A wide range of user perception applications leverage inertial measurement unit (IMU) data for online prediction. However, restricted by the non-i.i.d. nature of IMU data collected from mobile devices, most systems work well only in a controlled setting (e.g., for a specific user in particular postures), limiting application scenarios. To achieve uncontrolled online prediction on mobile devices, referred to as the flexible user perception (FUP) problem, is attractive but hard. In this paper, we propose a novel scheme, called Prism, which can obtain high FUP accuracy on mobile devices. The core of Prism is to discover task-aware domains embedded in IMU dataset, and to train a domain-aware model on each identified domain. To this end, we design an expectation-maximization (EM) algorithm to estimate latent domains with respect to the specific downstream perception task. Finally, the best-fit model can be automatically selected for use by comparing the test sample and all identified domains in the feature space. We implement Prism on various mobile devices and conduct extensive experiments. Results demonstrate that Prism can achieve the best FUP performance with a low latency.

Abstract As the fast growth of multimedia information, the security of multimedia systems is becoming a rather important topic nowadays. Multimedia systems are often suffering attacks when users access the information and online services. Because of the excellent features of the biometric, many biometric‐based three‐factor remote user authentication schemes have been proposed to provide high level of security for different network‐based application systems. Recently, An pointed out the weaknesses of Das's three‐factor remote user authentication scheme and proposed an improved biometric‐based three‐factor remote user authentication scheme. An's scheme improves the security problems of previous schemes while keeping the efficiency. However, after detailed analysis, we find that An's scheme exists some weaknesses such as vulnerable to denial‐of‐service attack and forgery attack, cannot detect unauthorized login quickly, and does not provide session key agreement. In order to provide high level of security for multimedia systems, we design a robust three‐factor remote user authentication scheme with key agreement using elliptic curve cryptosystem. Copyright © 2014 John Wiley & Sons, Ltd.

The use of machine learning approaches for long-term hand tracking poses some major challenges such as attaining robustness to inconsistencies in lighting, scale and object appearances, background clutter, and total object occlusion/disappearance. To address these issues in this paper, we present a robust machine learning approach based on enhanced particle filter trackers. The inherent drawbacks associated with the particle filter approach, i.e., sample degeneration and sample impoverishment, are minimized by infusing the particle filter with the mean shift approach. Moreover, to instill our tracker with reacquisition ability, we propose a rotation invariant and efficient detection framework named beta histograms of oriented gradients. Our robust appearance model operates on the red, green, blue color histogram and our newly proposed rotation invariant noise compensated local binary patterns descriptor, which is a noise compensated, rotation invariant version of the local binary patterns descriptor. Through our experiments, we demonstrate that our proposed hand tracker performs favorably against state-of-the-art algorithms on numerous challenging video sequences of hand postures, and overcomes the largely unsolved problem of redetecting hands after they vanish and reappear into the frame.