Kalman Filter Demystified: from Intuition to Probabilistic Graphical Model To
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MAGIC Summoning: Towards Automatic Suggesting and Testing of Gestures with Low Probability of False Positives During Use
JournalofMachineLearningResearch14(2013)209-242 Submitted 10/11; Revised 6/12; Published 1/13 MAGIC Summoning: Towards Automatic Suggesting and Testing of Gestures With Low Probability of False Positives During Use Daniel Kyu Hwa Kohlsdorf [email protected] Thad E. Starner [email protected] GVU & School of Interactive Computing Georgia Institute of Technology Atlanta, GA 30332 Editors: Isabelle Guyon and Vassilis Athitsos Abstract Gestures for interfaces should be short, pleasing, intuitive, and easily recognized by a computer. However, it is a challenge for interface designers to create gestures easily distinguishable from users’ normal movements. Our tool MAGIC Summoning addresses this problem. Given a specific platform and task, we gather a large database of unlabeled sensor data captured in the environments in which the system will be used (an “Everyday Gesture Library” or EGL). The EGL is quantized and indexed via multi-dimensional Symbolic Aggregate approXimation (SAX) to enable quick searching. MAGIC exploits the SAX representation of the EGL to suggest gestures with a low likelihood of false triggering. Suggested gestures are ordered according to brevity and simplicity, freeing the interface designer to focus on the user experience. Once a gesture is selected, MAGIC can output synthetic examples of the gesture to train a chosen classifier (for example, with a hidden Markov model). If the interface designer suggests his own gesture and provides several examples, MAGIC estimates how accurately that gesture can be recognized and estimates its false positive rate by comparing it against the natural movements in the EGL. We demonstrate MAGIC’s effectiveness in gesture selection and helpfulness in creating accurate gesture recognizers. -
A Neural Implementation of the Kalman Filter
A Neural Implementation of the Kalman Filter Robert C. Wilson Leif H. Finkel Department of Psychology Department of Bioengineering Princeton University University of Pennsylvania Princeton, NJ 08540 Philadelphia, PA 19103 [email protected] Abstract Recent experimental evidence suggests that the brain is capable of approximating Bayesian inference in the face of noisy input stimuli. Despite this progress, the neural underpinnings of this computation are still poorly understood. In this pa- per we focus on the Bayesian filtering of stochastic time series and introduce a novel neural network, derived from a line attractor architecture, whose dynamics map directly onto those of the Kalman filter in the limit of small prediction error. When the prediction error is large we show that the network responds robustly to changepoints in a way that is qualitatively compatible with the optimal Bayesian model. The model suggests ways in which probability distributions are encoded in the brain and makes a number of testable experimental predictions. 1 Introduction There is a growing body of experimental evidence consistent with the idea that animals are some- how able to represent, manipulate and, ultimately, make decisions based on, probability distribu- tions. While still unproven, this idea has obvious appeal to theorists as a principled way in which to understand neural computation. A key question is how such Bayesian computations could be per- formed by neural networks. Several authors have proposed models addressing aspects of this issue [15, 10, 9, 19, 2, 3, 16, 4, 11, 18, 17, 7, 6, 8], but as yet, there is no conclusive experimental evidence in favour of any one and the question remains open. -
Kalman and Particle Filtering
Abstract: The Kalman and Particle filters are algorithms that recursively update an estimate of the state and find the innovations driving a stochastic process given a sequence of observations. The Kalman filter accomplishes this goal by linear projections, while the Particle filter does so by a sequential Monte Carlo method. With the state estimates, we can forecast and smooth the stochastic process. With the innovations, we can estimate the parameters of the model. The article discusses how to set a dynamic model in a state-space form, derives the Kalman and Particle filters, and explains how to use them for estimation. Kalman and Particle Filtering The Kalman and Particle filters are algorithms that recursively update an estimate of the state and find the innovations driving a stochastic process given a sequence of observations. The Kalman filter accomplishes this goal by linear projections, while the Particle filter does so by a sequential Monte Carlo method. Since both filters start with a state-space representation of the stochastic processes of interest, section 1 presents the state-space form of a dynamic model. Then, section 2 intro- duces the Kalman filter and section 3 develops the Particle filter. For extended expositions of this material, see Doucet, de Freitas, and Gordon (2001), Durbin and Koopman (2001), and Ljungqvist and Sargent (2004). 1. The state-space representation of a dynamic model A large class of dynamic models can be represented by a state-space form: Xt+1 = ϕ (Xt,Wt+1; γ) (1) Yt = g (Xt,Vt; γ) . (2) This representation handles a stochastic process by finding three objects: a vector that l describes the position of the system (a state, Xt X R ) and two functions, one mapping ∈ ⊂ 1 the state today into the state tomorrow (the transition equation, (1)) and one mapping the state into observables, Yt (the measurement equation, (2)). -
