Gait-Based Smart Pairing System for Personal Wearable Devices

With the rapid development of embedded technology and mobile computing, we have seen a growing number of Internet of Things (IoT) devices on the market. As the number of wearable devices belonging to the same user increases rapidly, secure pairing between legitimate devices becomes an important research problem. In this chapter, we propose the first gait-based shared key generation system that assists two devices to generate a common secure key by exploiting the user’s unique walking pattern. The system is based on the fact that sensors on different positions of the same user exhibit similar accelerometer signal when the user is walking. Therefore, the acceleration can be used as a shared secret information to generate a common key on different devices independently. Our experimental results show that the key generated by two independent devices on the same body is able to achieve 100% bit agreement rate. The proposed key generation protocol can establish a 128-bit key in 5 s (about 10 steps) with entropy varying from 0.93 to 1. We also find that the proposed scheme can run in real time on modern smartphone and require low system cost

Uncovering User Interest from Biased and Noised Watch Time in Video Recommendation

In the video recommendation, watch time is commonly adopted as an indicator of user interest. However, watch time is not only influenced by the matching of users' interests but also by other factors, such as duration bias and noisy watching. Duration bias refers to the tendency for users to spend more time on videos with longer durations, regardless of their actual interest level. Noisy watching, on the other hand, describes users taking time to determine whether they like a video or not, which can result in users spending time watching videos they do not like. Consequently, the existence of duration bias and noisy watching make watch time an inadequate label for indicating user interest. Furthermore, current methods primarily address duration bias and ignore the impact of noisy watching, which may limit their effectiveness in uncovering user interest from watch time. In this study, we first analyze the generation mechanism of users' watch time from a unified causal viewpoint. Specifically, we considered the watch time as a mixture of the user's actual interest level, the duration-biased watch time, and the noisy watch time. To mitigate both the duration bias and noisy watching, we propose Debiased and Denoised watch time Correction (D$^2$Co), which can be divided into two steps: First, we employ a duration-wise Gaussian Mixture Model plus frequency-weighted moving average for estimating the bias and noise terms; then we utilize a sensitivity-controlled correction function to separate the user interest from the watch time, which is robust to the estimation error of bias and noise terms. The experiments on two public video recommendation datasets and online A/B testing indicate the effectiveness of the proposed method.Comment: Accepted by Recsys'2

Dexmedetomidine Versus Propofol Sedation Improves Sublingual Microcirculation After Cardiac Surgery: A Randomized Controlled Trial

ObjectivesTo compare the effects of dexmedetomidine and propofol on sublingual microcirculation in patients after cardiac surgery.DesignA prospective, randomized, single-blind study.SettingUniversity hospital.ParticipantsAdult patients undergoing elective valve surgery with cardiopulmonary bypass.InterventionsOn arrival in the intensive care unit (ICU), patients were assigned randomly to receive either dexmedetomidine (0.2-1.5 μg/kg/h) or propofol (5-50 μg/kg/min) with open-label titration to a target Richmond Agitation-Sedation Scale of 0 to –3.Measurements and Main ResultsSublingual microcirculation was recorded using sidestream dark-field imaging at ICU admission (baseline [T1]) and 4 hours (T2) and 24 hours after ICU admission (T3). At T2, median changes in perfused small-vessel density and the De Backer score from baseline were significantly greater in the dexmedetomidine group (n = 29) than in the propofol group (n = 32) (1.3 v 0 mm/mm2, p = 0.025; 0.9 v –0.1/mm, p = 0.005, respectively); median changes in small-vessel density and the proportion of perfused small vessels from baseline also tended to be higher in the dexmedetomidine group compared with the propofol group (1.0 v –0.1 mm/mm2, p = 0.050; 2.1% v 0.5%, p = 0.062, respectively). At T3, there still was a trend toward greater improvements in the small vessel density, proportion of perfused small-vessels, perfused small-vessel density, and De Backer score from baseline in the dexmedetomidine group than in the propofol group.ConclusionsThis trial demonstrated that dexmedetomidine sedation may be better able to improve microcirculation in cardiac surgery patients during the early postoperative period compared with propofol

Fast and accurate X-ray fluorescence computed tomography imaging with the ordered-subsets expectation maximization algorithm.

The ordered-subsets expectation maximization algorithm (OSEM) is introduced to X-ray fluorescence computed tomography (XFCT) and studied; here, simulations and experimental results are presented. The simulation results indicate that OSEM is more accurate than the filtered back-projection algorithm, and it can efficiently suppress the deterioration of image quality within a large range of angular sampling intervals. Experimental results of both an artificial phantom and cirrhotic liver show that with a satisfying image quality the angular sampling interval could be improved to save on the data-acquisition time when OSEM is employed. In addition, with an optimum number of subsets, the image reconstruction time of OSEM could be reduced to about half of the time required for one subset. Accordingly, it can be concluded that OSEM is a potential method for fast and accurate XFCT imaging