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Zeit: Freitag, 12. Juli 2019, 10.00 Uhr
Ort: Informatikzentrum, E3, Raum 9222
Referent: Dipl.-Inform. Malte Nuhn
Thema: Unsupervised Training with Applications in Natural Language
Processing//
Abstract:
The state-of-the-art algorithms for various natural language processing
tasks require large amounts of labeled training data. At the same time,
obtaining labeled data of high quality is often the most costly step in
setting up natural language processing systems.Opposed to this,
unlabeled data is much cheaper to obtain and available in larger
amounts.Currently, only few training algorithms make use of unlabeled
data. In practice, training with only unlabeled data is not performed at
all. In this thesis, we study how unlabeled data can be used to train a
variety of models used in natural language processing. In particular, we
study models applicable to solving substitution ciphers, spelling
correction, and machine translation. This thesis lays the groundwork for
unsupervised training by presenting and analyzing the corresponding
models and unsupervised training problems in a consistent manner.We show
that the unsupervised training problem that occurs when breaking
one-to-one substitution ciphers is equivalent to the quadratic
assignment problem (QAP) if a bigram language model is incorporated and
therefore NP-hard. Based on this analysis, we present an effective
algorithm for unsupervised training for deterministic substitutions. In
the case of English one-to-one substitution ciphers, we show that our
novel algorithm achieves results close to human performance, as
presented in [Shannon 49].
Also, with this algorithm, we present, to the best of our knowledge, the
first automatic decipherment of the second part of the Beale
ciphers.Further, for the task of spelling correction, we work out the
details of the EM algorithm [Dempster & Laird + 77] and experimentally
show that the error rates achieved using purely unsupervised training
reach those of supervised training.For handling large vocabularies, we
introduce a novel model initialization as well as multiple training
procedures that significantly speed up training without hurting the
performance of the resulting models significantly.By incorporating an
alignment model, we further extend this model such that it can be
applied to the task of machine translation. We show that the true
lexical and alignment model parameters can be learned without any
labeled data: We experimentally show that the corresponding likelihood
function attains its maximum for the true model parameters if a
sufficient amount of unlabeled data is available. Further, for the
problem of spelling correction with symbol substitutions and local
swaps, we also show experimentally that the performance achieved with
purely unsupervised EM training reaches that of supervised training.
Finally, using the methods developed in this thesis, we present results
on an unsupervised training task for machine translation with a ten
times larger vocabulary than that of tasks investigated in previous work.
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
Dear all,
part of the programme of the research training group UnRAVeL is a series
of introductory lectures on the topics of „randomness“ and „uncertainty“
in UnRAVeL’s research thrusts algorithms and complexity, verification,
logic and languages, and their application scenarios. Each lecture is
delivered by one of the researchers involved in UnRAVeL. The main aim is
to provide doctoral researchers as well as master students a broad
overview of the subjects of UnRAVeL.
This year, 12 UnRAVeL professors will answer the following questions,
based on one of their recent scientific results:
* How did you get to this result?
* How did you come up with certain key ideas?
* How did you cope with obstacles on the way? Which ideas you had did
not work out?
Following these talks, PhD students will give an informal summary of
their doctoral studies within UnRAVeL.
All interested doctoral researchers and master students are invited to
attend the UnRAVeL lecture series 2021 and engage in discussions with
researchers and doctoral students.
Details information can be found on
https://www.unravel.rwth-aachen.de/cms/UnRAVeL/Studium/~pzix/Ringvorlesung-…
All events take place on *Thursdays from 16:30 to 18:00 on Zoom*
https://rwth.zoom.us/j/96043715437?pwd=U0dRczkyQjRCY21abW13TDNmUHlhUT09
* 15/04/2021 Survey Lecture: Erika Ábrahám: Probabilistic Hyperproperties
* 22/04/2021 Jürgen Giesl: Inferring Expected Runtimes of
Probabilistic Programs
* 29/04/2021 Erich Grädel: Hidden Variables in Quantum Mechanics and
Logics of Dependence and Independence
* 06/05/2021 Christof Löding: Learning Automata for Infinite Words
* 20/05/2021 Martin Grohe: The Logic of Graph Neural Networks
* 10/06/2021 Britta Peis: Sensitivity Analysis for Submodular Function
Optimization with Applications in Algorithmic Game Theory
* 17/06/2021 Nils Nießen: Optimised Maintenance of Railway Infrastructure
* 24/06/2021 Gerhard Lakemeyer: Uncertainty in Robotics
* 01/07/2021 Joost-Pieter Katoen: The Surprises of Probabilistic
Termination
* 08/07/2021 Christina Büsing: Robust Minimum Cost Flow Problem Under
Consistent Flow Constraints
* 15/07/2021 Ringvorlesung: Gerhard Woeginger: Bilevel optimization
* 22/07/2021 Ulrike Meyer: Malware Detection
We are looking forward to seeing you at the lectures.
