TY - CONF AB - In this contribution we derive the Maximum A-Posteriori (MAP) estimates of the parameters of a Gaussian Mixture Model (GMM) in the presence of noisy observations. We assume the distortion to be white Gaussian noise of known mean and variance. An approximate conjugate prior of the GMM parameters is derived allowing for a computationally efficient implementation in a sequential estimation framework. Simulations on artificially generated data demonstrate the superiority of the proposed method compared to the Maximum Likelihood technique and to the ordinary MAP approach, whose estimates are corrected by the known statistics of the distortion in a straightforward manner. AU - Chinaev, Aleksej AU - Haeb-Umbach, Reinhold ID - 11740 KW - Gaussian noise KW - maximum likelihood estimation KW - parameter estimation KW - GMM parameter KW - Gaussian mixture model KW - MAP estimation KW - Map-based estimation KW - maximum a-posteriori estimation KW - maximum likelihood technique KW - noisy observation KW - sequential estimation framework KW - white Gaussian noise KW - Additive noise KW - Gaussian mixture model KW - Maximum likelihood estimation KW - Noise measurement KW - Gaussian mixture model KW - Maximum a posteriori estimation KW - Maximum likelihood estimation SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) TI - MAP-based Estimation of the Parameters of a Gaussian Mixture Model in the Presence of Noisy Observations ER - TY - CONF AB - In this paper we present an improved version of the recently proposed Maximum A-Posteriori (MAP) based noise power spectral density estimator. An empirical bias compensation and bandwidth adjustment reduce bias and variance of the noise variance estimates. The main advantage of the MAP-based postprocessor is its low estimation variance. The estimator is employed in the second stage of a two-stage single-channel speech enhancement system, where eight different state-of-the-art noise tracking algorithms were tested in the first stage. While the postprocessor hardly affects the results in stationary noise scenarios, it becomes the more effective the more nonstationary the noise is. The proposed postprocessor was able to improve all systems in babble noise w.r.t. the perceptual evaluation of speech quality performance. AU - Chinaev, Aleksej AU - Haeb-Umbach, Reinhold AU - Taghia, Jalal AU - Martin, Rainer ID - 11742 SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) TI - Improved Single-Channel Nonstationary Noise Tracking by an Optimized MAP-based Postprocessor ER - TY - CONF AB - Among the different configurations of multi-microphone systems, e.g., in applications of speech dereverberation or denoising, we consider the case without a priori information of the microphone-array geometry. This naturally invokes explicit or implicit identification of source-receiver transfer functions as an indirect description of the microphone-array configuration. However, this blind channel identification (BCI) has been difficult due to the lack of unique identifiability in the presence of observation noise or near-common channel zeros. In this paper, we study the implicit BCI performance of blind signal enhancement techniques such as the adaptive principal component analysis (PCA) or the iterative blind equalization and channel identification (BENCH). To this end, we make use of a recently proposed metric, the normalized filter-projection misalignment (NFPM), which is tailored for BCI evaluation in ill-conditioned (e.g., noisy) scenarios. The resulting understanding of implicit BCI performance can help to judge the behavior of multi-microphone speech enhancement systems and the suitability of implicit BCI to serve channel-based (i.e., channel-informed) enhancement. AU - Enzner, Gerald AU - Schmid, Dominic AU - Haeb-Umbach, Reinhold ID - 11762 T2 - 21th European Signal Processing Conference (EUSIPCO 2013) TI - On the Acoustic Channel Identification in Multi-Microphone Systems via Adaptive Blind Signal Enhancement Techniques ER - TY - CONF AU - Heymann, Jahn AU - Walter, Oliver AU - Haeb-Umbach, Reinhold AU - Raj, Bhiksha ID - 11815 T2 - Automatic Speech Recognition and Understanding Workshop (ASRU 2013) TI - Unsupervised Word Segmentation from Noisy Input ER - TY - CONF AB - In this paper, we consider the Maximum Likelihood (ML) estimation of the parameters of a GAUSSIAN in the presence of censored, i.e., clipped data. We show that the resulting Expectation Maximization (EM) algorithm delivers virtually biasfree and efficient estimates, and we discuss its convergence properties. We also discuss optimal classification in the presence of censored data. Censored data are frequently encountered in wireless LAN positioning systems based on the fingerprinting method employing signal strength measurements, due to the limited sensitivity of the portable devices. Experiments both on simulated and real-world data demonstrate the effectiveness of the proposed algorithms. AU - Hoang, Manh Kha AU - Haeb-Umbach, Reinhold ID - 11816 KW - Gaussian processes KW - Global Positioning System KW - convergence KW - expectation-maximisation algorithm KW - fingerprint identification KW - indoor radio KW - signal classification KW - wireless LAN KW - EM algorithm KW - ML estimation KW - WiFi indoor positioning KW - censored Gaussian data classification KW - clipped data KW - convergence properties KW - expectation maximization algorithm KW - fingerprinting method KW - maximum likelihood estimation KW - optimal classification KW - parameters estimation KW - portable devices sensitivity KW - signal strength measurements KW - wireless LAN positioning systems KW - Convergence KW - IEEE 802.11 Standards KW - Maximum likelihood estimation KW - Parameter estimation KW - Position measurement KW - Training KW - Indoor positioning KW - censored data KW - expectation maximization KW - signal strength KW - wireless LAN SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2013) TI - Parameter estimation and classification of censored Gaussian data with application to WiFi indoor positioning ER - TY - CONF AB - Recently, substantial progress has been made in the field of reverberant speech signal processing, including both single- and multichannel de-reverberation techniques, and automatic speech recognition (ASR) techniques robust to reverberation. To evaluate state-of-the-art algorithms and obtain new insights regarding potential future research directions, we propose a common evaluation framework including datasets, tasks, and evaluation metrics for both speech enhancement and ASR techniques. The proposed framework will be used as a common basis for the REVERB (REverberant Voice Enhancement and Recognition Benchmark) challenge. This paper describes the rationale behind the challenge, and provides a detailed description of the evaluation framework and benchmark results. AU - Kinoshita, Keisuke AU - Delcroix, Marc AU - Yoshioka, Takuya AU - Nakatani, Tomohiro AU - Habets, Emanuel AU - Haeb-Umbach, Reinhold AU - Leutnant, Volker AU - Sehr, Armin AU - Kellermann, Walter AU - Maas, Roland AU - Gannot, Sharon AU - Raj, Bhiksha ID - 11841 KW - Reverberant speech KW - dereverberation KW - ASR KW - evaluation KW - challenge T2 - IEEE Workshop on Applications of Signal Processing to Audio and Acoustics TI - The reverb challenge: a common evaluation framework for dereverberation and recognition of reverberant speech ER - TY - JOUR AB - In this contribution we extend a previously proposed Bayesian approach for the enhancement of reverberant logarithmic mel power spectral coefficients for robust automatic speech recognition to the additional compensation of background noise. A recently proposed observation model is employed whose time-variant observation error statistics are obtained as a side product of the inference of the a posteriori probability density function of the clean speech feature vectors. Further a reduction of the computational effort and the memory requirements are achieved by using a recursive formulation of the observation model. The performance of the proposed algorithms is first experimentally studied on a connected digits recognition task with artificially created noisy reverberant data. It is shown that the use of the time-variant observation error model leads to a significant error rate reduction at low signal-to-noise ratios compared to a time-invariant model. Further experiments were conducted on a 5000 word task recorded in a reverberant and noisy environment. A significant word