Skilled employees are the most important pillars of an organization. Despite this, most organizations face high attrition and turnover rates. While several machine learning models have been developed to analyze attrition and its causal factors, the interpretations of those models remain opaque. In this paper, we propose the HR-DSS approach, which stands for Human Resource (HR) Decision Support System, and uses explainable AI for employee attrition problems. The system is designed to assist HR departments in interpreting the predictions provided by machine learning models. In our experiments, we employ eight machine learning models to provide predictions. We further process the results achieved by the best-performing model by the SHAP explainability process and use the SHAP values to generate natural language explanations which can be valuable for HR. Furthermore, using "What-if-analysis", we aim to observe plausible causes for attrition of an individual employee. The results show that by adjusting the specific dominant features of each individual, employee attrition can turn into employee retention through informative business decisions.

We present a concept and implementation on increasing the efficiency of webinar software by a remote control approach using the technology WebRTC. This technology enables strong security and privacy, is crossdevice usable, uses open-source technology and enables a new level of interactiveness to webinars. We used SlideWiki, WebRTC, and browser speech to text engines to provide innovative accessibility features like multilingual presentations and live subtitles. Our solution was rated for real world usage aspects, tested within the SlideWiki project and we determined technological limits. Such measurements are currently not available and show that our approach outperforms open-source market competitors by efficiency and costs.

It is widely accepted that there is a large disparity between the availability of treatment options and the prevalence of diseases all over the world, thus placing individuals in danger. This disparity is partially caused by the restricted access to information that would allow health care and research policy makers to formulate more appropriate measures to mitigate it. Specifically, this shortage of information is caused by the difficulty in reliably obtaining and integrating data regarding the disease burden and the respective research investments. In response to these challenges, the Linked Data paradigm provides a simple mechanism for publishing and interlinking structured information on the Web. In conjunction with the ever increasing data on diseases and health care research available as Linked Data, an opportunity is created to reduce this information gap that would allow for better policy in response to these disparities. In this paper, we present the ReDD-Observatory, an approach for evaluating the Research-Disease Disparity based on the interlinking and integrating of various biomedical data sources. Specifically, we devise a method for representing statistical information as Linked Data and adopt interlinking algorithms for integrating relevant datasets (mainly GHO, Linked CT and PubMed). The assessment of the disparity is then performed with a number of parametrized SPARQL queries on the integrated data substrate. As a consequence, we are for the first time able to provide reliable indicators for the extent of the research-disease disparity in a semi-automated fashion, thus enabling health care professionals and policy makers to make more informed decisions.