Best regards,
Tim Seppelt for the organisation committee
https://www.unravel.rwth-aachen.de/global/show_picture.asp?id=aaaaaaaaaydoc…
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Zeit: Freitag, 19. Februar 2021, 11.00 Uhr
Zoom: https://rwth.zoom.us/j/2452218628
Referent: Andrea Schnorr, M.Sc.
LuFG i12
Thema: Feature Tracking for Space-Filling Structures
Abstract:
Feature-based visualization is a proven strategy to deal with the massive
amounts of data emerging from time-dependent simulations: the analysis
focuses on meaningful structures, i.e., said features.
Feature tracking algorithms aim at automatically finding corresponding
objects in successive time steps of these time-dependent data sets in order
to assemble the individual objects into spatio-temporal features.
Classically, feature-based visualization has focused on sparse structures,
i.e. structures which cover only a small portion of the data domain.
Given a sufficiently high temporal resolution, existing tracking approaches
are able to reliably resolve the correspondence between feature objects of
successive time steps.
Our research is motivated by our collaborators' work on the statistical
analysis of structures that are space-filling by definition: dissipation
elements.
Space-filling structures partition the entire domain.
Our collaborators aim at extending their statistical analysis to a
time-dependent setting.
Hence, we introduce an efficient approach for general feature tracking
which handles both sparse and space-filling data.
To this end, we develop a framework for automatic evaluation of tracking
approaches, an algorithmic framework for feature tracking, and an efficient
implementation of this framework.
First, we propose a novel evaluation framework based on algorithmic data
generators, which provide synthetic data sets and the corresponding ground
truth data.
This framework facilitates the structured quantitative analysis of an
approach's feature tracking performance and the comparison of different
approaches based on the resulting measurements.
Second, we introduce a novel approach for tracking both sparse and
space-filling features.
The correspondence between neighboring time-steps is determined by
successively solving two graph optimization problems.
In the first phase, one-to-one assignments are resolved by computing a
maximum-weight, maximum-cardinality matching on a bi-partite graph.
In its second phase, the algorithm detects events by finding a maximum
weight independent set in a graph of all possible, potentially conflicting
event explanations.
Third, we show an optimized version of the second stage of the tracking
framework which exploits the model-specific graph structure arising for the
tracking problem.
The method's effectiveness is demonstrated by a set of case studies
including the use of the evaluation framework as well as the analysis of
miscellaneous real-world simulation data sets.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Zeit: Dienstag, 8. Juni 2021, 14:00 Uhr
Ort:
https://rwth.zoom.us/j/98025555800?pwd=SUgxYWR3RGxnaGNMZGg5bmNqeGZYQT09
Meeting-ID: 980 2555 5800
Kenncode: 419493
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Vortragender: Holger H. Hoos (Universität Leiden)
Titel: Cooperative competition: A new way of solving computationally challenging problems in AI and beyond
Abstract:
Progress in solving challenging problems in artificial intelligence, computer science at large, and beyond is driven, to a significant extent, by competition - regular algorithm competitions as well as comparative performance evaluation against state-of-the-art methods from the literature. A prominent example for this is the satisfiability problem in propositional logic (SAT), an NP-hard problem that not only lies at the foundations of computer science, but also plays a key role in many real-world applications, notably in ensuring the correctness of hard- and software. In this presentation, I will argue that it is time to rethink the way we assess the state of the art in solving problems such as SAT and the incentives for improving it. I will demonstrate how automated algorithm selection and configuration techniques based on sophisticated machine learning and optimisation methods have fundamentally changed not only the state of the art in solving SAT and many other NP-hard problems, but also provide a natural basis for cooperative competition - a new approach for achieving and assessing progress not merely in solving these problems, but also in the way we approach them as a scientific community.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Zeit: Montag, 28. Juni 2021, 14.00 Uhr
Ort: https://rwth.zoom.us/my/b.kueppers?pwd=dDFyZkJvUDRFbFlFbnh0R3hQZVVRdz09
Referent: Bastian Küppers, M.Sc.
IT Center / Learning Technologies Research Group
Thema: Development of a Framework for E-Assessment on Students’ Devices
Abstract:
In line with the general trend towards digitization, teaching at German
universities is increasingly relying on digital elements, such as
learning management systems or smartphone apps. This evolution is also
taking place in exercises and practical courses associated with
lectures. For example, it is quite common for students to use their own
devices in programming exercises. However, examinations are not yet part
of this development. The decision to continue writing exams on paper is
often made based on reservations, most of which concern the fairness and
reliability of e-assessment. In particular, students’ reservations, because they may feel responsible for a functioning device, are a
major obstacle to the introduction of e-Assessment. It is therefore
important to overcome these reservations, to be able to successfully
implement e-assessment. In addition to reservations, financial reasons
are often an obstacle. The acquisition and administration of a suitable
IT infrastructure is expensive, both in terms of material costs and
personnel costs. Since the majority of students already own devices that
are potentially suitable for e-assessment, Bring Your Own Device (BYOD)
is a possible solution to the financial aspect. However, a BYOD approach
to e-assessment comes with new challenges that have to be tackled.
