scholarly journals Design Privacy with Analogia Graph

2021 ◽  
Vol 24 (2) ◽  
pp. 1769-1774
Author(s):  
Yang Cai ◽  
Joseph Laws ◽  
Nathaniel Bauernfeind
Keyword(s):  
Data Privacy ◽  
Search Space ◽  
Basis Functions ◽  
Human Vision ◽  
Surface Rendering ◽  
Model Data ◽  
Order Of Magnitude ◽  
Data Points ◽  
Relative Measurements ◽  
Privacy Measurement

Human vision is often guided by instinctual commonsense such as proportions and contours. In this paper, we explore how to use the proportion as the key knowledge for designing a privacy algorithm that detects human private parts in a 3D scan dataset. The Analogia Graph is introduced to study the proportion of structures. It is a graph-based representation of the proportion knowledge. The intrinsic human proportions are applied to reduce the search space by an order of magnitude. A feature shape template is constructed to match the model data points using Radial Basis Functions in a non-linear regression and the relative measurements of the height and area factors. The method is tested on 100 datasets from CAESAR database. Two surface rendering methods are studied for data privacy: blurring and transparency. It is found that test subjects normally prefer to have the most possible privacy in both rendering methods. However, the subjects adjusted their privacy measurement to a certain degree as they were informed the context of security.

Feature Hiding in 3D Human Body Scans

2006 ◽  
Vol 5 (4) ◽  
pp. 271-278 ◽  
Author(s):  
Joseph Laws ◽  
Nathaniel Bauernfeind ◽  
Yang Cai
Keyword(s):  
Data Privacy ◽  
Search Space ◽  
Data Sets ◽  
Surface Rendering ◽  
Data Set ◽  
Order Of Magnitude ◽  
Data Points ◽  
Scan Data ◽  
Relative Measurements ◽  
Privacy Measurement

In this paper, we explore a privacy algorithm that detects human private parts in a 3D scan data set. The analogia graph is introduced to study the proportion of structures. The intrinsic human proportions are applied to reduce the search space in an order of magnitude. A feature shape template is constructed to match the model data points using Radial Basis Functions in a non-linear regression and the relative measurements of the height and area factors. The method is tested on 100 data sets from CAESAR database. Two surface rendering methods are studied for data privacy: blurring and transparency. It is found that test subjects normally prefer to have the most possible privacy in both rendering methods. However, the subjects adjusted their privacy measurement to a certain degree as they were informed of the context of security.

Interpolation and extrapolation

2018 ◽  
Author(s):  
Joseph F. Boudreau ◽  
Eric S. Swanson
Keyword(s):  
Public Transportation ◽  
Spline Interpolation ◽  
Lattice Gauge Theory ◽  
Human Vision ◽  
Physical Sciences ◽  
One Dimensional ◽  
Lattice Gauge ◽  
Multidimensional Problems ◽  
Data Points ◽  
Lagrange Interpolating Polynomial

This chapter deals with two related problems occurring frequently in the physical sciences: first, the problem of estimating the value of a function from a limited number of data points; and second, the problem of calculating its value from a series approximation. Numerical methods for interpolating and extrapolating data are presented. The famous Lagrange interpolating polynomial is introduced and applied to one-dimensional and multidimensional problems. Cubic spline interpolation is introduced and an implementation in terms of Eigen classes is given. Several techniques for improving the convergence of Taylor series are discussed, including Shank’s transformation, Richardson extrapolation, and the use of Padé approximants. Conversion between representations with the quotient-difference algorithm is discussed. The exercises explore public transportation, human vision, the wine market, and SU(2) lattice gauge theory, among other topics.

scholarly journals Best-first Enumeration Based on Bounding Conflicts, and its Application to Large-scale Hybrid Estimation (Extended Abstract)

2020 ◽  
Author(s):  
Eric Timmons ◽  
Brian C. Williams
Keyword(s):  
Large Scale ◽  
Search Space ◽  
Estimation Methods ◽  
Directed Search ◽  
Hybrid Estimation ◽  
Continuous State ◽  
Order Of Magnitude ◽  
State Models ◽  
The Cost ◽  
Belief States

State estimation methods based on hybrid discrete and continuous state models have emerged as a method of precisely computing belief states for real world systems, however they have difficulty scaling to systems with more than a handful of components. Classical, consistency based diagnosis methods scale to this level by combining best-first enumeration and conflict-directed search. While best-first methods have been developed for hybrid estimation, conflict-directed methods have thus far been elusive as conflicts summarize constraint violations, but probabilistic hybrid estimation is relatively unconstrained. In this paper we present an approach (A*BC) that unifies best-first enumeration and conflict-directed search in relatively unconstrained problems through the concept of "bounding" conflicts, an extension of conflicts that represent tighter bounds on the cost of regions of the search space. Experiments show that an A*BC powered state estimator produces estimates up to an order of magnitude faster than the current state of the art, particularly on large systems.

