Towards Inferring Mechanical Lock Combinations using Wrist-Wearables as a Side-Channel

Wrist-wearables such as smartwatches and fitness bands are equipped with a variety of high-precision sensors that support novel contextual and activity-based applications. The presence of a diverse set of on-board sensors, however, also expose an additional attack surface which, if not adequately protected, could be potentially exploited to leak private user information. In this paper, we investigate the feasibility of a new attack that takes advantage of a wrist-wearable's motion sensors to infer input on mechanical devices typically used to secure physical access, for example, combination locks. We outline an inference framework that attempts to infer a lock's unlock combination from the wrist motion captured by a smartwatch's gyroscope sensor, and uses a probabilistic model to produce a ranked list of likely unlock combinations. We conduct a thorough empirical evaluation of the proposed framework by employing unlocking-related motion data collected from human subject participants in a variety of controlled and realistic settings. Evaluation results from these experiments demonstrate that motion data from wrist-wearables can be effectively employed as a side-channel to significantly reduce the unlock combination search-space of commonly found combination locks, thus compromising the physical security provided by these locks

Inferring Network Topology from Complex Dynamics

Inferring network topology from dynamical observations is a fundamental problem pervading research on complex systems. Here, we present a simple, direct method to infer the structural connection topology of a network, given an observation of one collective dynamical trajectory. The general theoretical framework is applicable to arbitrary network dynamical systems described by ordinary differential equations. No interference (external driving) is required and the type of dynamics is not restricted in any way. In particular, the observed dynamics may be arbitrarily complex; stationary, invariant or transient; synchronous or asynchronous and chaotic or periodic. Presupposing a knowledge of the functional form of the dynamical units and of the coupling functions between them, we present an analytical solution to the inverse problem of finding the network topology. Robust reconstruction is achieved in any sufficiently long generic observation of the system. We extend our method to simultaneously reconstruct both the entire network topology and all parameters appearing linear in the system's equations of motion. Reconstruction of network topology and system parameters is viable even in the presence of substantial external noise.Comment: 11 pages, 4 figure

Inferring the effective thickness of polyelectrolytes from stretching measurements at various ionic strengths: applications to DNA and RNA

By resorting to the thick-chain model we discuss how the stretching response of a polymer is influenced by the self-avoidance entailed by its finite thickness. The characterization of the force versus extension curve for a thick chain is carried out through extensive stochastic simulations. The computational results are captured by an analytic expression that is used to fit experimental stretching measurements carried out on DNA and single-stranded RNA (poly-U) in various solutions. This strategy allows us to infer the apparent diameter of two biologically-relevant polyelectrolytes, namely DNA and poly-U, for different ionic strengths. Due to the very different degree of flexibility of the two molecules, the results provide insight into how the apparent diameter is influenced by the interplay between the (solution-dependent) Debye screening length and the polymers' ``bare'' thickness. For DNA, the electrostatic contribution to the effective radius, $\Delta$, is found to be about 5 times larger than the Debye screening length, consistently with previous theoretical predictions for highly-charged stiff rods. For the more flexible poly-U chains the electrostatic contribution to $\Delta$ is found to be significantly smaller than the Debye screening length.Comment: iopart, 14 pages, 13 figures, to appear in J. Phys.: Condens. Matte

Approximate parameter inference in systems biology using gradient matching: a comparative evaluation

