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Zeit: Montag, 28. November 2022, 14:30 Uhr
Ort: Raum 2222, Ahornstr. 55
Referent: Christian Cherek M.Sc.
Lehrstuhl Informatik 10
Thema: The Impact of Tangible Interaction Techniques on Higher Cognitive Processes
Abstract:
Multitouch interaction brought incredible advancements to our everyday life.
The success of smartphones is unprecedented in modern history for a good reason.
On multitouch displays, input and output are collocated at the tip of our fingers.
This enables immediate feedback, highly flexible utilization of the available space,
updatability of interfaces, and new accessibility features. However, a touchscreen's
flat surface lacks haptic features, neglecting a big part of our sensory capabilities.
This thesis integrates itself into the tangible research community by presenting novel
ways to create tangibles for capacitive screens and presenting a software framework to
develop tangible applications with Apple's native APIs. We developed the Design Space
of Tangible Interaction, a taxonomy to help researchers and designers comparing tangible
designs and finding new ways to interact with tangibles. In this spirit, we evaluated
tangibles in novel ways beyond their well-established usability benefits. We found them
to contribute to users' way of thinking, awareness for collaborators, and intuitiveness
of highly complex input tasks.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Zeit: Donnerstag, 24. November 2022, 10:00 Uhr
Zoom:
https://rwth.zoom.us/j/93616983610?pwd=UnkwdWd4azRSNzVVUEt4WW1FdHNJUT09
Meeting-ID: 936 1698 3610
Kenncode: 382088
Referentin: Parnia Bahar, M.Sc.
Thema: Neural Sequence-to-Sequence Modeling for Language and Speech
Translation
Abstract:
In recent years, various fields in human language technology have been
advanced by the success of neural sequence-to-sequence modeling. The
application of attention models to automatic speech recognition, text,
and speech machine translation has become dominant and well-established.
Although the effectiveness of such models has been documented in
scientific papers, not all aspects of attention sequence-to-sequence
models have been explored. Therefore, the main contribution of this
thesis centers around redesigning attention models by proposing novel
alternative architectures.
From a modeling perspective, this research goes beyond current
sequence-to-sequence backbone models to directly incorporate input and
output sequences in a two-dimensional structure where an attention
mechanism is no longer required. This model distinguishes itself from
attention models in which inputs and outputs are treated as
one-dimensional sequences over time.
Current state-of-the-art attention models also lack an explicit
alignment, a core component of traditional systems. Such a gross
simplification of a complex process complicates the extraction of
alignments between input and output positions. To enable the
explainability of attention models and more controllable output, the
next part of this study integrates the attention model into the hidden
Markov model formulation by introducing alignments as a sequence of
hidden variables.
Finally, an exciting research direction is combining speech recognition
with text machine translation for speech-to-text translation. Besides
advancing a cascade of independently trained speech recognition and
machine translation systems, this thesis sheds light on different
end-to-end models to directly translate speech into a target text and
shows that such end-to-end models can practically translate speech
utterances as a substitute solution to cascaded speech translation.
Es laden ein: die Dozentinnen und Dozenten der Informatik
--
Stephanie Jansen
Faculty of Mathematics, Computer Science and Natural Sciences
HLTPR - Human Language Technology and Pattern Recognition
RWTH Aachen University
Theaterstraße 35-39
D-52062 Aachen
Tel: +49 241 80-21601
sek(a)hltpr.rwth-aachen.de
www.hltpr.rwth-aachen.de
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Zeit: Montag, 21.11.2022, 14:00-15:00 Uhr
Der öffentliche Vortrag findet hybrid statt:
Raum: Raum 5053.2 (B-IT-Hörsaal)/Informatikzentrum, Ahornstraße 55
Zoom: https://rwth.zoom.us/j/96565981989?pwd=b3B3aEVmSnJ1VFJhUDYwSlorbTcvQT09
Meeting-ID: 965 6598 1989
Kenncode: 346594
Referent: Herr Daxin Liu, M. Sc.
LuFG Informatik 5
Thema: Projection in a Probabilistic Epistemic Logic and Its Application to Belief-based-Program Verification
Abstract:
Rich representation of knowledge and actions has been a goal that many AI researchers pursue. Among all proposals, perhaps, the situation calculus by Reiter is the most widely studied, where actions are treated as logical terms and the agent's knowledge is represented by logical formulas. The language has been extended to incorporate many features like time, concurrency, procedures, etc..
Most recently, Belle and Lakemeyer proposed a modal logic DS which deals with degrees of belief and noisy sensing. The logic has many appealing properties like full introspection, however, it also has some shortcomings. Perhaps the main one is the lack of expressiveness when it comes to degrees of belief. Currently, the language allows expressing degrees of belief only as constants making it impossible to express belief distribution. Another important problem is that it lacks projection reasoning mechanisms. Projection is the task to determine whether a query about the future is entailed by an initial knowledge base. Two solutions of projection exist regression and progression.
While regression transfers the query about the future into a query about the initial state and evaluates it there, progression transfers the whole initial knowledge base into a future one.
In this thesis, we first lift the expressiveness of the logic DS by modifying both the syntax and semantics. Moreover, we investigate the projection problem in DS.
In particular, we propose a regression operator which can handle queries with nested beliefs and beliefs with quantifying-in. For progression, we show that classical progression is first-order definable for a fragment of the logic and provide our solution for the progression of belief in terms of only-believing after actions.
Moreover, we exploit how to apply the proposed methods in a more practical scenario: on the verification of belief programs, a probabilistic extension of Golog programs, where every action and sensing could be noisy and every test refers to the agent's subjective beliefs. We show that the verification problem is undecidable even in very restrictive settings. We also show a special case where the problem is decidable.
Es laden ein: die Dozentinnen und Dozenten der Informatik
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Leany Maaßen
RWTH Aachen University
Lehrstuhl Informatik 5, LuFG Informatik 5
Prof. Dr. Stefan Decker, Prof. Dr. Matthias Jarke,
Prof. Gerhard Lakemeyer Ph.D., JunProf. Dr. Sandra Geisler
Ahornstrasse 55
D-52074 Aachen
Tel: 0241-80-21509
Fax: 0241-80-22321
E-Mail: maassen(a)dbis.rwth-aachen.de