134 research outputs found
ROPLEX : natürlichsprachliche Beschreibung von generischen Roboterplandaten
Die vorliegende Arbeit beschreibt eine Erklärungskomponente einer natürlichsprachlichen Schnittstelle, die in Robotersystemen für die Kommunikation mit Menschen eingesetzt werden kann. Die speziell dafür benötigten Bereiche des Roboterwissens bestehen einerseits aus Plänen, in denen Roboteranweisungen formuliert sind, und andererseits aus Diagnose- sowie Umweltinformationen. Die für dieses Dialogmodul entwickelten Methoden zur Erfassung und Weiterverarbeitung von Daten erlauben einen vielseitigen Einsatz. Die textuellen Erklärungen von Roboter-Anweisungsplänen, Lage von Umweltobjekten und auch von Fehlern können in verschiedenen natürlichen Sprachen erzeugt werden. Zusätzlich ist eine natürlichsprachliche Konfigurationsschnittstelle erstellt worden, die es dem Menschen erlaubt, den Inhalt der Erklärungen mittels geschriebener und gesprochener Sprache zu manipulieren. Bei der Realisierung dieser Dialogeinheit lag der Schwerpunkt auf der Entwicklung eines flexiblen Analyse- und Auswertungsverfahrens für die vom Roboter bereitgestellten Informationen und in der Konzeption einer sprachunabhängigen Zwischenrepräsentation
Fine-grained Affective Processing Capabilities Emerging from Large Language Models
Large language models, in particular generative pre-trained transformers
(GPTs), show impressive results on a wide variety of language-related tasks. In
this paper, we explore ChatGPT's zero-shot ability to perform affective
computing tasks using prompting alone. We show that ChatGPT a) performs
meaningful sentiment analysis in the Valence, Arousal and Dominance dimensions,
b) has meaningful emotion representations in terms of emotion categories and
these affective dimensions, and c) can perform basic appraisal-based emotion
elicitation of situations based on a prompt-based computational implementation
of the OCC appraisal model. These findings are highly relevant: First, they
show that the ability to solve complex affect processing tasks emerges from
language-based token prediction trained on extensive data sets. Second, they
show the potential of large language models for simulating, processing and
analyzing human emotions, which has important implications for various
applications such as sentiment analysis, socially interactive agents, and
social robotics
Towards social embodied cobots: The integration of an industrial cobot with a social virtual agent
The integration of the physical capabilities of an industrial collaborative
robot with a social virtual character may represent a viable solution to
enhance the workers' perception of the system as an embodied social entity and
increase social engagement and well-being at the workplace. An online study was
setup using prerecorded video interactions in order to pilot potential
advantages of different embodied configurations of the cobot-avatar system in
terms of perceptions of Social Presence, cobot-avatar Unity and Social Role of
the system, and explore the relation of these. In particular, two different
configurations were explored and compared: the virtual character was displayed
either on a tablet strapped onto the base of the cobot or on a large TV screen
positioned at the back of the workcell. The results imply that participants
showed no clear preference based on the constructs, and both configurations
fulfill these basic criteria. In terms of the relations between the constructs,
there were strong correlations between perception of Social Presence, Unity and
Social Role (Collegiality). This gives a valuable insight into the role of
these constructs in the perception of cobots as embodied social entities, and
towards building cobots that support well-being at the workplace
MultiMediate '22: Backchannel Detection and Agreement Estimation in Group Interactions
Backchannels, i.e. short interjections of the listener, serve important
meta-conversational purposes like signifying attention or indicating agreement.
Despite their key role, automatic analysis of backchannels in group
interactions has been largely neglected so far. The MultiMediate challenge
addresses, for the first time, the tasks of backchannel detection and agreement
estimation from backchannels in group conversations. This paper describes the
MultiMediate challenge and presents a novel set of annotations consisting of
7234 backchannel instances for the MPIIGroupInteraction dataset. Each
backchannel was additionally annotated with the extent by which it expresses
agreement towards the current speaker. In addition to a an analysis of the
collected annotations, we present baseline results for both challenge tasks.Comment: ACM Multimedia 202
Monitoring of lung edema by microwave reflectometry during lung ischemia-reperfusion injury in vivo
It is still unclear whether lung edema can be monitored by microwave reflectometry and whether the measured changes in lung dry matter content (DMC) are accompanied by changes in PaO(2) and in pro-to anti-inflammatory cytokine expression (IFN-gamma and IL-10). Right rat lung hili were cross-clamped at 37 degrees C for 0, 60, 90 or 120 min ischemia followed by 120 min reperfusion. After 90 min (DMC: 15.9 +/- 1.4%; PaO(2): 76.7 +/- 18 mm Hg) and 120 min ischemia (DMC: 12.8 +/- 0.6%; PaO(2): 43 +/- 7 mm Hg), a significant decrease in DMC and PaO(2) throughout reperfusion compared to 0 min ischemia (DMC: 19.5 +/- 1.11%; PaO(2): 247 +/- 33 mm Hg; p < 0.05) was observed. DMC and PaO(2) decreased after 60 min ischemia but recovered during reperfusion (DMC: 18.5 +/- 2.4%; PaO(2) : 173 +/- 30 mm Hg). DMC values reflected changes on the physiological and molecular level. In conclusion, lung edema monitoring by microwave reflectometry might become a tool for the thoracic surgeon. Copyright (c) 2006 S. Karger AG, Basel
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