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Found 12 papers, 1 papers with code
True or False? Detecting False Information on Social Media Using Graph Neural Networks
In recent years, false information such as fake news, rumors and conspiracy theories on many relevant issues in society have proliferated.
Towards detecting unanticipated bias in Large Language Models
Through this research, we aim to contribute to the development of fairer and more transparent AI systems.
More than words: Advancements and challenges in speech recognition for singing
This paper addresses the challenges and advancements in speech recognition for singing, a domain distinctly different from standard speech recognition.
Towards Large-scale Building Attribute Mapping using Crowdsourced Images: Scene Text Recognition on Flickr and Problems to be Solved
This work addresses the challenges in applying Scene Text Recognition (STR) in crowdsourced street-view images for building attribute mapping.
Geo-Information Harvesting from Social Media Data
In this article, we address key aspects in the field, including data availability, analysis-ready data preparation and data management, geo-information extraction from social media text messages and images, and the fusion of social media and remote sensing data.
A Survey of Uncertainty in Deep Neural Networks
Different examples from the wide spectrum of challenges in different fields give an idea of the needs and challenges regarding uncertainties in practical applications.
Out-of-distribution detection in satellite image classification
In satellite image analysis, distributional mismatch between the training and test data may arise due to several reasons, including unseen classes in the test data and differences in the geographic area.
Cross-language sentiment analysis of European Twitter messages duringthe COVID-19 pandemic
We separate the results by country of origin, and correlate their temporal development with events in those countries.
A simple method for domain adaptation of sentence embeddings
We demonstrate how to use this approach for a variety of data sets and present results on different data sets representing similar problems.
Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic
In this paper, we analyze Twitter messages (tweets) collected during the first months of the COVID-19 pandemic in Europe with regard to their sentiment.
Few-shot tweet detection in emerging disaster events
Social media sources can provide crucial information in crisis situations, but discovering relevant messages is not trivial.
One-Way Prototypical Networks
Trained on the commonly used Omniglot data set, we obtain a classification accuracy of . 98 on the matched test set, and of . 8 on unmatched MNIST data.