Amigaos 3.2 FAQ 47.1 (09.04.2021) English
$VER: AmigaOS 3.2 FAQ 47.1 (09.04.2021) English Please note: This file contains a list of frequently asked questions along with answers, sorted by topics. Before trying to contact support, please read through this FAQ to determine whether or not it answers your question(s). Whilst this FAQ is focused on AmigaOS 3.2, it contains information regarding previous AmigaOS versions. Index of topics covered in this FAQ: 1. Installation 1.1 * What are the minimum hardware requirements for AmigaOS 3.2? 1.2 * Why won't AmigaOS 3.2 boot with 512 KB of RAM? 1.3 * Ok, I get it; 512 KB is not enough anymore, but can I get my way with less than 2 MB of RAM? 1.4 * How can I verify whether I correctly installed AmigaOS 3.2? 1.5 * Do you have any tips that can help me with 3.2 using my current hardware and software combination? 1.6 * The Help subsystem fails, it seems it is not available anymore. What happened? 1.7 * What are GlowIcons? Should I choose to install them? 1.8 * How can I verify the integrity of my AmigaOS 3.2 CD-ROM? 1.9 * My Greek/Russian/Polish/Turkish fonts are not being properly displayed. How can I fix this? 1.10 * When I boot from my AmigaOS 3.2 CD-ROM, I am being welcomed to the "AmigaOS Preinstallation Environment". What does this mean? 1.11 * What is the optimal ADF images/floppy disk ordering for a full AmigaOS 3.2 installation? 1.12 * LoadModule fails for some unknown reason when trying to update my ROM modules. -
Lecture 8 the Kalman Filter
EE363 Winter 2008-09 Lecture 8 The Kalman filter • Linear system driven by stochastic process • Statistical steady-state • Linear Gauss-Markov model • Kalman filter • Steady-state Kalman filter 8–1 Linear system driven by stochastic process we consider linear dynamical system xt+1 = Axt + But, with x0 and u0, u1,... random variables we’ll use notation T x¯t = E xt, Σx(t)= E(xt − x¯t)(xt − x¯t) and similarly for u¯t, Σu(t) taking expectation of xt+1 = Axt + But we have x¯t+1 = Ax¯t + Bu¯t i.e., the means propagate by the same linear dynamical system The Kalman filter 8–2 now let’s consider the covariance xt+1 − x¯t+1 = A(xt − x¯t)+ B(ut − u¯t) and so T Σx(t +1) = E (A(xt − x¯t)+ B(ut − u¯t))(A(xt − x¯t)+ B(ut − u¯t)) T T T T = AΣx(t)A + BΣu(t)B + AΣxu(t)B + BΣux(t)A where T T Σxu(t) = Σux(t) = E(xt − x¯t)(ut − u¯t) thus, the covariance Σx(t) satisfies another, Lyapunov-like linear dynamical system, driven by Σxu and Σu The Kalman filter 8–3 consider special case Σxu(t)=0, i.e., x and u are uncorrelated, so we have Lyapunov iteration T T Σx(t +1) = AΣx(t)A + BΣu(t)B , which is stable if and only if A is stable if A is stable and Σu(t) is constant, Σx(t) converges to Σx, called the steady-state covariance, which satisfies Lyapunov equation T T Σx = AΣxA + BΣuB thus, we can calculate the steady-state covariance of x exactly, by solving a Lyapunov equation (useful for starting simulations in statistical steady-state) The Kalman filter 8–4 Example we consider xt+1 = Axt + wt, with 0.6 −0.8 A = , 0.7 0.6 where wt are IID N (0, I) eigenvalues of A are -
Understanding and Mitigating the Security Risks of Apple Zeroconf
2016 IEEE Symposium on Security and Privacy Staying Secure and Unprepared: Understanding and Mitigating the Security Risks of Apple ZeroConf Xiaolong Bai*,1, Luyi Xing*,2, Nan Zhang2, XiaoFeng Wang2, Xiaojing Liao3, Tongxin Li4, Shi-Min Hu1 1TNList, Tsinghua University, Beijing, 2Indiana University Bloomington, 3Georgia Institute of Technology, 4Peking University [email protected], {luyixing, nz3, xw7}@indiana.edu, [email protected], [email protected], [email protected] Abstract—With the popularity of today’s usability-oriented tend to build their systems in a “plug-and-play” fashion, designs, dubbed Zero Configuration or ZeroConf, unclear are using techniques dubbed zero-configuration (ZeroConf ). For the security implications of these automatic service discovery, example, the AirDrop service on iPhone, once activated, “plug-and-play” techniques. In this paper, we report the first systematic study on this issue, focusing on the security features automatically detects another Apple device nearby running of the systems related to Apple, the major proponent of the service to transfer documents. Such ZeroConf services ZeroConf techniques. Our research brings to light a disturb- are characterized by automatic IP selection, host name ing lack of security consideration in these systems’ designs: resolving and target service discovery. Prominent examples major ZeroConf frameworks on the Apple platforms, includ- include Apple’s Bonjour [3], and the Link-Local Multicast ing the Core Bluetooth Framework, Multipeer Connectivity and -
