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Zeit: Dienstag, 10. November 2020, 14:00 Uhr
Ort: Videokonferenz (Zoom-Meeting, Informationen siehe unten)
Referent: Mathias Obster, M.Sc. RWTH
Informatik 11 - Embedded Software
Thema: Unterstützung der SPS-Programmierung durch Statische Analyse
während der Programmeingabe
Abstract:
Durch abstrakte Interpretation und genauer durch Wertemengenanalyse lassen sich vollautomatisch Fehler in Programmcode finden, ohne diesen auszuführen. Bei Programmen für Speicherprogrammierbare Steuerungen (SPSen) ist dies für die Fehlervermeidung von besonderem Interesse, da sie im industriellen Umfeld zum Einsatz kommen.
In diesem Vortrag wird ein Ansatz vorgestellt, mit dem diese Methoden bereits während der Programmeingabe, also während der Entwicklung eines SPS-Programms, zur Fehlererkennung und -vermeidung beitragen kann. Dafür wurde das Analyseframework ARCADE.PLC erweitert, sodass es Analyseergebnisse in einer Entwicklungsumgebung darstellen kann, die auch in der Industrie zum Einsatz kommt. So können dort sowohl Warnungen vor potenziell fehlerhaftem Programmverhalten als auch mögliche Variablenwerte dargestellt werden.
Ein neu eingeführter inkrementeller Ansatz kann darüber hinaus den Berechnungsaufwand verringern, der sonst durch die häufige Ausführung der Analysen entsteht. Dabei wird ausgenutzt, dass sich während der Entwicklung in kurzen Zeitintervallen üblicherweise nur kleine Änderungen für das Gesamtprogramm ergeben.
Für die Bereitstellung einer integrierten Lösung für den Anwender wurden die Konzepte und eine Erweiterung für eine Entwicklungsumgebung für SPS-Programme prototypisch umgesetzt und im Rahmen einer kleinen Nutzerstudie evaluiert. Dabei stand die Frage im Mittelpunkt, ob Programmierer während der Eingabe und Bearbeitung eines SPS-Programms von Ergebnissen der Statischen Analyse profitieren können.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Thema: [Promotion Mathias Obster] Vortrag
Uhrzeit: 10.Nov.2020 02:00 PM Amsterdam, Berlin, Rom, Stockholm, Wien
Zoom-Meeting beitreten
https://rwth.zoom.us/j/98589897540?pwd=OWZidEpuU1N5bjA5UFIxcFhNUEJidz09
Meeting-ID: 985 8989 7540
Kenncode: 111012
Dear members of the Computer Science Department,
You are cordially invited to the talk of Prof. Michael Schaub, RWTH Aachen.
Title: Learning from signals on graphs with unobserved edges
Abstract:
In many applications we are confronted with the following system identification scenario: we observe a dynamical process that describes the state of a system at particular times. Based on these observations we want to infer the (dynamical) interactions between the entities we observe. In the context of a distributed system, this typically corresponds to a "network identification" task: find the (weighted) edges of the graph of interconnections.
However, often the number of samples we can obtain from such a process are far too few to identify the edges of the network exactly. Can we still reliably infer some aspects of the underlying system?
Motivated by this question we consider the following identification problem: instead of trying to infer the exact network, we aim to recover a (low-dimensional) statistical model of the network based on the observed signals on the nodes. More concretely, here we focus on observations that consist of snapshots of a diffusive process that evolves over the unknown network. We model the (unobserved) network as generated from an independent draw from a latent stochastic blockmodel (SBM), and our goal is to infer both the partition of the nodes into blocks, as well as the parameters of this SBM. We present simple spectral algorithms that provably solve the partition and parameter inference problems with high-accuracy. We further discuss some possible variations and extensions of this problem setup.
