In recent years, significant effort has been invested verifying the reproducibility and robustness of research claims in social and behavioral sciences (SBS), much of which has involved resource-intensive replication projects. In this paper, we investigate prediction of the reproducibility of SBS papers using machine learning methods based on a set of features. We propose a framework that extracts five types of features from scholarly work that can be used to support assessments of reproducibility of published research claims. Bibliometric features, venue features, and author features are collected from public APIs or extracted using open source machine learning libraries with customized parsers. Statistical features, such as p-values, are extracted by recognizing patterns in the body text. Semantic features, such as funding information, are obtained from public APIs or are extracted using natural language processing models. We analyze pairwise correlations between individual features and their importance for predicting a set of human-assessed ground truth labels. In doing so, we identify a subset of 9 top features that play relatively more important roles in predicting the reproducibility of SBS papers in our corpus. Results are verified by comparing performances of 10 supervised predictive classifiers trained on different sets of features.

Housed administratively in the College of IST, the Center will bring together researchers from across the University to develop new AI technologies and understand their social and ethical implications. The Penn State Center for Socially Responsible Artificial Intelligence promotes the thoughtful development and application of AI and studies its impact on all areas of human endeavor. In addition to supporting research focused explicitly on AI for social good and mitigating threats from its misuse, through this center, Penn State will encourage that all AI research and development activities consider social and ethical implications as well as intended and possible unintended consequences. "Given the rapid expansion and progression of interdisciplinary research and the wide-ranging impact of artificial intelligence on society, this center will engage and enable Penn State scholars and educators to work together and use AI to address the grand challenges of our time," said Andrew Sears, dean of the College of Information Sciences and Technology (IST), who led the founding of the center. The endeavor will bring together researchers from diverse disciplines across the University, enabling multidisciplinary research and educational programs that will shape the future of AI.

Background: The current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from actigraphy is prediction of sleep-wake states. Current literature on prediction of sleep-wake states from actigraphy consists of methods that use population data, which we call generalized models. However, accounting for variability of sleep patterns across individuals calls for personalized models of sleep-wake states prediction that could be potentially better suited to individual-level data and yield more accurate estimation of sleep.