I see what you did there: understanding when to trust a ML model with NOVA

In this demo paper we present NOVA, a machine learning and explanation interface that focuses on the automated analysis of social interactions. NOVA combines Cooperative Machine Learning (CML) and explainable AI (XAI) methods to reduce manual labelling efforts while simultaneously generating an intuitive understanding of the learning process of a classification system. Therefore, NOVA features a semi-automated labelling process in which users are provided with immediate visual feedback on the predictions, which gives insights into the strengths and weaknesses of the underlying classification system. Following an interactive and exploratory workflow, the performance of the model can be improved by manual revision of the predictions

Cognitive architecture of multimodal multidimensional dialogue management

Numerous studies show that participants of real-life dialogues happen to get involved in rather dynamic non-sequential interactions. This challenges the dialogue system designs based on a reactive interlocutor paradigm and calls for dialog systems that can be characterised as a proactive learner, accomplished multitasking planner and adaptive decision maker. Addressing this call, the thesis brings innovative integration of cognitive models into the human-computer dialogue systems. This work utilises recent advances in Instance-Based Learning of Theory of Mind skills and the established Cognitive Task Analysis and ACT-R models. Cognitive Task Agents, producing detailed simulation of human learning, prediction, adaption and decision making, are integrated in the multi-agent Dialogue Man-ager. The manager operates on the multidimensional information state enriched with representations based on domain- and modality-specific semantics and performs context-driven dialogue acts interpretation and generation. The flexible technical framework for modular distributed dialogue system integration is designed and tested. The implemented multitasking Interactive Cognitive Tutor is evaluated as showing human-like proactive and adaptive behaviour in setting goals, choosing appropriate strategies and monitoring processes across contexts, and encouraging the user exhibit similar metacognitive competences

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research