Cooper ( Interaction Design
Cooper ( Interaction Design Inspiration The Myth of Metaphor Cooper books by Alan Cooper, Chairman & Founder Articles June 1995 Newsletter Originally Published in Visual Basic Programmer's Journal Concept projects Book reviews Software designers often speak of "finding the right metaphor" upon which to base their interface design. They imagine that rendering their user interface in images of familiar objects from the real world will provide a pipeline to automatic learning by their users. So they render their user interface as an office filled with desks, file cabinets, telephones and address books, or as a pad of paper or a street of buildings in the hope of creating a program with breakthrough ease-of- learning. And if you search for that magic metaphor, you will be in august company. Some of the best and brightest designers in the interface world put metaphor selection as one of their first and most important tasks. But by searching for that magic metaphor you will be making one of the biggest mistakes in user interface design. Searching for that guiding metaphor is like searching for the correct steam engine to power your airplane, or searching for a good dinosaur on which to ride to work. I think basing a user interface design on a metaphor is not only unhelpful but can often be quite harmful. The idea that good user interface design is based on metaphors is one of the most insidious of the many myths that permeate the software community. Metaphors offer a tiny boost in learnability to first time users at http://www.cooper.com/articles/art_myth_of_metaphor.htm (1 of 8) [1/16/2002 2:21:34 PM] Cooper ( Interaction Design tremendous cost. -
UC Santa Cruz UC Santa Cruz Electronic Theses and Dissertations
UC Santa Cruz UC Santa Cruz Electronic Theses and Dissertations Title Efficient Bug Prediction and Fix Suggestions Permalink https://escholarship.org/uc/item/47x1t79s Author Shivaji, Shivkumar Publication Date 2013 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA SANTA CRUZ EFFICIENT BUG PREDICTION AND FIX SUGGESTIONS A dissertation submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in COMPUTER SCIENCE by Shivkumar Shivaji March 2013 The Dissertation of Shivkumar Shivaji is approved: Professor Jim Whitehead, Chair Professor Jose Renau Professor Cormac Flanagan Tyrus Miller Vice Provost and Dean of Graduate Studies Copyright c by Shivkumar Shivaji 2013 Table of Contents List of Figures vi List of Tables vii Abstract viii Acknowledgments x Dedication xi 1 Introduction 1 1.1 Motivation . .1 1.2 Bug Prediction Workflow . .9 1.3 Bug Prognosticator . 10 1.4 Fix Suggester . 11 1.5 Human Feedback . 11 1.6 Contributions and Research Questions . 12 2 Related Work 15 2.1 Defect Prediction . 15 2.1.1 Totally Ordered Program Units . 16 2.1.2 Partially Ordered Program Units . 18 2.1.3 Prediction on a Given Software Unit . 19 2.2 Predictions of Bug Introducing Activities . 20 2.3 Predictions of Bug Characteristics . 21 2.4 Feature Selection . 24 2.5 Fix Suggestion . 25 2.5.1 Static Analysis Techniques . 25 2.5.2 Fix Content Prediction without Static Analysis . 26 2.6 Human Feedback . 28 iii 3 Change Classification 30 3.1 Workflow . 30 3.2 Finding Buggy and Clean Changes . -
Kalman-Filter Control Schemes for Fringe Tracking
A&A 541, A81 (2012) Astronomy DOI: 10.1051/0004-6361/201218932 & c ESO 2012 Astrophysics Kalman-filter control schemes for fringe tracking Development and application to VLTI/GRAVITY J. Menu1,2,3,, G. Perrin1,3, E. Choquet1,3, and S. Lacour1,3 1 LESIA, Observatoire de Paris, CNRS, UPMC, Université Paris Diderot, Paris Sciences et Lettres, 5 place Jules Janssen, 92195 Meudon, France 2 Instituut voor Sterrenkunde, KU Leuven, Celestijnenlaan 200D, 3001 Leuven, Belgium e-mail: [email protected] 3 Groupement d’Intérêt Scientifique PHASE (Partenariat Haute résolution Angulaire Sol Espace) between ONERA, Observatoire de Paris, CNRS and Université Paris Diderot, France Received 31 January 2012 / Accepted 28 February 2012 ABSTRACT Context. The implementation of fringe tracking for optical interferometers is inevitable when optimal exploitation of the instrumental capacities is desired. Fringe tracking allows continuous fringe observation, considerably increasing the sensitivity of the interfero- metric system. In addition to the correction of atmospheric path-length differences, a decent control algorithm should correct for disturbances introduced by instrumental vibrations, and deal with other errors propagating in the optical trains. Aims. In an effort to improve upon existing fringe-tracking control, especially with respect to vibrations, we attempt to construct con- trol schemes based on Kalman filters. Kalman filtering is an optimal data processing algorithm for tracking and correcting a system on which observations are performed. As a direct application, control schemes are designed for GRAVITY, a future four-telescope near-infrared beam combiner for the Very Large Telescope Interferometer (VLTI). Methods. We base our study on recent work in adaptive-optics control. -
