How well can we detect stress from sensor data from wearables? And how to develop systems for it fast? You can find the full project and code on GitHub and a paper here (PDF). Or just visit the interactive live demo showcasing the whole project right now!
With about 300 million affected people, major depressive disorder (MDD) is one of the most common diseases worldwide. During the COVID-19 pandemic, the number of cases increased even further, by 28%. Many factors may be correlated with MDD, including the excessive use of social media apps. We investigated the relationship between the use of social … Read more
Can you predict if a self-described pornography-addict will relapse – based on his previous mental condition? We used machine learning to answer this question based on data collected by polish collaborators. Users used a mobile app for tracking their addictions and mental state. You can find a full project description and code on GitHub.
Today, I am starting at the National Institute of Informatics (NII) in the group of Prof. Akiko Aizawa. I acquired funding financing my full-time position in the competitive program IFI (German: Internationale Forschungsaufenthalte für Informatikerinnen & Informatiker) by the German Academic Exchange Service (DAAD). I will work on topics relating to user retention, data science, … Read more
Next app prediction can help enhance user interface design, pre-loading of apps, and network optimizations. Prior work has explored this topic, utilizing multiple different approaches but challenges like the user cold-start problem, data sparsity, and privacy concerns related to contextual data like location histories, persist. The user cold-start problem occurs when a user has recently … Read more
Several governments introduced or promoted the use of contact-tracing apps during the ongoing COVID-19 pandemic. In Germany, the related app is called Corona-Warn-App, and by end of 2020, it had 22.8 million downloads. Contact tracing is a promising approach for containing the spread of the novel coronavirus. It is only effective if there is a … Read more
We investigated associations between personality traits and smartphone usage in daily life. 526 participants (mean age 34.57 years, SD = 12.85, 21% female) used TYDR (Track Your Daily Routine) for 48 days, on average (SD = 63.2, range 2 to 304). The Big Five Inventory 2 (BFI-2) was used to measure personality traits (Extraversion, Agreeableness, … Read more
We released AnaLoc on GitHub. AnaLoc is a microservice-based, scalable architecture for stream and batch processing of industrial sensor data. The code on GitHub shows the analysis of location data of autonomous vehicles in the modern factory. The architecture is built on Spring Boot and Apache Flink and is deployed via Docker Compose. More details … Read more
Expanding on our earlier work on privacy in mobile sensing/data collection apps, we evaluated the data we collected with TYDR. Our experimental evaluation based on the first two month of data collected with TYDR shows evidence that our users accept our proposed privacy model. Based on data about granting TYDR all or no Android system … Read more
Expanding our earlier work on choice overload in related-article recommendations, we analyzed a digital library to figure out the ideal number of recommendations to show to users. Some of our metrics point toward 5-6 items as the ideal number of recommendations to display. You can find the full paper here (PDF). Choice Overload describes a … Read more
