Passive faraday mirror attack in practical two-way quantum key distribution system

The faraday mirror (FM) plays a very important role in maintaining the stability of two way plug-and-play quantum key distribution (QKD) system. However, the practical FM is imperfect, which will not only introduce additional quantum bit error rate (QBER) but also leave a loophole for Eve to spy the secret key. In this paper, we propose a passive faraday mirror attack in two way QKD system based on the imperfection of FM. Our analysis shows that, if the FM is imperfect, the dimension of Hilbert space spanned by the four states sent by Alice is three instead of two. Thus Eve can distinguish these states with a set of POVM operators belonging to three dimension space, which will reduce the QBER induced by her attack. Furthermore, a relationship between the degree of the imperfection of FM and the transmittance of the practical QKD system is obtained. The results show that, the probability that Eve loads her attack successfully depends on the degree of the imperfection of FM rapidly, but the QBER induced by Eve's attack changes with the degree of the imperfection of FM slightly

The Impact of Self-Monitoring on Theory of Planned Behavior:Study of Web Portal Usage

With the prosperity of the Internet and WWW, lots of web sites have been raised. The eBusinessWeekly 2000 reported that “web portal” was with highly proportion (46.1%) among all kind of the websites; this reveals that web portal would be the place where Internet users visit most often. Since the Internet is widely popular, user’s perception to use such WWW technology was easy to understand. In this research, we study the personality trait of self-monitoring on the Internet web portal usage behavior. Thus, we focused on the concept of self-monitoring in exploring the attitude, web self-efficacy, intention and web portal usage to see the variation in this concept. The combined dataset shows that attitude toward web portal and web self-efficacy are significant impacted on intention, and actual web portal usage is significant influenced by both intention and web self-efficacy. Further, the differences between high self-monitoring (HSM) group and low self-monitoring (LSM) group are observed and have significant di inct in our research model, therefore two moderating hypotheses are both supported. Base on the research findings, conclusions and implications are discussed

Effect of source tampering in the security of quantum cryptography

The security of source has become an increasingly important issue in quantum cryptography. Based on the framework of measurement-device-independent quantum-key-distribution (MDI-QKD), the source becomes the only region exploitable by a potential eavesdropper (Eve). Phase randomization is a cornerstone assumption in most discrete-variable (DV-) quantum communication protocols (e.g., QKD, quantum coin tossing, weak coherent state blind quantum computing, and so on), and the violation of such an assumption is thus fatal to the security of those protocols. In this paper, we show a simple quantum hacking strategy, with commercial and homemade pulsed lasers, by Eve that allows her to actively tamper with the source and violate such an assumption, without leaving a trace afterwards. Furthermore, our attack may also be valid for continuous-variable (CV-) QKD, which is another main class of QKD protocol, since, excepting the phase random assumption, other parameters (e.g., intensity) could also be changed, which directly determine the security of CV-QKD.Comment: 9 pages, 6 figure

Value of segmental myocardial strain by 2-dimensional strain echocardiography for assessment of scar area induced in a rat model of myocardial infarction

<p>Abstract</p> <p>Objectives</p> <p>Two-dimensional strain echocardiography (2DSE) technique has enabled accurate quantification of regional myocardial function. This experimental study was aimed to investigate the value of 2DSE in detection of segmental regional myocardial dysfunction induced by fibrosis following myocardial infarction in a small animal (rat) model.</p> <p>Methods</p> <p>A rat model of myocardial infarction was established by ligation of the proximal left anterior descending coronary artery in 17 SD rats. Regional myocardial function was detected by 2DSE at baseline and 4-weeks post-infarction, including end-systolic radial strain and strain rate (SR and SrR) and end-systolic circumferential strain and strain rate (SC and SrC) of each of six segments at papillary level. According to the size of scar found by histologic Masson staining, the optimal cutoff points of parameters for detecting scar area were analyzed and the sensitivity and specificity of every parameter to detect myocardial scar were obtained using ROC.</p> <p>Results</p> <p>(1) Comparing with parameters measured at baseline, there were significant decreases in SR, SrR, SC and SrC of each segment at 4 weeks post-infarction, with the worst in the infarct area (32.90 ± 8.79 vs 11.18 ± 3.89, 6.28 ± 1.35 vs 3.18 ± 0.47, -14.46 ± 2.21 vs <it>-</it>6.30 ± 2.17 and 4.93 ± 0.95 vs 2.59 ± 1.16, respectively) (all <it>P </it>< 0.05). (2)By 4 weeks, the myocardium of infarct area (anteroseptum, anterior and anterolateral) had fibrosis (31.33 ± 9.89, 73.42 ± 13.21 and 13.99 ± 3.24%, respectively) with minimal fibrosis in inferoseptal segment (0.32 ± 0.19%), no fibrosis was found in the inferior and inferolateral segments. (3)Significant negative correlations were found between the size of segmental scar and 2DSE parameters (r-value -0.61 ~ -0.80, all <it>P </it>< 0.01) with the strongest correlation in SR. SR less than 10% has 84% sensitivity and 98% specificity for detecting segments of scar area greater than 30% with AUC = 0.97.</p> <p>Conclusions</p> <p>2DSE is able to assess regional myocardial dysfunction in a rat model of myocardial infarction and has high accuracy in detecting infarct segments with scar area greater than 30%.</p

Nonlinear Inertia Classification Model and Application

Classification model of support vector machine (SVM) overcomes the problem of a big number of samples. But the kernel parameter and the punishment factor have great influence on the quality of SVM model. Particle swarm optimization (PSO) is an evolutionary search algorithm based on the swarm intelligence, which is suitable for parameter optimization. Accordingly, a nonlinear inertia convergence classification model (NICCM) is proposed after the nonlinear inertia convergence (NICPSO) is developed in this paper. The velocity of NICPSO is firstly defined as the weighted velocity of the inertia PSO, and the inertia factor is selected to be a nonlinear function. NICPSO is used to optimize the kernel parameter and a punishment factor of SVM. Then, NICCM classifier is trained by using the optical punishment factor and the optical kernel parameter that comes from the optimal particle. Finally, NICCM is applied to the classification of the normal state and fault states of online power cable. It is experimentally proved that the iteration number for the proposed NICPSO to reach the optimal position decreases from 15 to 5 compared with PSO; the training duration is decreased by 0.0052 s and the recognition precision is increased by 4.12% compared with SVM