error rate reduction was obtained demonstrating the effectiveness of the approach on real-world data. AU - Leutnant, Volker AU - Krueger, Alexander AU - Haeb-Umbach, Reinhold ID - 11862 IS - 8 JF - IEEE Transactions on Audio, Speech, and Language Processing KW - Bayes methods KW - compensation KW - error statistics KW - reverberation KW - speech recognition KW - Bayesian feature enhancement KW - background noise KW - clean speech feature vectors KW - compensation KW - connected digits recognition task KW - error statistics KW - memory requirements KW - noisy reverberant data KW - posteriori probability density function KW - recursive formulation KW - reverberant logarithmic mel power spectral coefficients KW - robust automatic speech recognition KW - signal-to-noise ratios KW - time-variant observation KW - word error rate reduction KW - Robust automatic speech recognition KW - model-based Bayesian feature enhancement KW - observation model for reverberant and noisy speech KW - recursive observation model TI - Bayesian Feature Enhancement for Reverberation and Noise Robust Speech Recognition VL - 21 ER - TY - CONF AB - We present a novel method to exploit correlations of adjacent time-frequency (TF)-slots for a sparseness-based blind speech separation (BSS) system. Usually, these correlations are exploited by some heuristic smoothing techniques in the post-processing of the estimated soft TF masks. We propose a different approach: Based on our previous work with one-dimensional (1D)-hidden Markov models (HMMs) along the time axis we extend the modeling to two-dimensional (2D)-HMMs to exploit both temporal and spectral correlations in the speech signal. Based on the principles of turbo decoding we solved the complex inference of 2D-HMMs by a modified forward-backward algorithm which operates alternatingly along the time and the frequency axis. Extrinsic information is exchanged between these steps such that increasingly better soft time-frequency masks are obtained, leading to improved speech separation performance in highly reverberant recording conditions. AU - Tran Vu, Dang Hai AU - Haeb-Umbach, Reinhold ID - 11909 T2 - 21th European Signal Processing Conference (EUSIPCO 2013) TI - Blind Speech Separation Exploiting Temporal and Spectral Correlations Using Turbo Decoding of 2D-HMMs ER - TY - CONF AB - In this paper we present a speech presence probability (SPP) estimation algorithmwhich exploits both temporal and spectral correlations of speech. To this end, the SPP estimation is formulated as the posterior probability estimation of the states of a two-dimensional (2D) Hidden Markov Model (HMM). We derive an iterative algorithm to decode the 2D-HMM which is based on the turbo principle. The experimental results show that indeed the SPP estimates improve from iteration to iteration, and further clearly outperform another state-of-the-art SPP estimation algorithm. AU - Vu, Dang Hai Tran AU - Haeb-Umbach, Reinhold ID - 11917 KW - correlation methods KW - estimation theory KW - hidden Markov models KW - iterative methods KW - probability KW - spectral analysis KW - speech processing KW - 2D HMM KW - SPP estimates KW - iterative algorithm KW - posterior probability estimation KW - spectral correlation KW - speech presence probability estimation KW - state-of-the-art SPP estimation algorithm KW - temporal correlation KW - turbo principle KW - two-dimensional hidden Markov model KW - Correlation KW - Decoding KW - Estimation KW - Iterative decoding KW - Noise KW - Speech KW - Vectors SN - 1520-6149 T2 - 38th International Conference on Acoustics, Speech and Signal Processing (ICASSP 2013) TI - Using the turbo principle for exploiting temporal and spectral correlations in speech presence probability estimation ER - TY - CONF AB - In this paper we consider the unsupervised word discovery from phonetic input. We employ a word segmentation algorithm which simultaneously develops a lexicon, i.e., the transcription of a word in terms of a phone sequence, learns a n-gram language model describing word and word sequence probabilities, and carries out the segmentation itself. The underlying statistical model is that of a Pitman-Yor process, a concept known from Bayesian non-parametrics, which allows for an a