Borehole dc resistivity response for a transitional invaded zone

Geophysics ◽  
1994 ◽  
Vol 59 (12) ◽  
pp. 1796-1805 ◽  
Author(s):  
K. K. Roy ◽  
D. J. Dutta
Keyword(s):  
Separation Of Variables ◽  
Electrode Array ◽  
Forward Model ◽  
Scale Model ◽  
Model Data ◽  
Mud Cake ◽  
Alternative Approach ◽  
Data Points ◽  
Invaded Zone ◽  
D Model

A borehole direct‐current resistivity boundary value problem for normal and lateral elctrode configuratin is soved assuming axial symmetry. The borehole mud, a flushed zone, an invaded zone, and an unciontaminated zone are all assumed to be present. A linear transition in resistivity is assumed for the invaded zone. Frobenius extended power series and the method of separation of variables are used to solved the 1-D problem. Single-run borehole resistivity sounding and solution of the inverse problem are suggested fo estimatingthe resisitivity of the uncontaminated zone and the radius of invasion. Finite‐difference modeling is dione to estimate the effect of shoulder beds ion borehole sounding. Some of the computed 1-D and 2-D model apparent reisivity curves are compared with the existing scale model data. The analysis reveals that the mud cake effect is negligible for normal and lateral electrode array and the invasion zone thickness is feflected in the forward models. Apparent resistivity curves with and without a transitional invaded zone are well separated. Resistivity departure curves are well separated for fixed resistivity and variable resistivity invaded zone models. A normal electrode configuration can feel the presence of the shoulder bed in a 2-D model when the bed thickness is about 12 time the electrode separation. One‐dimensional ridge regression inversion the synthetic forward model data is presented to suggest an alternative approach for determining the resistivey of the uncontaminated zone ([Formula: see text]) and the radius of invasion [Formula: see text]. We conclude that (1) a single run borehole sounding with 10 or 12 data points from a normal or lateral log may be used, rather than 3 points from a dual laterolog [Formula: see text] tool, for better estimation of [Formula: see text], and (2) a borehole forward model should include a transitional invaded zone. Finally, an alternative approach for the estimation of the radius of invasion is proposed.

The Discrimination of Dynamic Orientation Changes in Gratings

Perception ◽  
1995 ◽  
Vol 24 (6) ◽  
pp. 665-679 ◽  
Author(s):  
Michael J Wright ◽  
Kevin N Gurney
Keyword(s):  
Spatial Frequency ◽  
Visual Memory ◽  
Frequency Function ◽  
Short Isis ◽  
Angular Rotation ◽  
Spatial Frequencies ◽  
Straight Line ◽  
Order Of Magnitude ◽  
Data Points ◽  
Best Fitting

Thresholds were measured for discrimination of direction of a step angular rotation of gratings. The addition of simultaneous phase displacements (translation) had little effect on rotation thresholds for gratings over a considerable range; discrimination of rotation is unaffected by random directional translations an order of magnitude larger. Angular rotation discrimination thresholds increased with interstimulus interval (ISI). Thus discrimination is based at short ISIs (180 ms or less) on a percept of rotary motion, but at ISIs of several seconds by a spatial strategy (comparing static component orientations) relying on visual memory. Data points for the short-ISI region fell below the best-fitting straight line, and the slope of the short-ISI region of the curve was steeper than that of the long-ISI region. However, when either compound or simple gratings with uncorrelated spatial frequencies were used in the two stimulus frames, there was no evidence for a separate function at short ISIs. Orientation-change thresholds were measured for simple gratings as a function of contrast and spatial frequency. The contrast function showed saturation and the spatial frequency function was U-shaped. Rotation sensitivity for gratings is thus similar in its spatiotemporal properties to translation sensitivity. The findings support the proposal that rotation discrimination (at short ISIs) is achieved by a template mechanism combining signals from different directional detectors, rather than by cognitive comparison of the outputs of the directional mechanisms themselves.

scholarly journals Search Space Reduction for Determination of Earthquake Source Parameters Using PCA and k-Means Clustering

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Seongjae Lee ◽  
Taehyoun Kim
Keyword(s):  
Monte Carlo ◽  
Surface Deformation ◽  
Source Parameters ◽  
Search Space ◽  
Dislocation Model ◽  
Space Reduction ◽  
Parameter Search ◽  
Search Space Reduction ◽  
Data Points ◽  
Parameter Search Space

