In order to enhance the security in wireless communication, authentication schemes come to be more crucial and widely deployed recently, especially those which are referred to as multi-factor biometric authentication that base on password, biometrics, and smart card protections. A new scheme in this way was proposed in 2010 by Li and Hwang. Then Das extended the work of Li et al. and made an improvement of their weak scheme in 2011. However, in 2012, Younghwa An demonstrated that Das's protocol failed to achieve mutual authentication for the server and the user. In this paper, it is described that Younghwa An's scheme cannot withstand the following two attacks. (i) It is still vulnerable to replay attack, then an adversary can masquerade as the legal server. (ii) It cannot provide user anonymity and resistance to user masquerading attack, because an adversary can execute the re-registration process by intercepting the ID i in the login phase. Therefore, an improvement to Younghwa An's scheme is presented in this paper. Then, security formal analysis of the modified scheme using the Burrows-Abadi-Needham logic is given, which demonstrates that the modified scheme with slight high computation costs can protect against the several possible attacks.

Autonomous driving has attracted significant research interests in the past two decades as it offers many potential benefits, including releasing drivers from exhausting driving and mitigating traffic congestion, among others. Despite promising progress, lane-changing remains a great challenge for autonomous vehicles (AV), especially in mixed and dynamic traffic scenarios. Recently, reinforcement learning (RL) has been widely explored for lane-changing decision makings in AVs with encouraging results demonstrated. However, the majority of those studies are focused on a single-vehicle setting, and lane-changing in the context of multiple AVs coexisting with human-driven vehicles (HDVs) have received scarce attention. In this paper, we formulate the lane-changing decision-making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning (MARL) problem, where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs. Specifically, a multi-agent advantage actor-critic (MA2C) method is proposed with a novel local reward design and a parameter sharing scheme. In particular, a multi-objective reward function is designed to incorporate fuel efficiency, driving comfort, and the safety of autonomous driving. A comprehensive experimental study is made that our proposed MARL framework consistently outperforms several state-of-the-art benchmarks in terms of efficiency, safety, and driver comfort.

This study was aimed at analyzing data and evaluating the accuracy of a new subcutaneous continuous glucose-monitoring device by referring to finger-pricking measurement. The data were obtained from 7 diabetic patients. An improved implanted flex sensor was used to measure the interstitial glucose concentration every 3 min within 6 days, and five finger-pricking samples were collected every day for comparison. A periodic glucose change happened every day. 2.45% of CGM values were in the hypoglycemic range (<70 mg/dl), 55.4% were in the normal range (70-180 mg/dl), and 42.15% were in the hyperglycemic range (>180 mg/dl). The interstitial glucose concentrations (n=204) were well linearly correlated with the capillary glucose concentrations (r=0.94, P<0.01), but a delay occurred between 10 and 40 minutes within two measurements (CGM later), and the individual average delay times were relatively close to 24.6 minutes. Clarke’s error grid analysis showed that 86.7% of the points fell in zone A, 12.7% fell in zone B, and only 0.6% fell in zone D. An overall MARD was 10.22%. This study demonstrated that the CGM was accurate and highly reliable; thus, it constituted a new device for continuous glucose monitoring in diabetic patients.

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