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Future engineers are increasingly confronted with the so-called Megatrends which are the big social challenges society has to cope with. These Megatrends, such as “Silver Society”, “Globalization”, “Mobility” and “Female Shift” require an application-oriented perspective on Diversity especially in the engineering field. Therefore, it is necessary to enable future engineers not only to look at the technical perspectives of a problem, but also to be able to see the related questions within societies they are developing their artefacts for. The aim of teaching engineering should be to prepare engineers for these requirements and to draw attention to the diverse needs in a globalized world.
Bringing together technical knowledge and social competences which go beyond a mere training of the so-called “soft skills”, is a new approach followed at RWTH Aachen University, one of the leading technical universities in Germany. RWTH Aachen University has established the bridging professorship “Gender and Diversity in Engineering” (GDI) which educates engineers with an interdisciplinary approach to expand engineering limits. In the frame of a sustainable teaching concept the research group under the leadership of Prof. Carmen Leicht-Scholten has developed an approach which imparts a supplication-specific Gender and Diversity expertise to engineers. In workshops students gain theoretical knowledge about Gender and Diversity and learn how to transfer their knowledge in their special field of study and later work. To substantiate this, the course participants have to solve case studies from real life. The cases which are developed in collaboration with non-profit organizations and enterprises from economy rise the students to challenges which are inspired by professional life. Evaluation shows the success of this approach as well as an increasing demand for such teaching formats.
Extrem hohe Blitzströme
(2017)
Messenger apps like WhatsApp or Telegram are an integral part of daily communication. Besides the various positive effects, those services extend the operating range of criminals. Open trading groups with many thousand participants emerged on Telegram. Law enforcement agencies monitor suspicious users in such chat rooms. This research shows that text analysis, based on natural language processing, facilitates this through a meaningful domain overview and detailed investigations. We crawled a corpus from such self-proclaimed black markets and annotated five attribute types products, money, payment methods, user names, and locations. Based on each message a user sends, we extract and group these attributes to build profiles. Then, we build features to cluster the profiles. Pretrained word vectors yield better unsupervised clustering results than current
state-of-the-art transformer models. The result is a semantically meaningful high-level overview of the user landscape of black market chatrooms. Additionally, the extracted structured information serves as a foundation for further data exploration, for example, the most active users or preferred payment methods.
In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions.
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.