Voting Systems with Trust Mechanisms in Cyberspace: Vulnerabilities and Defenses

With the popularity of voting systems in cyberspace, there is growing evidence that current voting systems can be manipulated by fake votes. This problem has attracted many researchers working on guarding voting systems in two areas: relieving the effect of dishonest votes by evaluating the trust of voters, and limiting the resources that can be used by attackers, such as the number of voters and the number of votes. In this paper, we argue that powering voting systems with trust and limiting attack resources are not enough. We present a novel attack named as Reputation Trap (RepTrap). Our case study and experiments show that this new attack needs much less resources to manipulate the voting systems and has a much higher success rate compared with existing attacks. We further identify the reasons behind this attack and propose two defense schemes accordingly. In the first scheme, we hide correlation knowledge from attackers to reduce their chance to affect the honest voters. In the second scheme, we introduce robustness-of-evidence, a new metric, in trust calculation to reduce their effect on honest voters. We conduct extensive experiments to validate our approach. The results show that our defense schemes not only can reduce the success rate of attacks but also significantly increase the amount of resources an adversary needs to launch a successful attack

Electronic Structures of SiC Nanoribbons

Electronic structures of SiC nanoribbons have been studied by spin-polarized density functional calculations. The armchair nanoribbons are nonmagnetic semiconductor, while the zigzag nanoribbons are magnetic metal. The spin polarization in zigzag SiC nanoribbons is originated from the unpaired electrons localized on the ribbon edges. Interestingly, the zigzag nanoribbons narrower than $\sim$4 nm present half-metallic behavior. Without the aid of external field or chemical modification, the metal-free half-metallicity predicted for narrow SiC zigzag nanoribbons opens a facile way for nanomaterial spintronics applications.Comment: 10 pages, 5 figure

CryptoEval: Evaluating the Risk of Cryptographic Misuses in Android Apps with Data-Flow Analysis

The misunderstanding and incorrect configurations of cryptographic primitives have exposed severe security vulnerabilities to attackers. Due to the pervasiveness and diversity of cryptographic misuses, a comprehensive and accurate understanding of how cryptographic misuses can undermine the security of an Android app is critical to the subsequent mitigation strategies but also challenging. Although various approaches have been proposed to detect cryptographic misuses in Android apps, seldom studies have focused on estimating the security risks introduced by cryptographic misuses. To address this problem, we present an extensible framework for deciding the threat level of cryptographic misuses in Android apps. Firstly, we propose a unified specification for representing cryptographic misuses to make our framework extensible and develop adapters to unify the detection results of the state-of-the-art cryptographic misuse detectors, resulting in an adapter-based detection toolchain for a more comprehensive list of cryptographic misuses. Secondly, we employ a misuse-originating data-flow analysis to connect each cryptographic misuse to a set of data-flow sinks in an app, based on which we propose a quantitative data-flow-driven metric for assessing the overall risk of the app introduced by cryptographic misuses. To make the per-app assessment more useful in the app vetting at the app-store level, we apply unsupervised learning to predict and classify the top risky threats, to guide more efficient subsequent mitigations. In the experiments on an instantiated implementation of the framework, we evaluate the accuracy of our detection and the effect of data-flow-driven risk assessment of our framework. Our empirical study on over 40,000 apps as well as the analysis of popular apps reveals important security observations on the real threats of cryptographic misuses in Android apps

Characterization of the ompL1 gene of pathogenic Leptospira species in China and cross-immunogenicity of the OmpL1 protein

<p>Abstract</p> <p>Background</p> <p>The usefulness of available vaccine and serological tests for leptospirosis is limited by the low cross-reactivity of antigens from numerous serovars of pathogenic <it>Leptospira </it>spp. Identification of genus-specific protein antigens (GP-Ag) of <it>Leptospira </it>would be important for development of universal vaccines and serodiagnostic methods. OmpL1, a transmembrane porin of pathogenic leptospires, was identified as a possible GP-Ag, but its sequence diversity and immune cross-reactivity among different serovars of pathogenic leptospires remains largely unknown.</p> <p>Results</p> <p>PCR analysis demonstrated that the <it>ompL1 </it>gene existed in all 15 official Chinese standard strains as well as 163 clinical strains of pathogenic leptospires isolated in China. In the standard strains, the <it>ompL1 </it>gene could be divided into three groups (<it>ompL1/1</it>, <it>ompL1/2 </it>and <it>ompL1/3</it>) according to their sequence identities. Immune electron microscopy demonstrated that all products of the different gene types of <it>ompL1 </it>are located on the surface of leptospires. The microscopic agglutination test revealed extensive yet distinct cross-immunoagglutination among the antisera against recombinant OmpL1 (rOmpL1) and leptospiral strains belonging to different <it>ompL1 </it>gene types. These cross-immunoreactions were further verified by ELISAs using the OmpL1 proteins as the coated antigens in serum samples from 385 leptospirosis patients. All the antisera against rOmpL1 proteins could inhibit <it>L. interrogans </it>strain Lai from adhering to J774A.1 cells. Furthermore, immunization of guinea pigs with each of the rOmpL1 proteins could cause cross-immunoprotection against lethal challenge with leptospires from different <it>ompL1 </it>gene types.</p> <p>Conclusion</p> <p>Three types of the <it>ompL1 </it>gene are present in pathogenic leptospires in China. OmpL1 is an immunoprotective GP-Ag which should be considered in the design of new universal vaccines and serodiagnostic methods against leptospirosis.</p

