Matching the right people!
Creating a functional prototype for online matching of people and expertise for innovation. This project built on the results of the "Asking the right questions" project.
Research Bundles
"Research bundles" give you a space in the AI on-demand platform where you can collect and publish the outputs of a small research project in a compact way. A research bundle collects in a single place all the assets (code, data, tutorials, examples, ...) produced by your project and published on the AI on-demand platform. Of course, you can also put links to assets published elsewhere, like Github or Zenodo.
Memory Aware Conversational AI to aid virtual Team-Meetings
We propose investigating human recollection of team meetings and how conversational AI could use this information to create better team cohesion in virtual settings
Multilingual Event-Type-Anchored Ontology for Natural Language Understanding (META-O-NLU)
Many industrial NLP applications emphasise the processing and detection of nouns, especially proper nouns (Named Entity Recognition, NER). However, processing of verbs has been neglected. This project addresses that gap.
Multilingual SynSemClass for the Semantic Web (acronym: MSSW)
Conversion of the SynSemClass event type ontology to the linguistic linked open data (LLOD) cloud.
Multimodal Perception and Interaction with Transformers
This micro-project surveys tools and data sets for experiments for demonstrating the potential use of transformers for multimodal perception and multimodal interactions.
Neural Mechanism in Human Brain Activity During Weight lifting
In this project, we investigated the changing pattern in EEG and analyzed the different features in EEG data during the lifting of different weights.
Neural-Symbolic Integration: explainability and reasoning in KENN
The project combined the insights gained on KENN
Normative behavior and extremism in facebook groups
The project delivered detailed analyses of the tweets around the USA elections and subsequent riots.
Online Deep-AUTOML
We studied online optimization methods for hyper-parameter tuning.
Online reinforcement learning for human-robot interactive learning
Coping with the variability of human feedback during interactive learning through ensemble reinforcement learning