The characteristics of an earthquake can be derived by estimating the source geometries of the earthquake using parameter inversion that minimizes the L2 norm of residuals between the measured and the synthetic displacement calculated from a dislocation model. Estimating source geometries in a dislocation model has been regarded as solving a nonlinear inverse problem. To avoid local minima and describe uncertainties, the Monte-Carlo restarts are often used to solve the problem, assuming the initial parameter search space provided by seismological studies. Since search space size significantly affects the accuracy and execution time of this procedure, faulty initial search space from seismological studies may adversely affect the accuracy of the results and the computation time. Besides, many source parameters describing physical faults lead to bad data visualization. In this paper, we propose a new machine learning-based search space reduction algorithm to overcome these challenges. This paper assumes a rectangular dislocation model, i.e., the Okada model, to calculate the surface deformation mathematically. As for the geodetic measurement of three-dimensional (3D) surface deformation, we used the stacking interferometric synthetic aperture radar (InSAR) and the multiple-aperture SAR interferometry (MAI). We define a wide initial search space and perform the Monte-Carlo restarts to collect the data points with root-mean-square error (RMSE) between measured and modeled displacement. Then, the principal component analysis (PCA) and the k-means clustering are used to project data points with low RMSE in the 2D latent space preserving the variance of original data as much as possible and extract k clusters of data with similar locations and RMSE to each other. Finally, we reduce the parameter search space using the cluster with the lowest mean RMSE. The evaluation results illustrate that our approach achieves 55.1~98.1% reductions in search space size and 60~80.5% reductions in 95% confidence interval size for all source parameters compared with the conventional method. It was also observed that the reduced search space significantly saves the computational burden of solving the nonlinear least square problem.

An Universal Adversarial Attack Method Based on Spherical Projection

2021 ◽  
pp. 2250038
Author(s):  
Chunlong Fan ◽  
Zhimin Zhang ◽  
Jianzhong Qiao
Keyword(s):  
Search Space ◽  
Stochastic Gradient ◽  
Dimensional Sphere ◽  
Generation Algorithm ◽  
The Neural Network ◽  
Order Of Magnitude ◽  
Adversarial Attack ◽  
Baseline Algorithm ◽  
Solution Efficiency ◽  
Spherical Projection

Adversarial attack on neural networks has become an important problem restricting its security applications, and among adversarial attacks oriented towards the sample set, the universal perturbation design causing most sample output errors is critical to the study. This paper takes the neural network for image classification as the research object, summarizes the existing universal perturbation generation algorithm, proposes a universal perturbation generation algorithm combining batch stochastic gradient rise and spherical projection search, achieves loss function reduction through the iterative training of stochastic gradient rise in batch samples, and limits the universal perturbation search to a high-dimensional sphere with radius [Formula: see text] to reduce the search space of universal perturbation. Moreover, the regularized technology is introduced to improve the generation quality of universal perturbations. The experimental results show that compared with the baseline algorithm, the attack success rate increases by more than 10%, the solution efficiency of universal perturbation is improved by one order of magnitude, and the quality controllability of universal perturbation is better.

scholarly journals Optimum Dataset Size and Search Space for Minimum Zone Roundness Evaluation by Genetic Algorithm

2013 ◽  
Vol 13 (3) ◽  
pp. 100-107 ◽  
Author(s):  
A. Meo ◽  
L. Profumo ◽  
A. Rossi ◽  
M. Lanzetta
Keyword(s):  
Genetic Algorithm ◽  
Computation Time ◽  
Experimental Tests ◽  
Estimation Algorithm ◽  
Search Space ◽  
Inspection Time ◽  
Profile Measurement ◽  
Dataset Size ◽  
Order Of Magnitude ◽  
Minimum Zone

Roundness is one of the most common features in machining. The minimum zone tolerance (MZT) approach provides the minimum roundness error, i.e. the minimum distance between the two concentric reference circles containing the acquired profile; more accurate form error estimation results in less false part rejections. MZT is still an open problem and is approached here by a Genetic Algorithm. Only few authors have addressed the definition of the search space center and size and its relationship with the dataset size, which greatly influence the inspection time for the profile measurement and the convergence speed of the roundness estimation algorithm for a given target accuracy. Experimental tests on certified roundness profiles, using the profile centroid as the search space center, have shown that the search space size is related to the number of dataset points and an optimum exists, which provides a computation time reduction up to an order of magnitude.

Plastic Flow of Solid Mixtures of Li2SO4 and K2SO4

1966 ◽  
Vol 21 (5) ◽  
pp. 593-594
Author(s):  
Arnold Lundén ◽  
Björn Jonson ◽  
Bengt Augustsson
Keyword(s):  
Electrical Conductivity ◽  
Plastic Flow ◽  
Penetration Rate ◽  
Cation Transport ◽  
Considerable Change ◽  
Thermal Pretreatment ◽  
Self Diffusion ◽  
Order Of Magnitude ◽  
Relative Measurements ◽  
High Temperature Modification

The addition of K2SO4 to Li2SO4 causes a considerable change in the rheological properties of the cubic high temperature modification. A simple device for relative measurements of the plastic flow of the salt consisted of a sphere of stainless steel at the end of a steel rod on top of which weights were hung in order to obtain a suitable penetration rate through the salt. This rate depended on the composition of the mixture as well as on the thermal pretreatment of the salt. The temperature dependence was strong; a crude estimation gave an “activation energy” of the order of 2 x 105 cal/mole, i. e. more than an order of magnitude higher than for electrical conductivity or cation self-diffusion. This result is in agreement with the interpretation of the electrical conductivity as being due solely to cation transport.

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