Multi-modal sensors on mobile devices (e.g., smart watches and smartphones) have been widely used to ubiquitously perceive human mobility and body motions for understanding social interactions between people. This work investigates the correlations between the multi-modal data observed by mobile devices and social closeness among people along their trajectories. To close the gap between cyber-world data distances and physical-world social closeness, this work quantifies the cyber distances between multi-modal data. The human mobility traces and body motions are modeled as cyber signatures based on ambient Wi-Fi access points and accelerometer data observed by mobile devices that explicitly indicate the mobility similarity and movement similarity between people. To verify the merits of modeled cyber distances, we design the localization-free CybeR-physIcal Social dIStancing (CRISIS) system that detects if two persons are physically non-separate (i.e., not social distancing) due to close social interactions (e.g., taking similar mobility traces simultaneously or having a handshake with physical contact). Extensive experiments are conducted in two small-scale environments and a large-scale environment with different densities of Wi-Fi networks and diverse mobility and movement scenarios. The experimental results indicate that our approach is not affected by uncertain environmental conditions and human mobility with an overall detection accuracy of 98.41% in complex mobility scenarios. Furthermore, extensive statistical analysis based on 2-dimensional (2D) and 3-dimensional (3D) mobility datasets indicates that the proposed cyber distances are robust and well-synchronized with physical proximity levels.

In this paper, we study an important recommendation problem with heterogeneous feedback of users' grade scores such as 5-star grade scores and like/dislike binary ratings assigned to items. As a response, we address the problem from a transfer learning perspective, i.e., taking the grade scores as the target data and the binary ratings as the auxiliary data, in order to share knowledge between two different types of data more sufficiently. Technically, besides the observed explicit feedback of grade scores and binary ratings, we propose to exploit the implicit preference context beneath the feedback, which is incorporated into the prediction process of users' grade scores to items. Finally, we develop a novel and generic transfer learning solution, i.e., preference-aware transfer (PAT), which embodies several recent algorithms as parts of our solution and special cases. To verify our novel solution, we then conduct extensive empirical studies on two large and public datasets and find that our PAT performs significantly better than the state-of-the-art methods.

The increasing availability of heterogeneous ambient sensing systems challenges the according information processing systems to analyse and compare a variety of different systems in a single scenario. For instance, localization of objects can be performed by image processing systems as well as by radio based localization. If such systems are utilized to localize the same objects, synergy of the outputs is important to enable comparable and meaningful analysis. This demo showcases the practical deployment and challenges of such an example system.