Research Institutes

https://www.hannovermesse.de/en/

Abstract
At the beginning of the pandemic contact tracing apps proliferated as a potential solution to scaling infection tracking and response. While significant focus was put on developing privacy protocols for these apps, relatively less attention was given to understanding why, and why not, users might adopt them. Yet, for these technological solutions to benefit public health, users must be willing to adopt these apps. In this talk I showcase the value of taking a descriptive ethics approach to setting best practices in this new domain. Descriptive ethics, introduced by the field of moral philosophy, determines best practices by learning directly from the user -- observing people’s preferences and inferring best practice from that behavior -- instead of exclusively relying on experts' normative decisions. This talk presents an empirically-validated framework of user's decision inputs to adopt COVID19 contact tracing apps, including app accuracy, privacy, benefits, and mobile costs. Using predictive models of users' likelihood to install COVID apps based on quantifications of these factors, I show how high the bar is for achieving adoption. I conclude by discussing a large-scale field study in which we put our survey and experimental results into practice to help the U.S. state of Louisiana advertise their COVID app and found differential results regarding the effect of privacy transparency when prosocial vs. individualist appeals were used to encourage app adoption.

http://domino.mpi-inf.mpg.de/internet/events.nsf/0/F7DC84935733F248C12588440024305D

https://www.hannovermesse.de/en/

Abstract:

Language corpora have been crucial for the study of language usage and variation. Similarly, translation corpora have informed us about the specific language variety of translations, variously called translationese, the third code, interlanguage, etc. However, corpora of interpreting have comparatively contributed fairly little to the study of spoken varieties. I will review attempts that have been made and suggest some new avenues of research into the remote corners of the universe of constrained language.

If you would like to meet the speaker, please contact Heike Przybyl: heike.przybyl@uni-saarland.de

The talk will be held in hybrid form in Conference Room 2.02, Building A2.2 & via Teams: Microsoft Teams-Besprechung
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+49 69 677765673,,957606757# Germany, Frankfurt am Main
Telefonkonferenz-ID: 957 606 757#
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https://explainable-intelligent.systems/lectures/

Abstract:
Social media communities like Reddit and Twitter allow users to express
their views on topics of their interest, and to engage with other users
who may share or oppose these views. This can lead to productive
discussions towards a consensus, or to contended debates, where
disagreements frequently arise. Prior work on such settings has
primarily focused on identifying notable instances of antisocial
behavior such as hate-speech and "trolling", which represent possible
threats to the health of a community. These, however, are exceptionally
severe phenomena, and do not encompass controversies stemming from user
debates, differences of opinions, and off-topic content, all of which
can naturally come up in a discussion without going so far as to
compromise its development.
This dissertation proposes a framework for the systematic analysis of
social media discussions that take place in the presence of
controversial themes, disagreements, and mixed opinions from
participating users. We start by building a feature model to
characterize adversarial discussions surrounding political campaigns on
Twitter, with a focus on the factual and sentimental nature of their
topics and the role played by different users involved. We then extend
our approach to Reddit discussions, leveraging community feedback
signals to define a new notion of controversy and to highlight
conversational archetypes that arise from frequent and interesting
interaction patterns. We use our feature model to build logistic
regression classifiers that can predict future instances of controversy
in Reddit communities centered on politics, world news, sports, and
personal relationships. Finally, our model also provides the basis for a
comparison of different communities in the health domain, where topics
and activity vary considerably despite their shared overall focus.

http://domino.mpi-inf.mpg.de/internet/events.nsf/0/E0A7A43223166D82C1258854003C5175

Abstract:
In this talk you will learn about distributed systems, with a particular focus on graph problems and fault tolerance.

Fault-tolerance in a microprocessor or even System-on-Chip can be improved by using a fault-tolerant pulse propagation design. The existing design TRIX achieves this goal by being a distributed system consisting of very simple nodes. We show that even in the typical mode of operation without faults, TRIX performs significantly better than a regular wire or clock tree: Statistical evaluation of our simulated experiments show that we achieve a skew with standard deviation of O(log log H), where H is the height of the TRIX grid.

http://domino.mpi-inf.mpg.de/internet/events.nsf/0/535BF8690FE25F72C125883D004305D6

The distance-r generalization of classic graph problems can give us insights on how distance affects hardness of a problem. For the distance-r dominating set problem, we present both an algorithmic upper and unconditional lower bound for any graph class with certain high-girth and sparseness criteria. In particular, our algorithm achieves a O(r · f(r))-approximation in time O(r), where f is the expansion function, which correlates with density. For constant r, this implies a constant approximation factor, in constant time. We also show that no algorithm can achieve a (2r + 1 − δ)-approximation for any δ > 0 in time O(r), not even on the class of cycles of girth at least 5r. Furthermore, we extend the algorithm to related graph cover problems and even to a different execution model.

Furthermore, we investigate the problem of packet forwarding, which addresses the question of how and when best to forward packets in a distributed system. These packets are injected by an adversary. We build on the existing algorithm OED to handle more than a single destination. In particular, we show that buffers of size O(log n) are sufficient for this algorithm, in contrast to O(n) for the naive approach.

https://infolab.cs.uni-saarland.de/2022/04/19/studycheck-lehramt-informatik/

The interpretation of many sentences depends on context. This is particularly true for pragmatic interpretations, that is, interpretations that go beyond the literal meaning of sentences. For example, consider the sentence „The dog looks awfully happy.“ Out of context, its interpretation primarily concerns some dog’s emotional state. However, if this sentence is given as an answer to „What happened to the turkey?“, listeners will additionally infer that the dog likely ate the turkey. In this talk, I will present three studies that investigate to what extent recently proposed contextual language models are able to take context into account when drawing different pragmatic inferences. I will focus on context-dependent scalar inferences (inferring that „some“ sometimes, but not always, means „some but not all“), presuppositions, and tracking of discourse entities, and explain to what extent models like BERT, DeBERTa, and GPT-3 can interpret context-sensitive sentences.

If you would like to meet the speaker, please contact Vera Demberg: vera@coli.uni-saarland.de

Location: The talk will be held in hybrid form in Conference Room 2.02, Building A2.2 & via Teams:
________________________________________________________________________________
Microsoft Teams-Besprechung
Nehmen Sie von Ihrem Computer oder der mobilen App aus teil
Klicken Sie hier, um an der Besprechung teilzunehmen
Oder anrufen (nur Audio)
+49 69 677765673,,823276136# Germany, Frankfurt am Main
Telefonkonferenz-ID: 823 276 136#
Lokale Nummer suchen | PIN zurücksetzen
Weitere Infos | Besprechungsoptionen
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