Analyzing Head Orientation of Neurotypical and Autistic Individuals in Triadic Conversations

We propose a system that estimates people's body and head orientations using low-resolution point cloud data from two LiDAR sensors. Our models make accurate estimations in real-world conversation settings where the subject moves naturally with varying head and body poses. The body orientation estimation model uses ellipse fitting while the head orientation estimation model is a pipeline of geometric feature extraction and an ensemble of neural network regressors. Compared with other body and head orientation estimation systems using RGB cameras, our proposed system uses LiDAR sensors to preserve user privacy, while achieving comparable accuracy. Unlike other body/head orientation estimation systems, our sensors do not require a specified placement in front of the subject. Our models achieve a mean absolute estimation error of 5.2 degrees for body orientation and 13.7 degrees for head orientation. We use our models to quantify behavioral differences between neurotypical and autistic individuals in triadic conversations. Tests of significance show that people with autism spectrum disorder display significantly different behavior compared to neurotypical individuals in terms of distributing attention between participants in a conversation, suggesting that the approach could be a component of a behavioral analysis or coaching system.

On Tie Strength Augmented Social Correlation for Inferring Preference of Mobile Telco Users

For mobile telecom operators, it is critical to build preference profiles of their customers and connected users, which can help operators make better marketing strategies, and provide more personalized services. With the deployment of deep packet inspection (DPI) in telecom networks, it is possible for the telco operators to obtain user online preference. However, DPI has its limitations and user preference derived only from DPI faces sparsity and cold start problems. To better infer the user preference, social correlation in telco users network derived from Call Detailed Records (CDRs) with regard to online preference is investigated. Though widely verified in several online social networks, social correlation between online preference of users in mobile telco networks, where the CDRs derived relationship are of less social properties and user mobile internet surfing activities are not visible to neighbourhood, has not been explored at a large scale. Based on a real world telecom dataset including CDRs and preference of more than $550K$ users for several months, we verified that correlation does exist between online preference in such \textit{ambiguous} social network. Furthermore, we found that the stronger ties that users build, the more similarity between their preference may have. After defining the preference inferring task as a Top-$K$ recommendation problem, we incorporated Matrix Factorization Collaborative Filtering model with social correlation and tie strength based on call patterns to generate Top-$K$ preferred categories for users. The proposed Tie Strength Augmented Social Recommendation (TSASoRec) model takes data sparsity and cold start user problems into account, considering both the recorded and missing recorded category entries. The experiment on real dataset shows the proposed model can better infer user preference, especially for cold start users.