On the way to a solution that works in the previously described
scenario, requirements engineering has been a vital part of the process.
Therefore, students and teachers as well as official policies have been
consulted to derive the requirements to a feasible BYOD approach to
e-assessment. In addition to the found requirements, a threat model has
been developed to identify additional requirements to the security of
such an approach. Afterwards, a software framework was developed and
implemented which fulfilled the gathered requirements. Finally, the
software prototype was evaluated regarding functionality, usability,
performance, and security. Beyond the software prototype, an
organizational framework has been developed which covers (hardware)
requirements for the institute of higher education as well as important
organizational details for the conduction of electronic assessment.
In this talk, we discuss important key points of the research process
and present the results of our work.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Zeit: Freitag, 18. Juni 2021, 15.00 Uhr
Ort: Zoom-Videokonferenz
Link: https://rwth.zoom.us/j/94806153993?pwd=dUIwQWYxNUx1cG1jOEpLc25WdmJwUT09
Referent: Marcel Hark M.Sc.
Lehr- und Forschungsgebiet Informatik 2
Thema: Towards Complete Methods for Automatic Complexity and Termination Analysis of (Probabilistic) Programs
Abstract:
The increasing importance of computer programs in our everyday life has led to more and more
complex software systems. To prove correctness of such a system, formal verification is the
standard methodology. Two of the most important properties of a program are its termination
behavior and its efficiency.
Moreover, in recent years randomization in programming has gained a lot of interest. For
example, to model non-deterministic behavior in real-world applications, probabilistic concepts
have proved very successful.
In this talk, we investigate formal verification of programs which also feature assignments via
discrete probability distributions. In particular, we are interested in proving (non-)termination
and inferring bounds on the (expected) worst-case runtime of such programs.
In general, formal verification of programs is undecidable. Still, whenever possible, complete
approaches for verifying certain properties on (sub-)classes of programs are preferable to
incomplete ones since they always yield definite results, i.e., either a proof or a counterexample.
Hence, we also characterize sub-classes of programs for which we can present complete approaches
for analyzing termination and runtime complexity.
To analyze systems arising from real-world applications, formal verification has to proceed
automatically. Thus, we discuss the automation of our results as well.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Marcel Hark
Research Group Computer Science 2
RWTH Aachen University
Ahornstr. 55
52074 Aachen
Germany
E-Mail: marcel.hark(a)cs.rwth-aachen.de"
Phone: +49-241/80-21218
Fax: +49-241/80-22217
Room: 4208
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Zeit: Mittwoch, 26. Mai 2021, 14:00 Uhr
Zoom: https://rwth.zoom.us/j/92362727123?pwd=MjhBT2VaczI0RDE1c2VNQkxGckw3UT09 <https://rwth.zoom.us/j/92362727123?pwd=MjhBT2VaczI0RDE1c2VNQkxGckw3UT09>
Referent: Philipp Wacker, M.Sc.
Lehrstuhl Informatik 10
Thema: Interaction Techniques for Mid-Air Pen Input in Handheld Augmented Reality
Abstract:
Augmented Reality changes the way we interact with virtual information. Currently, virtual information is shown on 2D screens, separated from the real world. With Augmented Reality, virtual content can be shown directly embedded in the real world. This opens up the area of situated modeling in which virtual models are designed in context of the real world to, for example, print them out using a 3D printer. In an initial study, we show that sketching on physical objects improves stroke accuracy compared to strokes on virtual objects, and that features guiding a stroke, either through a concave or convex shape or through a visual guide, further improve the accuracy especially for physical objects.
The most available form of Augmented Reality (AR) is Handheld Augmented Reality which shows the virtual information embedded in the camera view of everyday smartphones or tablets. However, continuously specifying a 3D position—needed, e.g., for drawing in mid-air—is not directly possible in today’s systems. We build the ARPen system to allow for situated modeling in Handheld AR, requiring only a 3D-printed pen and a consumer smartphone. But many essential interactions are not yet clear for such a bimanual system. We design and evaluate selection & manipulation techniques to adjust the pose of a mid-air object, as well as menu techniques to control properties of objects in the scene. We show that ray-casting techniques, especially through the tip of the pen, generally perform well. However, interacting on the touchscreen or even combinations of both touchscreen and mid-air input also achieve promising results. To overcome perception issues of determining the depth of virtual objects in Handheld AR, we design depth visualizations that show the position of the pen tip in relation to other objects in the scene. We identify that a heatmap visualization, coloring every object in the scene depending on their distance to the pen tip, achieves best results and was preferred by study participants.
We release the ARPen system as an open-source toolbox, enabling researchers to implement and evaluate interaction techniques for Handheld AR with a mid-air pen. Our findings on essential interaction techniques provide a starting point for the development and evaluation of specialized application scenarios.
Es laden ein: die Dozentinnen und Dozenten der Informatik