Design and Performance Research on Dual Layer Cement Based Absorber Reinforced with Graphene Nanosheets and Manganese-zinc Ferrite

Dual layer cement-based absorber is synthesized by mixing with graphene nanosheets and manganese-zinc ferrite, to study the effect of absorbing filler content on the mechanical properties, microstructure, electrical resistivity and reflectivity of the paste. The microstructure of the absorber is seen by Scanning Electron Microscope (SEM) images, Fourier Transform Infrared (FTIR) spectroscopy, X-Ray Diffraction (XRD) curves of the absorber. The results show that graphene nanosheets significantly reduce the electrical resistivity of paste, increasing its mechanical properties by improving its pore structure. SEM images indicate that graphene nanosheets promote the increase and coarsening of cement hydration products and produce a large number of dense bulk crystals. Furthermore, reflectivity measurements show that the minimum reflectivity of – 14.1 dB is obtained in the range of 2 ~ 18 GHz and the effective bandwidth of 16 GHz is obtained when reflectivity is less than – 7 dB. This study provides a new method for the preparation of dual layer cement-based absorber

Interpreting Distributional Reinforcement Learning: A Regularization Perspective

Distributional reinforcement learning~(RL) is a class of state-of-the-art algorithms that estimate the whole distribution of the total return rather than only its expectation. Despite the remarkable performance of distributional RL, a theoretical understanding of its advantages over expectation-based RL remains elusive. In this paper, we attribute the superiority of distributional RL to its regularization effect in terms of the value distribution information regardless of its expectation. Firstly, by leverage of a variant of the gross error model in robust statistics, we decompose the value distribution into its expectation and the remaining distribution part. As such, the extra benefit of distributional RL compared with expectation-based RL is mainly interpreted as the impact of a \textit{risk-sensitive entropy regularization} within the Neural Fitted Z-Iteration framework. Meanwhile, we establish a bridge between the risk-sensitive entropy regularization of distributional RL and the vanilla entropy in maximum entropy RL, focusing specifically on actor-critic algorithms. It reveals that distributional RL induces a corrected reward function and thus promotes a risk-sensitive exploration against the intrinsic uncertainty of the environment. Finally, extensive experiments corroborate the role of the regularization effect of distributional RL and uncover mutual impacts of different entropy regularization. Our research paves a way towards better interpreting the efficacy of distributional RL algorithms, especially through the lens of regularization

Association between functional disability with postural balance among patients with chronic low back pain

IntroductionPostural balance is impaired in patients with chronic low back pain (CLBP). In addition, the swaying velocity can be affected by low back pain (LBP) dysfunction. However, the extent to which the dysfunction affects postural balance in CLBP patients remains unclear. Therefore, this study aimed to investigate the effect of LBP-related disability on postural balance among CLBP patients and determine factors associated with postural balance impairments.MethodsParticipants with CLBP were recruited and instructed to complete the one-leg stance and Y-balance test. Moreover, they were divided into two subgroups (i.e., low and medium to high LBP-related disability groups) to compare the difference in postural balance based on the degree of LBP-related disability measured by the Roland Morris Disability Questionnaire. The relationships between postural balance and negative emotions as well as LBP characteristics were determined using the Spearman correlations.ResultsA total of 49 participants with low LBP-related disabilities and 33 participants with medium to high LBP-related disabilities participated in the study. Compared to the medium to high LBP-related disability group, patients in the low LBP-related disability group performed better in one-leg stance on the left leg (z = -2.081, p = 0.037). For Y-balance test, patients in the low LBP-related disability group also had greater normalized values of left leg reach in posteromedial (t = 2.108, p = 0.038) direction and composite score (t = 2.261, p = 0.026) and of right leg reach in posteromedial (t = 2.185, p = 0.032), and posterolateral (t = 2.137, p = 0.036) directions as well as composite score (t = 2.258, p = 0.027). Factors associated with postural balance impairments were also revealed, such as anxiety, depression, and fear avoidance belief.DiscussionThe greater the dysfunction degree, the worse the CLBP patient’s postural balance impairment. Negative emotions could also be considered contributing factors for postural balance impairments