Implicit Smartphone User Authentication with Sensors and Contextual Machine Learning

Authentication of smartphone users is important because a lot of sensitive data is stored in the smartphone and the smartphone is also used to access various cloud data and services. However, smartphones are easily stolen or co-opted by an attacker. Beyond the initial login, it is highly desirable to re-authenticate end-users who are continuing to access security-critical services and data. Hence, this paper proposes a novel authentication system for implicit, continuous authentication of the smartphone user based on behavioral characteristics, by leveraging the sensors already ubiquitously built into smartphones. We propose novel context-based authentication models to differentiate the legitimate smartphone owner versus other users. We systematically show how to achieve high authentication accuracy with different design alternatives in sensor and feature selection, machine learning techniques, context detection and multiple devices. Our system can achieve excellent authentication performance with 98.1% accuracy with negligible system overhead and less than 2.4% battery consumption.Comment: Published on the IEEE/IFIP International Conference on Dependable Systems and Networks (DSN) 2017. arXiv admin note: substantial text overlap with arXiv:1703.0352

Status of the Muon (g-2) Experiment

The status of the muon (g-2) experiment, E821 at the Brookhaven AGS, is given. A new result with a precision of 5 parts per million has been obtained with direct muon injection into the ring and is presented. The theoretical motivation for the experiment, and a discussion of the sensitivity to non-standard model physics is also discussed.Comment: 12 pages, Latex, special macros needed: LP99macros.tex, Invited presentation at the XIX International Symposium on Lepton and Photon Interactions at High Energy, Stanford University, August 199

Muscle Memory and the Local Concentration of Capital Punishment

The modern death penalty is not just concentrating in a handful of practicing states; it is disappearing in all but a few capitally active localities. Capital-punishment concentration, however, still surfaces more as the subject of casual observation than as the object of sophisticated academic inquiry. Normative and doctrinal analyses of the phenomenon are virtually nonexistent, in part because the current ability to measure and report concentration is so limited. This Article is the first attempt to measure capital-punishment concentration rigorously, by combining different sources of county-level data and by borrowing quantitative tools that economists use to study market competition. The analysis yields three major findings: (1) capital sentencing is concentrating dramatically; (2) executions are concentrating more gradually; and (3) both trends persist within most capitally active states. Certain normative and doctrinal conclusions follow from the empirical findings. The causes of concentration are likely to be more bureaucratic and path dependent than they are democratic and pragmatic, reflecting what I call the “muscle memory” of local institutional practice. If local muscle memory indeed explains concentration, such concentration violates basic punishment norms requiring equal treatment of similar offenders. This problem notwithstanding, existing death penalty jurisprudence does not account for local concentration. For concentration to have any influence on the outcome of constitutional inquiry, the Supreme Court would have to revise its working definition of “arbitrariness.

Surface r Modes and Burst Oscillations of Neutron Stars

We study the $r$-modes propagating in steadily mass accreting, nuclear burning, and geometrically thin envelopes on the surface of rotating neutron stars. For the modal analysis, we construct the envelope models which are fully radiaitive or have a convective region. As the angular rotation frequency $\Omega$ is increased, the oscillation frequency $\omega$ of the $r$-modes in the thin envelopes deviates appreciably from the asymptotic frequency $\omega=2m\Omega/l^\prime(l^\prime+1)$ defined in the limit of $\Omega\to 0$, where $\omega$ is the frequency observed in the corotating frame of the star, and $m$ and $l^\prime$ are the indices of the spherical harmonic function $Y_{l^\prime}^m$ representing the angular dependence of the modes. We find that the fundamental $r$-modes in the convective models are destabilized by strong nuclear burning in the convective region. Because of excessive heating by nuclear buring, the corotating-frame oscillation frequency $\omega$ of the $r$-modes in the convective models becomes larger, and hence the inertial-frame oscillation frequency $|\sigma|$ becomes smaller, than those of the corresopnding $r$-modes in the radiative models, where $\sigma=\omega-m\Omega$ is negative for the $r$-modes of positive $m$. We find that the relative frequency change $f=-(\sigma_{conv}-\sigma_{rad})/\sigma_{rad}$ is always positive and becomes less than $\sim$0.01 for the fundamental $r$-modes of $l^\prime>|m|+1$ at $|\sigma_{rad}|/2\pi\sim$300Hz for $m=1$ or at $|\sigma_{rad}|/2\pi\sim$600Hz for $m=2$, where $\sigma_{conv}$ and $\sigma_{rad}$ denote the oscillation frequencies for the convective and the radiative envelope models, respectively.Comment: 20 pages, 12 figure

Persistence in systems with algebraic interaction

Persistence in coarsening 1D spin systems with a power law interaction $r^{-1-\sigma}$ is considered. Numerical studies indicate that for sufficiently large values of the interaction exponent $\sigma$ ($\sigma\geq 1/2$ in our simulations), persistence decays as an algebraic function of the length scale $L$, $P(L)\sim L^{-\theta}$. The Persistence exponent $\theta$ is found to be independent on the force exponent $\sigma$ and close to its value for the extremal ($\sigma \to \infty$) model, $\bar\theta=0.17507588...$. For smaller values of the force exponent ($\sigma< 1/2$), finite size effects prevent the system from reaching the asymptotic regime. Scaling arguments suggest that in order to avoid significant boundary effects for small $\sigma$, the system size should grow as ${[{\cal O}(1/\sigma)]}^{1/\sigma}$.Comment: 4 pages 4 figure

Long-term Solar Irradiance Variability: 1984-1989 Observations

Long-term variability in the total solar irradiance has been observed in the Earth Radiation Budget Experiment (ERBE) solar monitor measurements. The monitors have been used to measure the irradiance from the Earth Radiation Budget Satellite (ERBS) and the National Oceanic and Atmospheric Administration NOAA-9 and NOAA-10 spacecraft platforms since October 25, 1984, January 23, 1985, and October 22, 1986, respectively. Before September 1986, the ERBS irradiance values were found to be decreasing -0.03 percent per year. This period was marked by decreasing solar magnetic activity. Between September 1986 and mid-1989, the irradiance values increased approximately 0.1 percent. The latter period was marked by increasing solar activity which was associated with the initiations of the sunspot cycle number 22 and of a new 22-year Hale solar magnetic cycle. Therefore, long-term solar-irradiance variability appears to be correlated directly with solar activity. The maximum smoothed sunspot number occurred during September 1989, according to the Sunspot Index Data Center. Therefore, the recent irradiance increasing trend should disappear during early 1990 and change into a decreasing trend if the observed irradiance variability is correlated more so with the 11-year sunspot cycle than the 22-year Hale cycle. The ERBE irradiance values are presented and compared with sunspot activity for the 1984 to 1989 period. The ERBE values are compared with those available from the Nimbus-7 and Solar Maximum Mission spacecraft experiments

U-health expert system with statistical neural network

Ubiquitous Health(U-Health) system witch focuses on automated applications that can provide healthcare to human anywhere and anytime using wired and wireless mobile technologies is becoming increasingly important. This system consists of a network system to collect data and a sensor module which measures pulse, blood pressure, diabetes, blood sugar, body fat diet with management and measurement of stress etc, by both wired and wireless and further portable mobile connections. In this paper, we propose an expert system using back-propagation to support the diagnosis of citizens in U-Health system