geometrical configuration of the sensors. Therefore, acoustic microphone geometry calibration has recently become a very active field of research. This article provides an application-oriented, comprehensive survey of existing methods for microphone position self-calibration, which will be categorized by the measurements they use and the scenarios they can calibrate. Selected methods will be evaluated comparatively with real-world recordings.
Keywords: Acoustic sensors; Microphones; Portable computers; Smart phones; Wireless communication; Wireless sensor networks

Induction-based Verification of Timed Systems - Isenberg, Tobias

The RWTH/UPB/FORTH System Combination for the 4th CHiME Challenge Evaluation - Menne, Tobias; Heymann, Jahn; Alexandridis, Anastasios; Irie, Kazuki; Zeyer, Albert; Kitza, Markus; Golik, Pavel; Kulikov, Ilia; Drude, Lukas; Schlüter, Ralf; Ney, Hermann; Haeb-Umbach, Reinhold; et al. (Computer Speech and Language)
This paper describes automatic speech recognition (ASR) systems developed jointly by RWTH, UPB and FORTH for the 1ch, 2ch and 6ch track of the 4th CHiME Challenge. In the 2ch and 6ch tracks the final system output is obtained by a Confusion Network Combination (CNC) of multiple systems. The Acoustic Model (AM) is a deep neural network based on Bidirectional Long Short-Term Memory (BLSTM) units. The systems differ by front ends and training sets used for the acoustic training. The model for the 1ch track is trained without any preprocessing. For each front end we trained and evaluated individual acoustic models. We compare the ASR performance of different beamforming approaches: a conventional superdirective beamformer [1] and an MVDR beamformer as in [2], where the steering vector is estimated based on [3]. Furthermore we evaluated a BLSTM supported Generalized Eigenvalue beamformer using NN-GEV [4]. The back end is implemented using RWTH?s open-source toolkits RASR [5], RETURNN [6] and rwthlm [7]. We rescore lattices with a Long Short-Term Memory (LSTM) based language model. The overall best results are obtained by a system combination that includes the lattices from the system of UPB?s submission [8]. Our final submission scored second in each of the three tracks of the 4th CHiME Challenge.

Unsupervised Word Discovery from Speech using Bayesian Hierarchical Models - Walter, Oliver; Haeb-Umbach, Reinhold (38th German Conference on Pattern Recognition (GCPR 2016))
In this paper we demonstrate an algorithm to learn words from speech using non-parametric Bayesian hierarchical models in an unsupervised setting. We exploit the assumption of a hierarchical structure of speech, namely the formation of spoken words as a sequence of phonemes. We employ the Nested Hierarchical Pitman-Yor Language Model, which allows an a priori unknown and possibly unlimited number of words. We assume the n-gram probabilities of words, the m-gram probabilities of phoneme sequences in words and the phoneme sequences of the words themselves as latent variables to be learned. We evaluate the algorithm on a cross language task using an existing speech recognizer trained on English speech to decode speech in the Xitsonga language supplied for the 2015 ZeroSpeech challenge. We apply the learning algorithm on the resulting phoneme graphs and achieve the highest token precision and F score compared to present systems.

Protocol options for low power sensor network MAC using wake-up receivers with duty cycling - Blobel, Johannes; Sommer, Christoph; Dressler, Falko (2016 IEEE International Conference on Communications (ICC))

On the impact of adjacent channel interference in multi-channel VANETs - Campolo, Claudia; Sommer, Christoph; Dressler, Falko; Molinaro, Antonella (2016 IEEE International Conference on Communications (ICC))

Special Issue on Multi-radio, Multi-technology, Multi-system Vehicular Communications - Campolo, Claudia; Cheng, Lin; Sommer, Christoph; (Michael) Tsai, Hsin-Mu (Computer Communications)

Cars as the base for service discovery and provision in highly dynamic networks - Hagenauer, Florian; Sommer, Christoph; Merschjohann, Simon; Higuchi, Takamasa; Dressler, Falko; Altintas, Onur (2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS))

