Damian Borth
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Prof. Dr.
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Borth
First name
Damian
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damian.borth@unisg.ch
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+41 71 224 26 27
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https://twitter.com/damianborth
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Deep Learning für die Wirtschaftsprüfung - Eine Darstellung von Theorie, Funktionsweise und Anwendungsmöglichkeiten

2021-07-28 , Gierbl, Anita Stefanie , Schreyer, Marco , Borth, Damian , Leibfried, Peter

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Intellectual property protection in the age of self-learning systems: Appropriability issues in artificial intelligence

2020-03-27 , Häfner, Naomi , Gassmann, Oliver , Borth, Damian

This study examines how firms in the autonomous driving industry that pursue artificial intelligence-based innovations attempt to appropriate returns from these innovations. It intends to contribute to the literature on value appropriation from innovation by investigating the extent to which firms can and do keep the key components of AI systems (data set, training approach, and model) private versus publishing them. Using a qualitative research design, we establish that there are regulatory, technical, and enforcement aspects to the components that prompt firms to either protect or publish.

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Künstliche Intelligenz in der Wirtschaftsprüfung - Identifikation ungewöhnlicher Buchungen in der Finanzbuchhaltung

2018-11-01 , Schreyer, Marco , Sattarov, Timur , Borth, Damian , Dengel, Andreas , Reimer, Bernd

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Large-Scale Social Multimedia Analysis

2019-04 , Benjamin, Bischke , Damian, Borth , Andreas, Dengel , Stefanos, Vrochidis , Benoit, Huet , Edward Y., Chang , Ioannis, Kompatsiaris

The Internet is abundant with opinions, sentiments, and reflections of the society about products, brands, and institutions hidden under tons of irrelevant and unstructured data. This work addresses the contextual augmentation of events in social media streams in order to fully leverage the knowledge present in social multimedia by making three major contributions. First, a global study of the Twitter Firehose is presented. To our knowledge this is the first study of this kind and comprehension providing valuable insights about variability of tweets with respect to multimedia content. The results for more than one billion tweets show the great potential of the stream for many application domains. As a second key contribution, a fully automated system was developed for the augmentation of social multimedia with contextual information on a large scale. The system trawls multimedia content from Twitter and performs a multi-modal analysis on it. The analysis considers temporal, visual, textual, geographical, and user-specific dimensions. Third, we present a near-duplicate detection approach based on deep learn- ing to detect the most frequent images being propagated through Twitter during events

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A Novel Dataset and Benchmark for Surface NO2 Prediction from Remote Sensing Data Including COVID Lockdown Measures

2021-07-16 , Scheibenreif, Linus Mathias , Mommert, Michael , Borth, Damian

NO2 is an atmospheric trace gas that contributes to global warming as a precursor of greenhouse gases and has adverse effects on human health. Surface NO2 concentrations are commonly measured through strictly localized networks of air quality stations on the ground. This work presents a novel dataset of surface NO2 measurements aligned with atmospheric column densities from Sentinel-5P, as well as geographic and meteorological variables and lockdown information. The dataset provides access to data from a variety of sources through a common format and will foster data-driven research into the causes and effects of NO2 pollution. We showcase the value of the new dataset on the task of surface NO2 estimation with gradient boosting. The resulting models enable daily estimates and confident identification of EU NO2 exposure limit breaches. Additionally, we investigate the influence of COVID-19 lockdowns on air quality in Europe and find a significant decrease in NO2 levels.

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