priori unknown and unlimited number of different words. Using a hierarchy of Pitman-Yor processes, language models of different order can be employed and nesting it with another hierarchy of Pitman-Yor processes on the phone level allows for backing off unknown word unigrams by phone m-grams. We present results on a large-vocabulary task, assuming an error-free phone sequence is given. We finish by discussing options how to cope with noisy phone sequences. AU - Walter, Oliver AU - Haeb-Umbach, Reinhold AU - Chaudhuri, Sourish AU - Raj, Bhiksha ID - 11921 T2 - IEEE International Conference on Robotics and Automation (ICRA 2013) TI - Unsupervised Word Discovery from Phonetic Input Using Nested Pitman-Yor Language Modeling ER - TY - CONF AU - Walter, Oliver AU - Korthals, Timo AU - Haeb-Umbach, Reinhold AU - Raj, Bhiksha ID - 11924 T2 - Automatic Speech Recognition and Understanding Workshop (ASRU 2013) TI - Hierarchical System for Word Discovery Exploiting DTW-Based Initialization ER - TY - GEN AB - In this paper we present a novel initialization method for unsupervised learning of acoustic patterns in recordings of continuous speech. The pattern discovery task is solved by dynamic time warping whose performance we improve by a smart starting point selection. This enables a more accurate discovery of patterns compared to conventional approaches. After graph-based clustering the patterns are employed for training hidden Markov models for an unsupervised speech acquisition. By iterating between model training and decoding in an EM-like framework the word accuracy is continuously improved. On the TIDIGITS corpus we achieve a word error rate of about 13 percent by the proposed unsupervised pattern discovery approach, which neither assumes knowledge of the acoustic units nor of the labels of the training data. AU - Walter, Oliver AU - Schmalenstroeer, Joerg AU - Haeb-Umbach, Reinhold ID - 11926 TI - A Novel Initialization Method for Unsupervised Learning of Acoustic Patterns in Speech (FGNT-2013-01) ER - TY - CONF AU - Bloessl, Bastian AU - Segata, Michele AU - Sommer, Christoph AU - Dressler, Falko ID - 11976 SN - 9781450319997 T2 - Proceedings of the 19th annual international conference on Mobile computing & networking - MobiCom '13 TI - Decoding IEEE 802.11a/g/p OFDM in software using GNU radio ER - TY - CONF AU - Bloessl, Bastian AU - Segata, Michele AU - Sommer, Christoph AU - Dressler, Falko ID - 11977 SN - 9781450321815 T2 - Proceedings of the second workshop on Software radio implementation forum - SRIF '13 TI - An IEEE 802.11a/g/p OFDM receiver for GNU radio ER - TY - CONF AU - Joerer, Stefan AU - Segata, Michele AU - Bloessl, Bastian AU - Cigno, Renato Lo AU - Sommer, Christoph AU - Dressler, Falko ID - 12022 SN - 9781467349963 T2 - 2012 IEEE Vehicular Networking Conference (VNC) TI - To crash or not to crash: Estimating its likelihood and potentials of beacon-based IVC systems ER - TY - JOUR AU - Joerer, Stefan AU - Segata, Michele AU - Bloessl, Bastian AU - Lo Cigno, Renato AU - Sommer, Christoph AU - Dressler, Falko ID - 12025 JF - IEEE Transactions on Vehicular Technology SN - 0018-9545 TI - A Vehicular Networking Perspective on Estimating Vehicle Collision Probability at Intersections ER - TY - CONF AU - Klingler, Florian AU - Dressler, Falko AU - Cao, Jiannong AU - Sommer, Christoph ID - 12028 SN - 9781479907496 T2 - 2013 10th Annual Conference on Wireless On-demand Network Systems and Services (WONS) TI - Use both lanes: Multi-channel beaconing for message dissemination in vehicular networks ER - TY - CONF AU - Schwartz, Ramon S. AU - Ohazulike, Anthony E. AU - Sommer, Christoph AU - Scholten, Hans AU - Dressler, Falko AU - Havinga, Paul ID - 12044 SN - 9781467349963 T2 - 2012 IEEE Vehicular Networking Conference (VNC) TI - Fair and adaptive data dissemination for Traffic Information Systems ER - TY - JOUR AU - Schwartz, Ramon S. AU - Ohazulike, Anthony E. AU - Sommer, Christoph AU - Scholten, Hans AU - Dressler, Falko AU - Havinga, Paul ID - 12045 JF - Ad Hoc Networks SN - 1570-8705 TI - On the applicability of fair and adaptive data dissemination in traffic information systems ER - TY - CONF AU - Sommer, Christoph AU - Joerer, Stefan AU - Dressler, Falko ID - 12064 SN - 9781467349963 T2 - 2012 IEEE Vehicular Networking Conference (VNC) TI - On the applicability of Two-Ray path loss models for vehicular network simulation ER - TY - CONF AU - Sommer, Christoph AU - Joerer, Stefan AU - Segata, Michele AU - Tonguz, Ozan AU - Cigno, Renato Lo AU - Dressler, Falko ID - 12065 SN - 9781467359467 T2 - 2013 Proceedings IEEE INFOCOM TI - How shadowing hurts vehicular communications and how dynamic beaconing can help ER - TY - JOUR AU - Sommer, Christoph AU - Eckhoff, David AU - Dressler, Falko ID - 12066 JF - IEEE Transactions on Mobile Computing SN - 1536-1233 TI - IVC in Cities: Signal Attenuation by Buildings and How Parked Cars Can Improve the Situation ER - TY - CONF AU - Hellebrand, Sybille ID - 12979 T2 - 14th IEEE Latin American Test Workshop - (LATW'13) TI - Analyzing and Quantifying Fault Tolerance Properties ER - TY - JOUR AU - Domik, Gitta AU - Arens, Stephan AU - Stilow, Peter AU - Friedrich, Hauke ID - 15441 IS - 1 JF - IEEE computer graphics and applications TI - Helping High Schoolers Move the (Virtual) World VL - 33 ER - TY - CONF AU - Busjahn, Teresa AU - Schulte, Carsten ID - 15664 T2 - Koli Calling TI - The use of code reading in teaching programming ER - TY - CONF AU - Bennedsen, Jens AU - Schulte, Carsten ID - 15665 T2 - LaTiCE TI - Object Interaction Competence Model v. 2.0 ER - TY - CONF AU - Buchholz, Malte AU - Saeli, Mara AU - Schulte, Carsten ID - 15666 T2 - WiPSCE TI - PCK and reflection in computer science teacher education ER - TY - CONF AU - Schulte, Carsten ID - 15667 T2 - WiPSCE TI - Reflections on the role of programming in primary and secondary computing education ER - TY - CONF AU - Cheng, W. AU - Henzgen, S. AU - Hüllermeier, Eyke ID - 15752 T2 - In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany TI - Labelwise versus pairwise decomposition in label ranking ER - TY - CONF AU - Senge, Robin AU - del Coz, J. AU - Hüllermeier, Eyke ID - 15753 T2 - In Proceedings Workshop LWA-2009, Lernen-Wissensentdeckung-Adaptivität, Bamberg, Germany TI - Rectifying classifier chains for multi-label classification, Bamberg, Germany ER - TY - CONF AU - Busa-Fekete, Robert AU - Fober, T. AU - Hüllermeier, Eyke ED - Hoffmann, F. ED - Hüllermeier, Eyke ID - 15755 T2 - in Proceedings 23th Workshop Computational Intelligence, Dortmund Germany TI - Preference-based evolutionary optimization using generalized racing algorithms ER - TY - CONF AU - Henzgen, S. AU - Hüllermeier, Eyke ED - Hoffmann, F. ED - Hüllermeier, Eyke ID - 15756 T2 - in Proceedings 23th Workshop Computational Intelligence, Dortmund Germany TI - Weighted rank correlation measures based on fuzzy order relations ER - TY - CONF AU - Weng, P. AU - Busa-Fekete, Robert AU - Hüllermeier, Eyke ID - 15757 T2 - In Proceedings ECML/PKDD-Workshop on Reinforcement learning from Generalized Feedback:Beyond Numerical Rewards, Prague TI - Interactive Q-learning with ordinal rewards and unreliable tutor ER - TY - CONF AU - Busa-Fekete, Robert AU - Szörenyi, B. AU - Weng, P. AU - Hüllermeier, Eyke ID - 15758 T2 - In Proceedings ECML/PKDD-Workshop on Reinforcement learning from Generalized Feedback:Beyond Numerical Rewards, Prague TI - Preference-based evolutionary direct policy search ER - TY - CONF AU - Cheng, W. AU - Hüllermeier, Eyke ID - 15759 T2 - In Proceedings M-PREF`13, 7th Multidisciplinary Workshop on Advances in Preference Handling Beijing, China TI - A nearest neigbor approach to label ranking based on generalized labelwise loss minimization ER - TY - CONF AU - Shaker, Ammar AU - Hüllermeier, Eyke ED - Krempl, G. ED - Zliobaite, I. ED - Wang, Y. ED - Forman, G. ID - 15760 T2 - In Proceedings RealStream 2013, 1st International Workshop on Real-World Challenges for Data Stream Mining, Prague, Czech Republic TI - Event history analysis on data streams: An application to earthquake occurence ER - TY - CONF AU - Senge, Robin AU - del Coz, J.J. AU - Hüllermeier, Eyke ED - Schmidt-Thieme, L. ED - Spiliopoulou, M. ID - 15761 T2 - In Proceedings of GFKL-2012, 36th Annual Conference of the German Classification Society, Studies in Classification, Data Analysis and Knowledge Organization, Hildesheim, Germany TI - On the problem of error propagation in classier chains for multi-label classification. Data Analysis, Machine