Interconnecting smart cities by vehicles: How feasible is it? - Hagenauer, Florian; Sommer, Christoph; Onishi, Ryokichi; Wilhelm, Matthias; Dressler, Falko; Altintas, Onur (2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS))

Cars as a main ICT resource of smart cities - Hagenauer, F.; Dressler, F.; Altintas, O.; Sommer, Christoph (Smart Cities and Homes)

IEEE 802.11p unicast considered harmful - Klingler, Florian; Dressler, Falko; Sommer, Christoph (2015 IEEE Vehicular Networking Conference (VNC))

Cluster-based transmit power control in heterogeneous vehicular networks - Memedi, Agon; Hagenauer, Florian; Dressler, Falko; Sommer, Christoph (2015 IEEE Vehicular Networking Conference (VNC))

Self-aware Computing Systems: An Engineering Approach
Taking inspiration from self-awareness in humans, this book introduces the new notion of computational self-awareness as a fundamental concept for designing and operating computing systems. The basic ability of such self-aware computing systems is to collect information about their state and progress, learning and maintaining models containing knowledge that enables them to reason about their behaviour. Self-aware computing systems will have the ability to utilise this knowledge to effectively and autonomously adapt and explain their behaviour, in changing conditions. This book addresses these fundamental concepts from an engineering perspective, aiming at developing primitives for building systems and applications. It will be of value to researchers, professionals and graduate students in computer science and engineering.

X Marks the Spot: Scan-Flip-Flop Clustering for Faster-than-at-Speed Test - Kampmann, Matthias; Hellebrand, Sybille (25th IEEE Asian Test Symposium (ATS'16))

Selfish Network Creation - On Variants of Network Creation Games - Cord-Landwehr, Andreas

On the identifiability of models in multi-criteria preference learning - Labreuche, C.; Hüllermeier, Eyke; Vojtas, P.; Fallah Tehrani, A. (in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany)

Preference -based reinforcement learning using dyad ranking - Schäfer, D.; Hüllermeier, Eyke (in Proceedings DA2PL`2016 Euro Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn, Germany)

Statistical Inference for Incomplete Ranking Data: A Comparison of two likelihood-based estimators - Couso, Ines; Ahmadi Fahandar, Mohsen; Hüllermeier, Eyke (in Proceedings DA2PL 2016 EURO Mini Conference From Multiple Criteria Decision Aid to Preference Learning, Paderborn Germany)

Support vector classification on noisy data using fuzzy superset losses - Lu, S.; Hüllermeier, Eyke (in Proceedings 26th Workshop Computational Intelligence, Dortmund Germany)

Plackett-Luce networks for dyad ranking - Schäfer, D.; Hüllermeier, Eyke (in Workshop LWDA "Lernen, Wissen, Daten, Analysen" Potsdam, Germany)

A data visualization course at the university of paderborn - Domik, Gitta (Pedagogy Data Visualization, IEEE VIS Workshop)

Self-stabilizing Metric Graphs - Gmyr, Robert; Lefèvre, Jonas; Scheideler, Christian (Proceedings of the 18th International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS))
We present a self-stabilizing algorithm for overlay networks that, for an arbitrary metric given by a distance oracle, constructs the graph representing that metric. The graph representing a metric is the unique minimal undirected graph such that for any pair of nodes the length of a shortest path between the nodes corresponds to the distance between the nodes according to the metric. The algorithm works under both an asynchronous and a synchronous daemon. In the synchronous case, the algorithm stablizes in time O(n) and it is almost silent in that after stabilization a node sends and receives a constant number of messages per round.

Scheduling with Interjob Communication on Parallel Processors - König, Jürgen; Mäcker, Alexander; Meyer auf der Heide, Friedhelm; Riechers, Sören (Proceedings of the 10th Annual International Conference on Combinatorial Optimization and Applications (COCOA))
Consider a scheduling problem in which a set of jobs with interjob communication, canonically represented by a weighted tree, needs to be scheduled on m parallel processors interconnected by a shared communication channel. In each time step, we may allow any processed job to use a certain capacity of the channel in order to satisfy (parts of) its communication demands to adjacent jobs processed in parallel. The goal is to find a schedule that minimizes the makespan and in which communication demands of all jobs are satisfied.We show that this problem is NP-hard in the strong sense even if the number of processors and the maximum degree of the underlying tree is constant.Consequently, we design and analyze simple approximation algorithms with asymptotic approximation ratio 2-2/m in case of paths and a ratio of 5/2 in case of arbitrary trees.