https://www.informatik.rwth-aachen.de/go/id/jgfsd
Wednesday, 21.10.2020, 10:00
Join Zoom Meeting https://rwth.zoom.us/j/92047949381?pwd=LzIwUW96WEM0MkRjZ01FUmhwd1I3QT09<https://www.google.com/url?q=https://rwth.zoom.us/j/92047949381?pwd%3DLzIwU…>
Meeting ID: 920 4794 9381 Password: unravel
Best regards
Helen Bolke-Hermanns
Helen Bolke-Hermanns
Fachgruppe Informatik
RWTH Aachen University
Ahornstr. 55, D-52074 Aachen
Building E3, 2nd floor, Room 9218
Telefon: +49 (241) 80-21-004
Fax: +49 (241) 80-22 215
E-Mail: Helen.Bolke-Hermanns(a)Informatik.RWTH-Aachen.de<mailto:Helen.Bolke-Hermanns@Informatik.RWTH-Aachen.de>
[rwth_informatik_bild_rgb]
Dear members of the Computer Science Department,
You are cordially invited to the talk of the UnRAVeL guest Guy Van den Broeck, UCLA
Title: From Probabilistic Circuits to Probabilistic Programs and Back
Abstract:
Probabilistic graphical models are a rich staple of probabilistic AI. However, they make a very specific choice of abstraction: probability distributions are represented by their variable-level (in)dependencies. In this talk I present some recent work on probabilistic models that go beyond classical PGMs, and make a radically different choice of abstraction; one that is computational. Concretely, I will discuss two classes of models: probabilistic circuits and probabilistic programs. Probabilistic circuits represent distributions through the computation graph of probabilistic inference. They move beyond PGMs by guaranteeing tractable inference for certain classes of queries. Probabilistic programs represent distributions through higher-level primitives of computation: iteration, branching, and procedural abstraction. They move beyond PGMs by looking "inside" of the dependencies. Finally, I will illustrate how these two computational abstractions are themselves closely related, by showing how the Dice probabilistic programming language compiles probabilistic programs into probabilistic circuits for inference.
Wednesday, Oct. 7th, 5.00 pm
The talk will take place as a zoom-session: https://rwth.zoom.us/j/92047949381?pwd=LzIwUW96WEM0MkRjZ01FUmhwd1I3QT09<https://www.google.com/url?q=https://rwth.zoom.us/j/92047949381?pwd%3DLzIwU…>
Meeting ID: 920 4794 9381, Password: unravel
Best regards
Helen Bolke-Hermanns
Helen Bolke-Hermanns
Fachgruppe Informatik
RWTH Aachen University
Ahornstr. 55, D-52074 Aachen
Building E3, 2nd floor, Room 9218
Telefon: +49 (241) 80-21-004
Fax: +49 (241) 80-22 215
E-Mail: Helen.Bolke-Hermanns(a)Informatik.RWTH-Aachen.de<mailto:Helen.Bolke-Hermanns@Informatik.RWTH-Aachen.de>
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Zeit: Dienstag, 03. November 2020, 12:30 Uhr
Zoom:
https://rwth.zoom.us/j/92659511051?pwd=TmVibnlCNUdYZzlZSW52eCs0V2RjQT09
Referent: Dr. Andreas Wortmann
Thema: Model-Driven Architecture and Behavior of Cyber-Physical Systems
Vorstellung des Habilitationsvorhabens.
Abstract:
Systems engineering has produced striking results in many domains.
Researchers and practitioners have devised concepts, methods, tools
that autonomously move vehicles, enable doctors to conduct remote
surgeries across continents, and sent astronauts into space. All of
these cyber-physical systems are driven by software whose complexity
increases tremendously. Overcompensating this growth in software and
systems complexity demands novel methods that increase the
abstraction in systems engineering, advance automation, and
facilitate the integration of domain expert solutions. Model-based
systems engineering aims to address this complexity by advancing
systems engineering from its contemporary document-based processes to
sophisticated model-based processes. In the latter, abstract models
serve as means for systems design, communication, documentation, and
basis for implementation. But to overcompensate the growth in
complexity, using models as secondary artifacts is insufficient.
Comprehensive research in software engineering has led to recognizing
that model-driven processes, in which models are the primary
engineering artifacts, can significantly improve abstraction,
automation, and domain-specific modeling to address the increasing
complexity in systems engineering. Yet, model-based systems
engineering focuses on informal models that are hardly accessible to
meaningful automation and overly generic.
