Johannes Kepler University of Linz

About: Johannes Kepler University of Linz is a education organization based out in Linz, Oberösterreich, Austria. It is known for research contribution in the topics: Computer science & Thin film. The organization has 6605 authors who have published 19243 publications receiving 385667 citations.

A Meta-Analysis of the International Gender Wage Gap

Abstract: Since the early 1970s, a number of authors have calculated gender wage differentials between women and men of equal productivity. This meta-study provides a new quantitative review of this vast amount of empirical literature on gender wage differentials as it concerns not only differences in methodology, data, and time periods, but also different countries. We place particular emphasis on a proper consideration of the quality of the underlying study which is done by a weighting with quality indicators. The results show that data restrictions - i.e. the limitation of the analysis to new entrants, never-marrieds, or one narrow occupation only - have the biggest impact on the resulting gender wage gap. Moreover, we are able to show what effect a misspecification of the underlying wage equation - like the frequent use of potential experience - has on the calculated gender wage gap. Over time, raw wage differentials worldwide have fallen substantially; however, most of this decrease is due to better labor market endowments of females.

Collecting complex activity datasets in highly rich networked sensor environments

Abstract: We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects performing morning activities, yielding over 25 hours of sensor data. We report the number of activity occurrences observed during post-processing, and estimate that over 13000 and 14000 object and environment interactions occurred. We describe the networked sensor setup and the methodology for data acquisition, synchronization and curation. We report on the challenges and outline lessons learned and best practice for similar large scale deployments of heterogeneous networked sensor systems. We evaluate data acquisition quality for on-body and object integrated wireless sensors; there is less than 2.5% packet loss after tuning. We outline our use of the dataset to develop new sensor network self-organization principles and machine learning techniques for activity recognition in opportunistic sensor configurations. Eventually this dataset will be made public.

Effective preprocessing in SAT through variable and clause elimination

Abstract: Preprocessing SAT instances can reduce their size considerably. We combine variable elimination with subsumption and self-subsuming resolution, and show that these techniques not only shrink the formula further than previous preprocessing efforts based on variable elimination, but also decrease runtime of SAT solvers substantially for typical industrial SAT problems. We discuss critical implementation details that make the reduction procedure fast enough to be practical.