FPGA-based acceleration of high density myoelectric signal processing - Boschmann, Alexander; Agne, Andreas; Witschen, Linus Matthias; Thombansen, Georg; Kraus, Florian; Platzner, Marco (2015 International Conference on ReConFigurable Computing and FPGAs (ReConFig))
Keywords: Electromyography; Feature extraction; Delays; Hardware Pattern recognition; Prosthetics; High definition video

Routing Games With Progressive Filling - Harks, Tobias; Höfer, Martin; Schewior, Kevin; Skopalik, Alexander (IEEE/ACM Transactions on Networking)
Abstract—Max-min fairness (MMF) is a widely known approachto a fair allocation of bandwidth to each of the usersin a network. This allocation can be computed by uniformlyraising the bandwidths of all users without violating capacityconstraints. We consider an extension of these allocations byraising the bandwidth with arbitrary and not necessarily uniformtime-depending velocities (allocation rates). These allocationsare used in a game-theoretic context for routing choices, whichwe formalize in progressive filling games (PFGs). We present avariety of results for equilibria in PFGs. We show that these gamespossess pure Nash and strong equilibria. While computation ingeneral is NP-hard, there are polynomial-time algorithms forprominent classes of Max-Min-Fair Games (MMFG), includingthe case when all users have the same source-destination pair.We characterize prices of anarchy and stability for pure Nashand strong equilibria in PFGs and MMFGs when players havedifferent or the same source-destination pairs. In addition, weshow that when a designer can adjust allocation rates, it is possibleto design games with optimal strong equilibria. Some initial resultson polynomial-time algorithms in this direction are also derived.

Symmetric Anonymous Credentials with Protocols for Relations on Attributes - Hamm, Julian

UTCNN: a Deep Learning Model of Stance Classification on Social Media Text - Chen, Wei-Fan; Ku, Lun-Wei (Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics)

WordForce: Visualizing Controversial Words in Debates - Chen, Wei-Fan; Lin, Fang-Yu; Ku, Lun-Wei (Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations)

Chinese Textual Sentiment Analysis: Datasets, Resources and Tools - Ku, Lun-Wei; Chen, Wei-Fan (Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Tutorial Abstracts)

Strategic Online Facility Location - Drees, Maximilian; Feldkord, Björn; Skopalik, Alexander (Proceedings of the 10th Annual International Conference on Combinatorial Optimization and Applications (COCOA))
In this paper we consider a strategic variant of the online facility location problem. Given is a graph in which each node serves two roles: it is a strategic client stating requests as well as a potential location for a facility. In each time step one client states a request which induces private costs equal to the distance to the closest facility. Before serving, the clients may collectively decide to open new facilities, sharing the corresponding price. Instead of optimizing the global costs, each client acts selfishly. The prices of new facilities vary between nodes and also change over time, but are always bounded by some fixed value α. Both the requests as well as the facility prices are given by an online sequence and are not known in advance.We characterize the optimal strategies of the clients and analyze their overall performance in comparison to a centralized offline solution. If all players optimize their own competitiveness, the global performance of the system is O(√α⋅α) times worse than the offline optimum. A restriction to a natural subclass of strategies improves this result to O(α). We also show that for fixed facility costs, we can find strategies such that this bound further improves to O(√α).

Solving Heterogeneity for a Successful Service Market - Arifulina, Svetlana

Corpus Analyses of Multimodal Narrative: The Example of Graphic Novels - Dunst, Alexander; Hartel, Rita; Hohenstein, Sven; Laubrock, Jochen (Digital Humanities 2016, DH 2016, Conference Abstracts)

Incremental updates on compressed XML - Böttcher, Stefan; Hartel, Rita; Jacobs, Thomas; Maneth, Sebastian (2016 IEEE 32nd International Conference on Data Engineering (ICDE))