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Machine learning models built on training data with multiple modalities can reveal new insights that are not accessible through unimodal datasets. For example, cardiac magnetic resonance images (MRIs) and electrocardiograms (ECGs) are both known to capture useful information about subjects' cardiovascular health status. A multimodal machine learning model trained from large datasets can potentially predict the onset of heart-related diseases and provide novel medical insights about the cardiovascular system. Despite the potential benefits, it is difficult for medical experts to explore multimodal representation models without visual aids and to test the predictive performance of the models on various subpopulations. To address the challenges, we developed a visual analytics system called Latent Space Explorer. Latent Space Explorer provides interactive visualizations that enable users to explore the multimodal representation of subjects, define subgroups of interest, interactively decode data with different modalities with the selected subjects, and inspect the accuracy of the embedding in downstream prediction tasks. A user study was conducted with medical experts and their feedback provided useful insights into how Latent Space Explorer can help their analysis and possible new direction for further development in the medical domain., Comment: 7 pages, 5 figures

Human-Robot Interaction (HRI) becomes more and more important in a world where robots integrate fast in all aspects of our lives but HRI applications depend massively on the utilized robotic system as well as the deployment environment and cultural differences. Because of these variable dependencies it is often not feasible to use a data-driven approach to train a model for human intent recognition. Expert systems have been proven to close this gap very efficiently. Furthermore, it is important to support understandability in HRI systems to establish trust in the system. To address the above-mentioned challenges in HRI we present an adaptable python library in which current state-of-the-art Models for context recognition can be integrated. For Context-Based Intention Recognition a two-layer Bayesian Network (BN) is used. The bayesian approach offers explainability and clarity in the creation of scenarios and is easily extendable with more modalities. Additionally, it can be used as an expert system if no data is available but can as well be fine-tuned when data becomes available., Comment: 7 Pages, 3 Figures, to be published in proceedings of IEEE RO-MAN Conference

Disease risk models can identify high-risk patients and help clinicians provide more personalized care. However, risk models developed on one dataset may not generalize across diverse subpopulations of patients in different datasets and may have unexpected performance. It is challenging for clinical researchers to inspect risk models across different subgroups without any tools. Therefore, we developed an interactive visualization system called RMExplorer (Risk Model Explorer) to enable interactive risk model assessment. Specifically, the system allows users to define subgroups of patients by selecting clinical, demographic, or other characteristics, to explore the performance and fairness of risk models on the subgroups, and to understand the feature contributions to risk scores. To demonstrate the usefulness of the tool, we conduct a case study, where we use RMExplorer to explore three atrial fibrillation risk models by applying them to the UK Biobank dataset of 445,329 individuals. RMExplorer can help researchers to evaluate the performance and biases of risk models on subpopulations of interest in their data., Comment: IEEE VIS 2022 Short

Robots are becoming everyday devices, increasing their interaction with humans. To make human-machine interaction more natural, cognitive features like Visual Voice Activity Detection (VVAD), which can detect whether a person is speaking or not, given visual input of a camera, need to be implemented. Neural networks are state of the art for tasks in Image Processing, Time Series Prediction, Natural Language Processing and other domains. Those Networks require large quantities of labeled data. Currently there are not many datasets for the task of VVAD. In this work we created a large scale dataset called the VVAD-LRS3 dataset, derived by automatic annotations from the LRS3 dataset. The VVAD-LRS3 dataset contains over 44K samples, over three times the next competitive dataset (WildVVAD). We evaluate different baselines on four kinds of features: facial and lip images, and facial and lip landmark features. With a Convolutional Neural Network Long Short Term Memory (CNN LSTM) on facial images an accuracy of 92% was reached on the test set. A study with humans showed that they reach an accuracy of 87.93% on the test set.

With the advent of mobile games and the according growing and competitive market, game user research can provide valuable insights and a competitive edge if methods and procedures are employed that match the distinct challenges that mobile devices, games and usage scenarios induce. We present a summary of parameters that frame the research setup and procedure, focusing on the trade-offs between lab and field studies and the related decision whether to pursue large-scale and quantitative or small-scale focused research accompanied by qualitative methods. We then illustrate the implications of these considerations on real world projects along the lines of two evaluations of different input methods for the action-puzzle mobile game Somyeol: a local study with 37 participants and a mixed design of qualitative and quantitative methods, and the strictly quantitative analysis of game-play data from 117,118 users. The findings underline the importance of small-scale evaluations prior to release., Comment: CHI Conference on Human Factors in Computing Systems 2013, Workshop Paper, 5 pages

Data tables in the form of spreadsheets or delimited text files are the most utilised data format in Systems Biology. However, they are often not sufficiently structured and lack clear naming conventions that would be required for modelling. We propose the SBtab format as an attempt to establish an easy-to-use table format that is both flexible and clearly structured. It comprises defined table types for different kinds of data; syntax rules for usage of names, shortnames, and database identifiers used for annotation; and standardised formulae for reaction stoichiometries. Predefined table types can be used to define biochemical network models and the biochemical constants therein. The user can also define own table types, adjusting SBtab to other types of data. Software code, tools, and further information can be found at www.sbtab.net.

semanticSBML 2.0 is an online collection of services for the work with biochemical network models in SBML format., Comment: in Adrian Paschke, Albert Burger, Andrea Splendiani, M. Scott Marshall, Paolo Romano: Proceedings of the 3rd International Workshop on Semantic Web Applications and Tools for the Life Sciences, Berlin,Germany, December 8-10, 2010