1,405 research outputs found
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
Active authentication refers to the process in which users are unobtrusively
monitored and authenticated continuously throughout their interactions with
mobile devices. Generally, an active authentication problem is modelled as a
one class classification problem due to the unavailability of data from the
impostor users. Normally, the enrolled user is considered as the target class
(genuine) and the unauthorized users are considered as unknown classes
(impostor). We propose a convolutional neural network (CNN) based approach for
one class classification in which a zero centered Gaussian noise and an
autoencoder are used to model the pseudo-negative class and to regularize the
network to learn meaningful feature representations for one class data,
respectively. The overall network is trained using a combination of the
cross-entropy and the reconstruction error losses. A key feature of the
proposed approach is that any pre-trained CNN can be used as the base network
for one class classification. Effectiveness of the proposed framework is
demonstrated using three publically available face-based active authentication
datasets and it is shown that the proposed method achieves superior performance
compared to the traditional one class classification methods. The source code
is available at: github.com/otkupjnoz/oc-acnn.Comment: Accepted and to appear at AFGR 201
C2AE: Class Conditioned Auto-Encoder for Open-set Recognition
Models trained for classification often assume that all testing classes are
known while training. As a result, when presented with an unknown class during
testing, such closed-set assumption forces the model to classify it as one of
the known classes. However, in a real world scenario, classification models are
likely to encounter such examples. Hence, identifying those examples as unknown
becomes critical to model performance. A potential solution to overcome this
problem lies in a class of learning problems known as open-set recognition. It
refers to the problem of identifying the unknown classes during testing, while
maintaining performance on the known classes. In this paper, we propose an
open-set recognition algorithm using class conditioned auto-encoders with novel
training and testing methodology. In contrast to previous methods, training
procedure is divided in two sub-tasks, 1. closed-set classification and, 2.
open-set identification (i.e. identifying a class as known or unknown). Encoder
learns the first task following the closed-set classification training
pipeline, whereas decoder learns the second task by reconstructing conditioned
on class identity. Furthermore, we model reconstruction errors using the
Extreme Value Theory of statistical modeling to find the threshold for
identifying known/unknown class samples. Experiments performed on multiple
image classification datasets show proposed method performs significantly
better than state of the art.Comment: CVPR2019 (Oral
Bacteriological profile and antibiogram of blood culture isolates from patients of rural tertiary care hospital
Microbial invasion of blood stream is associated with significant mortality and morbidity. Identification of bacterial isolates and antibiotic susceptibility of bacteria isolated from blood culture would guide the antibiotics treatment for patients with bacteremia. 1) To determine age – wise blood culture positivity rate in bacteremia 2) To identify age – wise common bacterial species isolates in bacteremia 3) To determine Antibiotic sensitivity pattern of the bacterial isolates. Atotal of 247 blood culture samples received from various clinical departments of rural teaching hospital from August 2013 to September 2015 were included in the study. Samples were collected in brain heart infusion broth. Identification of isolates and antimicrobial susceptibility was done as per standard microbiological methods. Out of 247 specimens bacteria sp. was isolated from 46 (18.62%) samples. Blood culture positivity was noted highest among neonates age group (38.71%). Lowest rate was observed among elders (4.55%). Klebsiella pneumoniae, Coagulase negative staphylococcus (CONs), and S. aureus were common blood culture isolates. In neonates Klebsiella pneumoniae was the most common isolate. Out of 27 gram negative bacilli, 14 (51.85%) were extended spectrum betalactamases (ESBL) positive. High resistance was noted against amoxycillin and amoxicillin/clavulanic acid and third generation cephalosporins in all gram negative organisms except, S. typhi. Out of 12 Staphylococcus sp., none of these were methicillin resistant. Routine antibiotic susceptibility surveillance helps in choice of antibiotics for treatment, identification of resistance and control of its spread. Published by the International journal of Microbiology and Mycology (IJMM
Continuous Uniform Finite Time Stabilization of Planar Controllable Systems
Continuous homogeneous controllers are utilized in a full state feedback setting for the uniform finite time stabilization of a perturbed double integrator in the presence of uniformly decaying piecewise continuous disturbances. Semiglobal strong Lyapunov functions are identified to establish uniform asymptotic stability of the closed-loop planar system. Uniform finite time stability is then proved by extending the homogeneity principle of discontinuous systems to the continuous case with uniformly decaying piecewise continuous nonhomogeneous disturbances. A finite upper bound on the settling time is also computed. The results extend the existing literature on homogeneity and finite time stability by both presenting uniform finite time stabilization and dealing with a broader class of nonhomogeneous disturbances for planar controllable systems while also proposing a new class of homogeneous continuous controllers
International Court of Justice for Animal Rights, held at the International Conference Centre, Place Varembé, Geneva, Switzerland, on 6 March 1995
Simulations of pilot-wave dynamics in a simple harmonic potential
We present the results of a numerical investigation of droplets walking in a harmonic potential on a vibrating fluid bath. The droplet's trajectory is described by an integro-differential equation, which is simulated numerically in various parameter regimes. We produce a regime diagram that summarizes the dependence of the walker's behavior on the system parameters for a droplet of fixed size. At relatively low vibrational forcing, a number of periodic and quasiperiodic trajectories emerge. In the limit of large vibrational forcing, the walker's trajectory becomes chaotic, but the resulting trajectories can be decomposed into portions of unstable quasiperiodic states.National Science Foundation (U.S.) (Grant CMMI-1333242)National Science Foundation (U.S.) (Grant DMS-1614043
Adversarially Robust One-class Novelty Detection
One-class novelty detectors are trained with examples of a particular class
and are tasked with identifying whether a query example belongs to the same
known class. Most recent advances adopt a deep auto-encoder style architecture
to compute novelty scores for detecting novel class data. Deep networks have
shown to be vulnerable to adversarial attacks, yet little focus is devoted to
studying the adversarial robustness of deep novelty detectors. In this paper,
we first show that existing novelty detectors are susceptible to adversarial
examples. We further demonstrate that commonly-used defense approaches for
classification tasks have limited effectiveness in one-class novelty detection.
Hence, we need a defense specifically designed for novelty detection. To this
end, we propose a defense strategy that manipulates the latent space of novelty
detectors to improve the robustness against adversarial examples. The proposed
method, referred to as Principal Latent Space (PrincipaLS), learns the
incrementally-trained cascade principal components in the latent space to
robustify novelty detectors. PrincipaLS can purify latent space against
adversarial examples and constrain latent space to exclusively model the known
class distribution. We conduct extensive experiments on eight attacks, five
datasets and seven novelty detectors, showing that PrincipaLS consistently
enhances the adversarial robustness of novelty detection models. Code is
available at https://github.com/shaoyuanlo/PrincipaLSComment: Accepted in IEEE Transactions on Pattern Analysis and Machine
Intelligence (T-PAMI), 202
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