This thesis summarizes 14 selected publications of a research program
towards a model-driven systems engineering that operates on
domain-specific modeling languages, supports sophisticated modeling
methods, and enables the systematic operation of cyber-physical
systems. The results of this research program cover four substantial
challenges towards the model-driven engineering of cyber-physical
systems: First, it contributes to understanding the use of models and
modeling languages for cyber-physical systems through two
comprehensive literature studies on modeling for cyber-physical
systems in Industry 4.0 and mobile robotics. The studies surveyed
over 3.000 publications each and produced insights into requirements
for the efficient model-driven engineering and operations of
cyber-physical systems in both domains. Second, it conduces novel
foundations for the efficient engineering of domain-specific modeling
languages based on the requirements identified in both studies. These
foundations introduce innovative notions of language components and
their composition upon which families of domain-specific modeling
languages can be created systematically efficiently. Third, it
leverages these foundations to produce modeling languages to describe
functional architectures and geometric-physical architectures of
cyber-physical systems that support unprecedented automated modeling
methods, including tracing, decomposition, and semantic differencing,
to facilitate modeling, maintaining, and evolving these
architectures. Fourth, it exploits the novel language engineering
foundations and the unprecedented automated modeling methods to
alleviate the systematic operation of cyber-physical systems with
digital twins that represent and optimize the observed systems.
Hence, this research program forges a bridge from observations on
modeling cyber-physical systems, over software language engineering
and modeling methods, to their operation that supports researchers
and practitioners to advance from the contemporary document-based
engineering of cyber-physical systems to their systematic
model-driven engineering.
Vita:
Andreas Wortmann is a tenured research associate at the Chair for
Software Engineering at RWTH Aachen University. There, he leads a
team on model-driven systems engineering, coordinates a workstream of
the Internet of Production excellence cluster, and advises the Center
for Systems Engineering. He conducts research in model-driven
software and systems engineering, formal methods in software
engineering, and software language engineering, which is documented
in over 70 publications. Moreover, he has chaired and organized
various international conferences and workshops, serves on the board
of the European Association for Programming Languages and Systems
(EAPLS), and co-chairs the working group on model-based systems
engineering of the German INCOSE chapter GfSE.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Prof. Dr. Bernhard Rumpe | http://www.se-rwth.de
Lehrstuhl Software Engineering | Informatik 3
Ahornstr. 55, 52074 Aachen, Germany | RWTH Aachen University
Phone ++49 241 80-21301 / Fax -22218 |
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Zeit: Dienstag, 22. September 2020, 16.00 Uhr
Ort: Videokonferenz (Zoom-Meeting, Informationen siehe unten)
Referent: Markus Hoehnerbach M.Sc.
High-Performance and Automatic Computing
Thema: A Framework for the Vectorization of Molecular Dynamics Kernels
Abstract:
We introduce a domain-specific language (DSL) for many-body potentials,
which are used in molecular dynamics (MD) simulations in the area of
materials science. We also introduce a compiler to translate the DSL
into high-performance code suitable for modern supercomputers.
We begin by studying ways to speedup up potentials on supercomputers
using two case studies: The Tersoff and the AIREBO potentials. In both
case studies, we identify a number of optimizations, both
domain-specific and general, to achieve speedups of up to 5x; we also
introduce a method to keep the resulting code performance portable.
During the AIREBO case study, we also discover that the existing code
contains a number of errors. This experience motivates us to include the
derivation step, the most error-prone step in manual optimization, in
our automation effort.
After having identified beneficial optimization techniques, we create a
``potential compiler'', short PotC, which generates fully-usable
performance-portable potential implementations from specifications
written in our DSL. DSL code is significantly shorter (20x to 30x) than
a manual code, reducing both manual work and opportunities to introduce
bugs.
We present performance results on five different platforms: Three CPU
platforms (Broadwell, Knights Landing, and Skylake) and two GPU
platforms (Pascal and Volta). While the performance in some cases
remains far below that of hand-written code, it also manages to match or
exceed manually written implementations in other cases. For these cases,
we achieve speedups of up to 9x compared to non-vectorized code.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Thema: Dissertation M. Höhnerbach
Uhrzeit: 22.Sep.2020 03:45 PM Paris
Zoom-Meeting beitreten
https://rwth.zoom.us/j/98920238040?pwd=Q3F1OStEcklpcDV1Vk5IWEp0cFFEQT09
Meeting-ID: 989 2023 8040
Kenncode: 668020
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Zeit: Freitag, 14. August 2020, 13.00 Uhr
Zoom: https://rwth.zoom.us/j/94972202308?pwd=Y1o5cnBQYTYxdS9tYldaN0piQkozQT09
Referent: Jan Rüth, M.Sc.
