An Efficient Searchable Encryption Against Keyword Guessing Attacks for Sharable Electronic Medical Records in Cloud-based System.

Preserving the privacy of electronic medical records (EMRs) is extremely important especially when medical systems adopt cloud services to store patients' electronic medical records. Considering both the privacy and the utilization of EMRs, some medical systems apply searchable encryption to encrypt EMRs and enable authorized users to search over these encrypted records. Since individuals would like to share their EMRs with multiple persons, how to design an efficient searchable encryption for sharable EMRs is still a very challenge work. In this paper, we propose a cost-efficient secure channel free searchable encryption (SCF-PEKS) scheme for sharable EMRs. Comparing with existing SCF-PEKS solutions, our scheme reduces the storage overhead and achieves better computation performance. Moreover, our scheme can guard against keyword guessing attack, which is neglected by most of the existing schemes. Finally, we implement both our scheme and a latest medical-based scheme to evaluate the performance. The evaluation results show that our scheme performs much better performance than the latest one for sharable EMRs.

Kernel-based least squares policy iteration for reinforcement learning.

In this paper, we present a kernel-based least squares policy iteration (KLSPI) algorithm for reinforcement learning (RL) in large or continuous state spaces, which can be used to realize adaptive feedback control of uncertain dynamic systems. By using KLSPI, near-optimal control policies can be obtained without much a priori knowledge on dynamic models of control plants. In KLSPI, Mercer kernels are used in the policy evaluation of a policy iteration process, where a new kernel-based least squares temporal-difference algorithm called KLSTD-Q is proposed for efficient policy evaluation. To keep the sparsity and improve the generalization ability of KLSTD-Q solutions, a kernel sparsification procedure based on approximate linear dependency (ALD) is performed. Compared to the previous works on approximate RL methods, KLSPI makes two progresses to eliminate the main difficulties of existing results. One is the better convergence and (near) optimality guarantee by using the KLSTD-Q algorithm for policy evaluation with high precision. The other is the automatic feature selection using the ALD-based kernel sparsification. Therefore, the KLSPI algorithm provides a general RL method with generalization performance and convergence guarantee for large-scale Markov decision problems (MDPs). Experimental results on a typical RL task for a stochastic chain problem demonstrate that KLSPI can consistently achieve better learning efficiency and policy quality than the previous least squares policy iteration (LSPI) algorithm. Furthermore, the KLSPI method was also evaluated on two nonlinear feedback control problems, including a ship heading control problem and the swing up control of a double-link underactuated pendulum called acrobot. Simulation results illustrate that the proposed method can optimize controller performance using little a priori information of uncertain dynamic systems. It is also demonstrated that KLSPI can be applied to online learning control by incorporating an initial controller to ensure online performance.

[Development of sleep/wake patterns in infants during the first 12 months of life].

OBJECTIVE: This was a nationwide study of sleep circadian in term infants. The aim was to understand the development characteristics of infants' sleep/wake patterns longitudinally in their own home environments over the first 12 months of life. METHOD: Totally 524 healthy term infants from 9 urban districts took part in this project Their sleep/wake patterns over 24 h were recorded using parental sleep diaries, from the 2nd day to 12 months old. RESULT: The results showed that infant daytime sleep changed significantly at 0-2, 3-4, 5-6, and 8-9 months after birth, and the change was the fastest in the first month, the mean percentage of daytime sleep decreased from 82.4% at Day 2 to 62.8% at 1 month old. Also, the average number of naps reduced from 3.7 to 2 across the infancy. The ability of continuous sleep throughout the night gradually enhanced from 1 month old, and the nocturnal longest sleep time extended to 6.8 h at 4 months of age as well as the nighttime awakening frequency less than 0.5 over 6 months old. Additionally, the nighttime sleep increased significantly at 4 and 9 months after birth, where the proportion of nighttime sleep increased from 55.8% at Day 2 to 64.3% of 4 months and 71.2% of 9 months respectively. In general, the total sleep time over a 24 h period presented a downward trend as the infant aged. CONCLUSION: The periods 0-6 and 8-9 months after birth were the key periods for the development of infant sleep.