DATEXIS at the  Beuth University is part of the following internationally recognized initiatives: 

PLASS: Platform for Analytical Supply Chain Management Services (2019-2022)

The goal of the PLASS project is the development of a prototypical B2B platform for AI-based decision support for supply chain management. The focus is on the automatic recognition of decision-relevant information and the acquisition of structured knowledge from global and multilingual text sources. These sources provide a large database of SCM information, particularly for the early detection of critical events and risks, but also of opportunities, e.g. through new technologies, suppliers and supply chains. PLASS enables SMBs and large enterprises to continuously monitor suppliers and supply chains, and supports supply chain managers in risk assessment and decision making. One approach is the integration of knowledge graphs, another approach is through feedback channels to continually improve and adapt the AI ​​models.

Partners: DFKI Berlin, Siemens, ubermetrics, Uni Leipzig, Beuth-Hochschule. Funding within the programe "Smart Data Economy", BMWi.

 

NOHATE: Overcoming crises in public communication about refugees, migration, foreigners (2018-2020)

More and more frequently, discussions turn to so-called Hate Speech – offensive and hateful posts by individual users – when divisive issues come up in social media or online commentary sections. Without sufficient moderation, this hate speech can quickly lead to an escalation or inhibition of discussions. Therefore, operators of such platforms are advised to identify and moderate this hateful communication. However, due to the large amounts of data and rapidity of communication, this proves elusive. Especially the discussion about the accommodation of refugees since 2015 strengthened the assumption that hate speech in social media does not only pose a threat for individuals but also for society as a whole since hateful communication can be linked to the political advancement of right-wing extremist parties, political apathy, and racist crimes.

The three-year joint project NOHATE aims to analyse hateful communication on social media platforms, in online forums and commentary sections in order to identify underlying causes and dynamics as well as develop methods and software for (early) recognition of hateful communication and potential strategies for de-escalation. A case study will offer a multidimensional perspective on displacement and migration and provide data for software development.

The partners in the joint project are Freie Universität BerlinBeuth University of Applied Sciences Berlin and VICO Research & Consulting. The project is funded by the Federal Ministry of Education and Research (BMBF) within the context of the funding initiative "Strengthening solidarity in times of crises and change".

You can find further information about the project here.

H2020: FashionBrain - Understanding Europe's Fashion Data Universe (2017-2019)

The primary goal of each retailer is to “understand your customers”. Our interviews with retailers show a primary demand from the retail industry for predicting a customer's next demand. Surprisingly , even a complete record of past purchases (and returns) is not sufficient to understand how items in a company's catalog do or do not connect with the customer's general tastes, lifestyle and aspirations. Moverover, from a business perspective, any efficiency gains in the logistics of supplier management, shipping and handling are rather minor, compared to the gains one could obtain from a better understanding of the customers’ personalities and habits. Given that the customer demands trigger proactive stocking and fashion production, this appears as a logical consequence.

In this project, we want to consolidate and extend existing European technologies in the area of database management, data mining, machine learning, image processing, information retrieval, and crowdsourcing to strengthen the positions of European fashion retailers among their world-wide competitors. Our choice for the fashion sector is a concise one: i) as a multi-billion euro industry, the fashion sector is extremely important for the European economy; ii) Europe already has a solid position in the world fashion stage, however, to maintain its position and keep up with the competitors, European fashion industry needs the help of advanced technology; and iii) European fashion industry provides an excellent exercise for new technologies, because it is a multi-sectorial by itself (i.e., imposes challenging data integration issues), it has a short life-cycle (i.e., requires timely reaction to the current events) and it involves diverse languages and cultures.

The main outcome of the FashionBrain project is the improvement of the fashion industry value chain obtained thanks to the creation of novel on-line shopping experiences, the detection of influencers, and the prediction of upcoming fashion trends. Tangible outcomes will include software, demonstrators, and novel algorithms for a data-driven fashion industry.

See more at CORDIS or at the FashionBrain Homepage