Lehrstuhl Informatik 4
Thema: Measuring the Evolution of the Internet in the Age of Giants
Abstract:
The Internet has evolved into an essential cornerstone of modern life. At its
core, it is seemingly still powered by protocols developed in the late 1980s.
Since then, the Internet has experienced a colossal visible evolution, e.g.,
from delivering small text files over dynamic websites to highly interactive
and bulky content that define whole economic sectors. Notwithstanding, it is
hard to believe that this change demanded no technological evolution, and in
fact, research and industry have worked on many mechanisms and improvements to
the core protocols. Today, we see Internet giants such as Google, Facebook, or
Akamai controlling clients, servers, and networks driving these changes. Still,
research has shown that these innovations often remain hard to deploy in
practice as the Internet has condensed to supporting only a small set of
protocols and parts of their features today.
In this talk, we present novel Internet measurement methodologies to gain an
understanding of how the Internet has evolved from textbook knowledge, how it
deviates from standardized practices, and how these discovered discrepancies
affect Internet operation. To this end, we recognize the critical role of
Internet giants and specifically investigate their impact on core Internet
technologies.
In this presentation, we focus on the transport layer and examine TCP
initial congestion window configurations, Congestion Control fairness, and
QUIC as a new Internet transport. Lastly, we present how WebAssembly is
largely abused for cryptocurrency mining in the Web today.
Our contributions demonstrate that Internet giants coin practical Internet
evolution. While they are keen to standardize their innovations, their
configurations are often a well-kept secret. Their market dominance challenges
the innovation of others, but even they are not immune to an abuse of their
technology.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Zeit: Montag, 13. Juli 2020, 16:00 Uhr
Zoom:
<https://rwth.zoom.us/j/95676455814?pwd=NUEvVnFVNEVLSjFsTWY2OEw2VWhrdz09>
https://rwth.zoom.us/j/95676455814?pwd=NUEvVnFVNEVLSjFsTWY2OEw2VWhrdz09
Meeting-ID: 956 7645 5814
Passwort: 302988
Referentin: M.Eng. Rihan Hai
Lehrstuhl Informatik 5
Thema: Data integration and Metadata Management in Data Lakes
Abstract:
Although big data has been discussed for some years, it still has many
research challenges, such as the variety of data. Non-integrated data
management systems with heterogeneous schemas, query languages, and data
models result in information silos. As traditional 'schema-on-write'
approaches such as data warehouses cannot solve the challenges to
efficiently integrate, access, and query the information silos, data lake
systems have been proposed as a solution to this problem. Data lakes are
repositories storing raw data in its original format and providing a common
access interface.
In this thesis, we present a comprehensive and flexible data lake
architecture and the prototype system Constance. First, we propose a native
mapping representation to capture the hierarchical structures of nested
mappings and efficient mapping generation algorithms. Second, to provide a
unified querying interface, we design a novel query rewriting engine that
combines logical methods for data integration based on declarative mappings
with the big data processing system Apache Spark. Third, we also study the
formalism of the generated schema mappings as dependencies. Our algorithmic
approach transforms schema mappings expressed in second-order logic to their
logically equivalent first-order forms. Finally, we introduce
clustering-based algorithms to discover relaxed functional dependencies,
which enrich the metadata and improve data quality in the data lake.
Es laden ein: die Dozentinnen und Dozenten der Informatik
_______________________________
Leany Maaßen
RWTH Aachen University
Lehrstuhl Informatik 5, LuFG Informatik 5
Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke,
Prof. Gerhard Lakemeyer Ph.D.