Background: A challenging problem in current systems biology is that of parameter inference in biological pathways expressed as coupled ordinary differential equations (ODEs). Conventional methods that repeatedly numerically solve the ODEs have large associated computational costs. Aimed at reducing this cost, new concepts using gradient matching have been proposed, which bypass the need for numerical integration. This paper presents a recently established adaptive gradient matching approach, using Gaussian processes, combined with a parallel tempering scheme, and conducts a comparative evaluation with current state of the art methods used for parameter inference in ODEs. Among these contemporary methods is a technique based on reproducing kernel Hilbert spaces (RKHS). This has previously shown promising results for parameter estimation, but under lax experimental settings. We look at a range of scenarios to test the robustness of this method. We also change the approach of inferring the penalty parameter from AIC to cross validation to improve the stability of the method. Methodology: Methodology for the recently proposed adaptive gradient matching method using Gaussian processes, upon which we build our new method, is provided. Details of a competing method using reproducing kernel Hilbert spaces are also described here. Results: We conduct a comparative analysis for the methods described in this paper, using two benchmark ODE systems. The analyses are repeated under different experimental settings, to observe the sensitivity of the techniques. Conclusions: Our study reveals that for known noise variance, our proposed method based on Gaussian processes and parallel tempering achieves overall the best performance. When the noise variance is unknown, the RKHS method proves to be more robust

Revealing networks from dynamics: an introduction

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.Comment: Topical review, 48 pages, 7 figure

Improved description and monitoring of near surface hazardous infiltrate complexes by shear waves for effective containment reponse

Among numerous causes of fluid releases and infiltration in near surface, resurgence in such anthropic activities associated with unconventional resource developments have brought about a resounding concern. Apart from the risk of an immediate chemical hazard, a long term possible recurrent geo-environmental risk since can also be envisaged as for various prevalent stake holders and broader initiatives. Urgency and exactness for spatiotemporal containment and remediation promotes the devising of efficient methods for monitoring near subsurface flow complexes caused by such spills. Swave (Shear waves) spectral imaging results, in relevant context, of a controlled immiscible fluid displacement monitoring experimental study are analysed and inferred. Against the prospective method as well evaluated, Swave diffraction associated spectral peculiarities are examined, importantly, given background medium characteristics definitions invoking fresh insights of microscale significance alongside macroscale potential

Inferring the magnetic field vector in the quiet Sun. III. Disk variation of the Stokes profiles and isotropism of the magnetic field

We have studied the angular distribution of the magnetic field vector in the solar internetwork employing high-quality data (noise level $\sigma \approx 3\times 10^{-4}$ in units of the quiet-Sun intensity) at different latitudes recorded with the Hinode/SP instrument. Instead of applying traditional inversion codes of the radiative transfer equation to retrieve the magnetic field vector at each spatial point on the solar surface and studying the resulting distribution of the magnetic field vector, we surmised a theoretical distribution function of the magnetic field vector and used it to obtain the theoretical histograms of the Stokes profiles. These histograms were then compared to the observed ones. Any mismatch between them was ascribed to the theoretical distribution of the magnetic field vector, which was subsequently modified to produce a better fit to the observed histograms. With this method we find that Stokes profiles with signals above $2\times 10^{-3}$ (in units of the continuum intensity) cannot be explained by an isotropic distribution of the magnetic field vector. We also find that the differences between the histograms of the Stokes profiles observed at different latitudes cannot be explained in terms of line-of-sight effects. However, they can be explained by a distribution of the magnetic field vector that inherently varies with latitude. We note that these results are based on a series of assumptions that, although briefly discussed in this paper, need to be considered in more detail in the future.Comment: Accepted for publication in Astronomy and Astrophysics. 14 pages, 8 color figure

Sim-to-Real Transfer of Robotic Control with Dynamics Randomization

Simulations are attractive environments for training agents as they provide an abundant source of data and alleviate certain safety concerns during the training process. But the behaviours developed by agents in simulation are often specific to the characteristics of the simulator. Due to modeling error, strategies that are successful in simulation may not transfer to their real world counterparts. In this paper, we demonstrate a simple method to bridge this "reality gap". By randomizing the dynamics of the simulator during training, we are able to develop policies that are capable of adapting to very different dynamics, including ones that differ significantly from the dynamics on which the policies were trained. This adaptivity enables the policies to generalize to the dynamics of the real world without any training on the physical system. Our approach is demonstrated on an object pushing task using a robotic arm. Despite being trained exclusively in simulation, our policies are able to maintain a similar level of performance when deployed on a real robot, reliably moving an object to a desired location from random initial configurations. We explore the impact of various design decisions and show that the resulting policies are robust to significant calibration error