The Unscented Kalman Filter for Nonlinear Estimation
The Unscented Kalman Filter for Nonlinear Estimation Eric A. Wan and Rudolph van der Merwe Oregon Graduate Institute of Science & Technology 20000 NW Walker Rd, Beaverton, Oregon 97006 [email protected], [email protected] Abstract 1. Introduction The Extended Kalman Filter (EKF) has become a standard The EKF has been applied extensively to the field of non- technique used in a number of nonlinear estimation and ma- linear estimation. General application areas may be divided chine learning applications. These include estimating the into state-estimation and machine learning. We further di- state of a nonlinear dynamic system, estimating parame- vide machine learning into parameter estimation and dual ters for nonlinear system identification (e.g., learning the estimation. The framework for these areas are briefly re- weights of a neural network), and dual estimation (e.g., the viewed next. Expectation Maximization (EM) algorithm) where both states and parameters are estimated simultaneously. State-estimation This paper points out the flaws in using the EKF, and The basic framework for the EKF involves estimation of the introduces an improvement, the Unscented Kalman Filter state of a discrete-time nonlinear dynamic system, (UKF), proposed by Julier and Uhlman [5]. A central and (1) vital operation performed in the Kalman Filter is the prop- (2) agation of a Gaussian random variable (GRV) through the system dynamics. In the EKF, the state distribution is ap- where represent the unobserved state of the system and proximated by a GRV, which is then propagated analyti- is the only observed signal. The process noise drives cally through the first-order linearization of the nonlinear the dynamic system, and the observation noise is given by system. -
1 the Kalman Filter
Macroeconometrics, Spring, 2019 Bent E. Sørensen August 20, 2019 1 The Kalman Filter We assume that we have a model that concerns a series of vectors αt, which are called \state vectors". These variables are supposed to describe the current state of the system in question. These state variables will typically not be observed and the other main ingredient is therefore the observed variables yt. The first step is to write the model in state space form which is in the form of a linear system which consists of 2 sets of linear equations. The first set of equations describes the evolution of the system and is called the \Transition Equation": αt = Kαt−1 + Rηt ; where K and R are matrices of constants and η is N(0;Q) and serially uncorrelated. (This setup allows for a constant also.) The second set of equations describes the relation between the state of the system and the observations and is called the \Measurement Equation": yt = Zαt + ξt ; where ξt is N(0;H), serially uncorrelated, and E(ξtηt−j) = 0 for all t and j. It turns out that a lot of models can be put in the state space form with a little imagination. The main restriction is of course on the linearity of the model while you may not care about the normality condition as you will still be doing least squares. The state-space model as it is defined here is not the most general possible|it is in principle easy to allow for non-stationary coefficient matrices, see for example Harvey(1989). -
Automatic Program Repair Using Genetic Programming
Automatic Program Repair Using Genetic Programming A Dissertation Presented to the Faculty of the School of Engineering and Applied Science University of Virginia In Partial Fulfillment of the requirements for the Degree Doctor of Philosophy (Computer Science) by Claire Le Goues May 2013 c 2013 Claire Le Goues Abstract Software quality is an urgent problem. There are so many bugs in industrial program source code that mature software projects are known to ship with both known and unknown bugs [1], and the number of outstanding defects typically exceeds the resources available to address them [2]. This has become a pressing economic problem whose costs in the United States can be measured in the billions of dollars annually [3]. A dominant reason that software defects are so expensive is that fixing them remains a manual process. The process of identifying, triaging, reproducing, and localizing a particular bug, coupled with the task of understanding the underlying error, identifying a set of code changes that address it correctly, and then verifying those changes, costs both time [4] and money. Moreover, the cost of repairing a defect can increase by orders of magnitude as development progresses [5]. As a result, many defects, including critical security defects [6], remain unaddressed for long periods of time [7]. Moreover, humans are error-prone, and many human fixes are imperfect, in that they are either incorrect or lead to crashes, hangs, corruption, or security problems [8]. As a result, defect repair has become a major component of software maintenance, which in turn consumes up to 90% of the total lifecycle cost of a given piece of software [9].