Ahornstrasse 55
D-52074 Aachen
Tel: 0241-80-21509
Fax: 0241-80-22321
E-Mail: maassen(a)dbis.rwth-aachen.de
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Zeit: Dienstag, 30. Juni 2020, 14.00-15.00 Uhr
Zoom:
<https://rwth.zoom.us/j/95676455814?pwd=NUEvVnFVNEVLSjFsTWY2OEw2VWhrdz09>
https://rwth.zoom.us/j/95676455814?pwd=NUEvVnFVNEVLSjFsTWY2OEw2VWhrdz09
Meeting-ID: 956 7645 5814
Passwort: 302988
Referent: Dipl.-Kfm. Markus Beutel
Thema: End-to-End-Integration von komplementären Mobilitätsdienstleistungen
durch unternehmensübergreifende Anbieterkooperation
Abstract:
Reisenden steht heutzutage eine Vielzahl unterschiedlicher Verkehrsmodi zur
Verfügung. Dabei können sich viele Fortbewegungsmittel aufgrund
individueller Charakteristika gegenseitig ergänzen, anstatt sich zu
substituieren. Durch eine zumindest in Teilen segmentierte und
anbieterspezifische Bereitstellung von Verkehrsdienstleistungen können
beispielsweise ökonomische und ökologische Ineffizienzen entstehen. Im
Zentrum einer vollständigen Digitalisierung und Integration heterogener
Mobilitätsdienstleistungen sollte daher der ganzheitliche
Dienstleistungsprozess, über die gesamte Servicekette hinweg, in den
Mittelpunkt gestellt werden.
Das übergeordnete Ziel dieser Arbeit besteht in der Erforschung einer
integrierten Bereitstellung sich ergänzender Mobilitätsdienstleistungen,
über Unternehmensgrenzen hinweg. Ausgangspunkt dieser Arbeit bildet die
Untersuchung einer spezifischen Mobilitätsplattform im Hinblick auf
verschiedene Integrationsfaktoren. Auf Basis einer umfassenden Analyse von
Mobilitätsplattformen und in Verbindung mit der Beschreibung eines
organisatorischen Rollenmodells werden daraufhin mögliche
Anbieterkooperationsszenarien beschrieben. Um die unternehmensübergreifende
Integration auf Prozessebene zu betrachten, wird schließlich ein Ansatz zur
Erweiterung eines Softwarewerkzeugs zur Fusion von Geschäftsprozessmodellen
evaluiert.
Es laden ein: die Dozentinnen und Dozenten der Informatik
_______________________________
Leany Maaßen
RWTH Aachen University
Lehrstuhl Informatik 5, LuFG Informatik 5
Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke,
Prof. Gerhard Lakemeyer Ph.D.
Ahornstrasse 55
D-52074 Aachen
Tel: 0241-80-21509
Fax: 0241-80-22321
E-Mail: maassen(a)dbis.rwth-aachen.de
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Zeit: Freitag, 08. Mai 2020, 11.00 Uhr
Zoom:
https://rwth.zoom.us/j/94637532047?pwd=aVlweVRLdGU0OWNjdEh4TmJhSktrUT09
Referent: Dipl.-Medieninf. István Koren
Lehrstuhl Informatik 5
Thema: DevOpsUse: Community-Driven Continuous Innovation of Web Information
Infrastructures
Abstract:
The steady evolution of the Web over the last thirty years was shaped by an
interplay of new technologies and innovative applications. The current
challenges are caused by the ongoing digital transformation of whole
societies. In Industry 4.0 for example, these are changing workplace
settings and the adoption of the Internet of Things. Inhibiting the demanded
fast innovation cycles, this may create a disruptive and unstable
environment in which the requirements of heterogeneous professional
communities need to be addressed.
Information systems infrastructure, while only partially visible and thus
hard to grasp, has a strong influence on practices in professional
communities. Therefore, our aim is to stabilize the dichotomies apparent in
the Web by means of an agile information systems development methodology. It
supports the evolution of infrastructure through community-driven and
model-based technologies to guide it on a sustainable path of continuous
innovation. Agile development practices in software engineering, in
particular the already established DevOps approach, promote stronger
cooperation between development and operating teams. Our DevOpsUse
methodology additionally fosters a stronger involvement of end user
communities in software development processes by including them in the
process of infrastructuring, i.e. the appropriation of infrastructure during
its usage.
