Modeling Interdependent and Periodic Real-World Action Sequences

Mobile health applications, including those that track activities such as exercise, sleep, and diet, are becoming widely used. Accurately predicting human actions is essential for targeted recommendations that could improve our health and for personalization of these applications. However, making such predictions is extremely difficult due to the complexities of human behavior, which consists of a large number of potential actions that vary over time, depend on each other, and are periodic. Previous work has not jointly modeled these dynamics and has largely focused on item consumption patterns instead of broader types of behaviors such as eating, commuting or exercising. In this work, we develop a novel statistical model for Time-varying, Interdependent, and Periodic Action Sequences. Our approach is based on personalized, multivariate temporal point processes that model time-varying action propensities through a mixture of Gaussian intensities. Our model captures short-term and long-term periodic interdependencies between actions through Hawkes process-based self-excitations. We evaluate our approach on two activity logging datasets comprising 12 million actions taken by 20 thousand users over 17 months. We demonstrate that our approach allows us to make successful predictions of future user actions and their timing. Specifically, our model improves predictions of actions, and their timing, over existing methods across multiple datasets by up to 156%, and up to 37%, respectively. Performance improvements are particularly large for relatively rare and periodic actions such as walking and biking, improving over baselines by up to 256%. This demonstrates that explicit modeling of dependencies and periodicities in real-world behavior enables successful predictions of future actions, with implications for modeling human behavior, app personalization, and targeting of health interventions.Comment: Accepted at WWW 201

Cognitive assisted living ambient system: a survey

The demographic change towards an aging population is creating a significant impact and introducing drastic challenges to our society. We therefore need to find ways to assist older people to stay independently and prevent social isolation of these population. Information and Communication Technologies (ICT) provide various solutions to help older adults to improve their quality of life, stay healthier, and live independently for a time. Ambient Assisted Living (AAL) is a field to investigate innovative technologies to provide assistance as well as healthcare and rehabilitation to impaired seniors. The paper provides a review of research background and technologies of AAL

Enabling Artificial Intelligence Analytics on The Edge

This thesis introduces a novel distributed model for handling in real-time, edge-based video analytics. The novelty of the model relies on decoupling and distributing the services into several decomposed functions, creating virtual function chains (V F C model). The model considers both computational and communication constraints. Theoretical, simulation and experimental results have shown that the V F C model can enable the support of heavy-load services to an edge environment while improving the footprint of the service compared to state-of-the art frameworks. In detail, results on the V F C model have shown that it can reduce the total edge cost, compared with a monolithic and a simple frame distribution models. For experimenting on a real-case scenario, a testbed edge environment has been developed, where the aforementioned models, as well as a general distribution framework (Apache Spark ©), have been deployed. A cloud service has also been considered. Experiments have shown that V F C can outperform all alternative approaches, by reducing operational cost and improving the QoS. Finally, a migration model, a caching model and a QoS monitoring service based on Long-Term-Short-Term models are introduced