Ongoing Micro Projects Sample Clauses
Ongoing Micro Projects. (Just Started or About to Start)
1. Combining symbolic and sub-symbolic approaches - Improving neural Question- Answering-Systems through Document Analysis for enhanced accuracy and efficiency in Human-AI interaction. Knowledge discovery offers numerous challenges and opportunities. In the last decade, a significant number of applications have emerged relying on evidence from the scientific literature. ΑΙ methods offer innovative ways of applying knowledge discovery methods in the scientific literature facilitating automated reasoning, discovery and decision making on data. This micro-project will focus on the task of question answering (QA) for the biomedical domain. Our starting point is a neural QA engine developed by ILSP addressing experts’ natural language questions by jointly applying document retrieval and snippet extraction on a large collection of PUBMED articles, thus, facilitating medical experts in their work. DFKI will augment this system with a knowledge graph integrating the output of document analysis and segmentation modules. The knowledge graph will be incorporated in the QA system and used for exact answers and more efficient Human-AI interactions. We will primarily focus upon scientific articles on Covid-19 and SARS-CoV-2. • Paper(s) in a conference or/and journal • Demonstrator
2. Memory Aware Conversational AI to aid virtual Team-Meetings In this micro-project, we propose investigating human recollection of team meetings and how conversational AI could use this information to create better team cohesion in virtual settings. Specifically, we would like to investigate how a person's emotion, personality, relationship to fellow teammates, goal and position in the meeting influences how they remember the meeting. We want to use this information to create memory aware conversational AI that could leverage such data to increase team cohesion in future meetings. To achieve this goal, we plan first to record a multi-modal dataset of team meetings in a virtual setting. Second, administrate questionnaires to participants in different time intervals succeeding a session. Third, annotate the corpus. Fourth, carry out an initial corpus analysis to inform the design of memory-aware conversational AI. This micro-project will contribute to a longer-term effort in building a computational memory model for human-agent interaction. • A corpus of repeated virtual team meetings (6 sessions spaced, 1 week each) • manual annotations (people's recollection ...
Ongoing Micro Projects. (Just Started or About to Start)
1. X5LEARN: Cross Modal, Cross Cultural, Cross Lingual, Cross Domain, and Cross Site interface for access to openly licensed educational materials
Ongoing Micro Projects. (Just Started or About to Start)
1. Learning Individual Users’ Strategies for Adaptive UIs Adapting user interfaces (UIs) requires taking into account both positive and negative effects that changes may have on the user. A carelessly picked adaptation may impose high costs -- for example, due to surprise or relearning effort. It is essential to consider differences between users as the effect of an adaptation depends on the user's strategies, e.g., how each user searches for information in a UI. This microproject extends an earlier collaboration between partners on model-based reinforcement learning for adaptive UIs by developing methods to account for individual differences. Here, we first develop computational models to explain and predict users' visual search and pointing strategies when searching within a UI. We apply this model to infer user strategies based on interaction history and adapt UIs accordingly. The outcomes of this project will be (1) a publication at the ACM CHI conference and (2) integration in our platform for adaptive UIs. • Model of visual search and pointing in menus. The code will be available on GitHub • The integration of the model in our platform for adaptive UI. The code will be available on GitHub • A demo of the system will be available online • A publication at the conference ACM CHI
2. The knowledgeable and empathic behavior change coach We will develop a conceptual model of key components relating to supporting healthy behavior change. The model will provide a top-level representation of the clinical (from the psychological perspective) enablers and barriers that can be exploited for developing fine- grained models supporting the realization of behavior change paths within and across specific domains. The resulting ontology will form the basis for generating user models (Theory of Mind), developing reasoning and decision-making strategies for managing conflicting values and motives, which can be used in collaborative and persuasive dialogues with the user. Such knowledge is also fundamental for embedding empathic behavior as well as non-verbal behaviors which can be embodied by a virtual character in the role of a coach. Learning methods can be applied to explore trajectories of behavior change. The produced ontology will represent a valuable resource for the healthcare domain thanks to the knowledge included into the provided resource.