The developed DevOpsUse methodology has been successfully validated by the
transitions between three generations of technologies: near real-time
peer-to-peer Web architectures, edge computing, and the Internet of Things.
In particular, we were able to demonstrate our methodologys capabilities
through longitudinal studies in several large-scale international
digitalization projects. Beyond Web information systems, the framework and
its open source tools are applicable in further innovative areas like mixed
reality. Its broad adaptability testifies that DevOpsUse has the potential
to unlock sustainable innovation capabilities.
Es laden ein: die Dozentinnen und Dozenten der Informatik
_______________________________
Leany Maaßen
RWTH Aachen University
Lehrstuhl Informatik 5, LuFG Informatik 5
Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke,
Prof. Gerhard Lakemeyer Ph.D.
Ahornstrasse 55
D-52074 Aachen
Tel: 0241-80-21509
Fax: 0241-80-22321
E-Mail: maassen(a)dbis.rwth-aachen.de
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Zeit: Dienstag, 05. Mai 2020, 14.00 Uhr
Zoom:
https://us02web.zoom.us/j/84813327259?pwd=Y2NydlRMRzE1dkpkcmpERkFwMWZYZz09
Referent: Kazuki Irie, M.Sc.
Thema: Advancing Neural Language Modeling in Automatic Speech Recognition//
Abstract:
Statistical language modeling is one of the fundamental problems in
natural language processing. In the recent years, language modeling has
seen great advances by active research and engineering efforts in
applying artificial neural networks, especially those which are
recurrent. The application of neural language models to speech
recognition has now become well established and ubiquitous. Despite this
impression of some degree of maturity, we claim that the full potential
of the neural network based language modeling is yet to be explored. In
this thesis, we further advance neural language modeling in automatic
speech recognition, by investigating a number of new perspectives. From
the architectural view point, we investigate the newly proposed
Transformer neural networks for language modeling application. The
original model architecture proposed for machine translation is studied
and modified to accommodate the specific task of language modeling.
Particularly deep models with about one hundred layers are developed. We
present an in-depth comparison with the state-of-the-art recurrent
neural network language models based on the long short-term memory.
While scaling up language modeling to larger scale datasets, the
diversity of the data emerges as an opportunity and a challenge. The
current state-of-the-art neural language modeling lacks a mechanism of
handling diverse data from different domains for a single model to
perform well across different domains. In this context, we introduce
domain robust language modeling with neural networks, and propose two
solutions. As a first solution, we propose a new type of adaptive
mixture of experts model which is fully based on neural networks. In the
second approach, we investigate knowledge distillation from multiple
domain expert models, as a solution to the large model size problem seen
in the first approach. Methods for practical applications of knowledge
distillation to large vocabulary language modeling are proposed, and
studied to a large extent.
Finally, we investigate the potential of neural language models to
leverage long-span cross-sentence contexts for cross-utterance speech
recognition. The appropriate training method for such a scenario is
under-explored in the existing works. We carry out systematic
comparisons of the training methods, allowing us to achieve improvements
in cross-utterance speech recognition. In the same context, we study the
sequence length robustness for both recurrent neural networks based on
the long short-term memory and Transformers, because such a robustness
is one of the fundamental properties we wish to have, in neural networks
with the ability to handle variable length contexts. Throughout the
thesis, we tackle these problems through novel perspectives of neural
language modeling, while keeping the traditional spirit of language
modeling in speech recognition.
Es laden ein: die Dozentinnen und Dozenten der Informatik
--
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Stephanie Jansen
Faculty of Mathematics, Computer Science and Natural Sciences
HLTPR - Human Language Technology and Pattern Recognition
RWTH Aachen University
Ahornstraße 55
D-52074 Aachen
Tel. Frau Jansen: +49 241 80-216 06
Tel. Frau Andersen: +49 241 80-216 01
Fax: +49 241 80-22219
sek(a)i6.informatik.rwth-aachen.de
www.hltpr.rwth-aachen.de
Tel: +49 241 80-216 01/06
Fax: +49 241 80-22219
sek(a)i6.informatik.rwth-aachen.de
www.hltpr.rwth-aachen.de