Learning and Knowledge Discovery ER - TY - CONF AU - Fober, T. AU - Klebe, G. AU - Hüllermeier, Eyke ED - Lausen, B. ED - Van den Poel, D. ED - Ultsch, A. ID - 15763 T2 - In Proceedings GFKL-2011, Conference of the German Classification Society, Frankfurt Germany TI - Local clique merging: An extension of the maximum common subgraph measure with applications in structural bioinformatics, Algorithms from and for Nature and Life ER - TY - JOUR AU - Ardizzone, Vincenzo AU - Lewandowski, Przemyslaw AU - Luk, M. H. AU - Tse, Y. C. AU - Kwong, N. H. AU - Lücke, Andreas AU - Abbarchi, Marco AU - Baudin, Emmanuel AU - Galopin, Elisabeth AU - Bloch, Jacqueline AU - Lemaitre, Aristide AU - Leung, P. T. AU - Roussignol, Philippe AU - Binder, Rolf AU - Tignon, Jerome AU - Schumacher, Stefan ID - 15866 JF - Scientific Reports SN - 2045-2322 TI - Formation and control of Turing patterns in a coherent quantum fluid ER - TY - JOUR AU - Tautz, Raphael AU - Da Como, Enrico AU - Wiebeler, Christian AU - Soavi, Giancarlo AU - Dumsch, Ines AU - Fröhlich, Nils AU - Grancini, Giulia AU - Allard, Sybille AU - Scherf, Ullrich AU - Cerullo, Giulio AU - Schumacher, Stefan AU - Feldmann, Jochen ID - 15867 JF - Journal of the American Chemical Society SN - 0002-7863 TI - Charge Photogeneration in Donor–Acceptor Conjugated Materials: Influence of Excess Excitation Energy and Chain Length ER - TY - JOUR AU - Luk, M. H. AU - Tse, Y. C. AU - Kwong, N. H. AU - Leung, P. T. AU - Lewandowski, Przemyslaw AU - Binder, R. AU - Schumacher, Stefan ID - 15868 JF - Physical Review B SN - 1098-0121 TI - Transverse optical instability patterns in semiconductor microcavities: Polariton scattering and low-intensity all-optical switching ER - TY - JOUR AU - Ling, Sanliang AU - Schumacher, Stefan AU - Galbraith, Ian AU - Paterson, Martin J. ID - 15870 JF - The Journal of Physical Chemistry C SN - 1932-7447 TI - Excited-State Absorption of Conjugated Polymers in the Near-Infrared and Visible: A Computational Study of Oligofluorenes ER - TY - THES AB - XML Encryption and XML Signature describe how to apply encryption and signing algorithms to XML documents. These specifications are implemented in a wide range of systems and frameworks processing sensitive data, including banking, eGovernment, eCommerce, military, and eHealth infrastructures. The article presents practical and highly critical attacks which allow to forge signed XML documents or reveal contents of encrypted XML data. AU - Somorovsky, Juraj ID - 15901 SN - 1611-2776 TI - On the insecurity of XML Security ER - TY - CONF AU - Falkenberg, Andreas AU - Mainka, Christian AU - Somorovsky, Juraj AU - Schwenk, Jörg ID - 15902 SN - 9780769550251 T2 - 2013 IEEE 20th International Conference on Web Services TI - A New Approach towards DoS Penetration Testing on Web Services ER - TY - JOUR AU - Mainka, Christian AU - Mladenov, Vladislav AU - Somorovsky, Juraj AU - Schwenk, Jörg ID - 15903 JF - CEUR Workshop Proceedings TI - Penetration test tool for XML-based web services VL - 965 ER - TY - CONF AU - Jager, Tibor AU - Paterson, Kenneth G. AU - Somorovsky, Juraj ID - 15918 T2 - 20th Annual Network and Distributed System Security Symposium, NDSS 2013, San Diego, California, USA, February 24-27, 2013 TI - One Bad Apple: Backwards Compatibility Attacks on State-of-the-Art Cryptography ER - TY - BOOK AU - Sureth, Caren ID - 14982 SN - 9783824469741 TI - Der Einfluss von Steuern auf Investitionsentscheidungen bei Unsicherheit ER - TY - CONF AU - Böttcher, Stefan AU - Hartel, Rita AU - Jacobs, Thomas ID - 15093 SN - 0302-9743 T2 - Big Data - 29th British National Conference on Databases, BNCOD 2013, TI - Fast Multi-update Operations on Compressed XML Data ER - TY - CONF AU - Böttcher, Stefan AU - Brandenburg, Marc AU - Hartel, Rita ID - 15094 T2 - WEBIST 2013 - Proceedings of the 9th International Conference on Web Information Systems and Technologies TI - DAG - Index - A Compressed Index for XML Keyword Search ER - TY - CONF AU - Fallah Tehrani, A. AU - Hüllermeier, Eyke ED - Montero, J. ED - Pasi, G. ED - Ciucci, D. ID - 15112 T2 - in Proceedings EUSFLAT-2013 8th International Conference on the European Society for Fuzzy Logic and Technology, Milano, Italy TI - Ordinal Choquistic regression ER -