Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.

Attribute-based encryption (ABE) allows users to encrypt (cloud) data with fine-grained Boolean access control policies. To be able to decrypt the ciphertext, users need to have a private key with the associated attributes. If the attributes satisfy the formula, the plaintext can be recovered. In this paper, ABE is extended with dynamic attributes. This allows attributes to be added to an existing private key. A server component named Attribute Authority is introduced. By using these dynamic attributes, it is now possible to have the decryption depend on data that changes often, such as location information of a mobile device. Two schemes were developed that convert location data into usable ABE attributes. To demonstrate our results, an Android application was implemented and evaluated in a field test.

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 registered to the smartphone app system and there is not enough information about his/her preferences and his/her history of smartphone usage. In this work, we try to address the above issues. We introduce WhatsNextApp, an approach based on LSTM (Long Short-Term Memory) networks using sequences of app usage logs. Our approach is inspired by Word Embeddings and treats sequences of app usage logs as sequences of words. We collect a real-life data set consisting of 975 Android users with over 22 million app usage events. We build a generic (user-independent) WhatsNextApp model and the evaluation with our data set shows that it outperforms related studies for existing users where we achieve a recall@8 (recall for the top 8 apps) of 92%. For the user cold-start problem with the 500 most frequent apps, we achieve a recall@8 of 82.7%.

Recommender systems are used to filter through vast amounts of items and recommend those that potentially have the highest relevance for the user. Recently, research dealing with recommendations in academia increased. In this paper, we analyze to what extent social relations from existing data can be utilized to generate academic conference recommendations. We design and implement a social recommender system and show how, without the need for explicit ratings, viable recommendations can be made, while at the same time reducing the cost of kNN-neighborhood selection.