Computational models of argumentation are logic-based formalisms for knowledge representation that allow for the explicit modelling of automatic reasoning in terms of arguments, counterarguments, and their interplay. In this talk I give an overview on the core formalism in this area, abstract argumentation frameworks, and discuss its algorithmic issues.
News streams have several challenges for the past, present, and future of events. The past hides relations among events and actors; the present reflects needs of news readers; and the future waits to be predicted. The thesis has three parts regarding these time periods: We discover news chains using zigzagged search in the past, select front-page of current news for public, and predict future public reactions to events.
User experience includes all the users' emotions, beliefs, preferences, perceptions, physical and psychological responses, behaviors and accomplishments that occur before, during and after use of a system or service. In this talk, I will give a brief introduction to user experience concepts, common methods and practices. Furthermore, I will present the case of eye-controlled applications: several gaze interaction approaches introduces novel ideas which are 'useful', but are they 'usable'? In this regard, I would discuss how the current research ignores some important aspect of user experience, limiting eye tracking as a tool for general usage. GazeTheWeb will be discussed in brief with respect to user experience.
The aim of the research project GazeMining by WeST and EYEVIDO GmbH is to capture Web sessions semantically and thus obtain a complete picture of visual content, attention and interaction. This talk will give you insight to our first ideas, how to utilize both the structural and the visual data of a Web page to achieve the described goal. We propose a layer definition from the structural data, segmentation of each user session into visual states for each layer, combination of the states across the users and eventually the extraction of components on the Web page, like carousels or menus. The talk is concluded with an open discussion to gather feedback about the presented approach and to think about further challenges and use cases.
Over the last years, vector space embeddings have evolved to be the popular approach for including semantic information in neural-network-based machine learning architectures. Word embeddings map words to a latent continuous vector space with the goal to have similar words appear close together in the vector space. Knowledge graph embeddings map semantic triples into vector spaces with the goal to predict unseen triples from this representation. However, almost all developed embedding approaches are only capable of offline training, i.e., should new training data become available or old invalid, embeddings will have to be retrained from scratch.
Analysing human activities becomes one of the important research topics in recent years due to the fast and dramatic development of security issues in public spaces. In crowded environments, it is highly important to analyse the activities that arise from people's mobilities. In other words, the activities that can be expressed by the sequential positions of people (trajectories). The reason for this is the theory that these trajectories symbolise high-level interpretation of human activities. However, several factors make studying people's activities based on their mobilities a challenging task. Specifically, the vast diversity of possible activities that people may perform yield to a very complex recognition because of the high inter-class similarity.
Misinformation generates misperceptions, which have affected policies in many domains, including economy, health, environment, and foreign policy. Co-Inform is about empowering citizens, journalists, and policymakers with co-created socio-technical solutions, to increase resilience to misinformation, and to generate more informed behaviours and policies.
Compliance knowledge bases contain declarative business logic, meant to ensure that business processes are aligned towards company goals and regulations. Maintaining the quality of respective knowledge bases is widely recognized as a challenging task. A significant problem here is a potential inconsistency of knowledge bases, as this impedes the actual use of respective artifacts. Such inconsistencies can result from the incremental, often collaborative, creation of said knowledge bases. We investigate how quantitative measures can be used to assess the severity of inconsistency for individual elements of compliance knowledge bases.
Scientific paper recommendation is a task that aims to enhance the exploitation of Digital Libraries (DL) and helps researchers to find relevant papers from a large pool of papers. However, reliable sources to model the researcher interests must be provided to have accurate recommendations.
In my research project, I focused on the extraction of the user topical interests from papers that the user is connected with (authored or rated) and also by using the social structure of the academic network of the user (relations among researchers in the same domain).
Credit bureaus gather, aggregate and analyse information about consumers and business entities in order to assess credit related risks. On a methodological and technical level this involves the integration and quality assurance of data from various sources, the analysis of incoming data streams and the ability to train and apply predictive models. In this talk we will give an overview of challenging tasks related to use cases in the credit bureau industry and illustrate some modern approaches in the field of machine learning and data mining to address these tasks. All information about the talk available at https://www.uni-koblenz-landau.de/de/koblenz/fb4/ifi/Kolloquien/kolloquium_Gottron.