arXiv subjects

Michael Burke

Publications and source records attributed to Michael Burke.

43 records · Page 3Linked to original sources

arXiv2019

From explanation to synthesis: Compositional program induction for learning from demonstration

Hybrid systems are a compact and natural mechanism with which to address problems in robotics. This work introduces an approach to learning hybrid systems from demonstrations, with an emphasis on extracting models that are explicitly verifiable and easily interpreted by robot operators. We fit a sequence of controllers using sequential importance sampling under a generative switching proportional controller task model. Here, we parameterise controllers using a proportional gain and a visually verifiable joint angle goal. Inference under this model is challenging, but we address this by introducing an attribution prior extracted from a neural end-to-end visuomotor control model. Given the sequence of controllers comprising a task, we simplify the trace using grammar parsing strategies, taking advantage of the sequence compositionality, before grounding the controllers by training perception networks to predict goals given images. Using this approach, we are successfully able to induce a program for a visuomotor reaching task involving loops and conditionals from a single demonstration and a neural end-to-end model. In addition, we are able to discover the program used for a tower building task. We argue that computer program-like control systems are more interpretable than alternative end-to-end learning approaches, and that hybrid systems inherently allow for better generalisation across task configurations.

cs.RO↗

arXiv2019

Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms

Convolutional Neural Networks (CNNs) have been used successfully across a broad range of areas including data mining, object detection, and in business. The dominance of CNNs follows a breakthrough by Alex Krizhevsky which showed improvements by dramatically reducing the error rate obtained in a general image classification task from 26.2% to 15.4%. In road safety, CNNs have been applied widely to the detection of traffic signs, obstacle detection, and lane departure checking. In addition, CNNs have been used in data mining systems that monitor driving patterns and recommend rest breaks when appropriate. This paper presents a driver drowsiness detection system and shows that there are potential social challenges regarding the application of these techniques, by highlighting problems in detecting dark-skinned driver's faces. This is a particularly important challenge in African contexts, where there are more dark-skinned drivers. Unfortunately, publicly available datasets are often captured in different cultural contexts, and therefore do not cover all ethnicities, which can lead to false detections or racially biased models. This work evaluates the performance obtained when training convolutional neural network models on commonly used driver drowsiness detection datasets and testing on datasets specifically chosen for broader representation. Results show that models trained using publicly available datasets suffer extensively from over-fitting, and can exhibit racial bias, as shown by testing on a more representative dataset. We propose a novel visualisation technique that can assist in identifying groups of people where there might be the potential of discrimination, using Principal Component Analysis (PCA) to produce a grid of faces sorted by similarity, and combining these with a model accuracy overlay.

cs.CV↗

arXiv2019

DepthwiseGANs: Fast Training Generative Adversarial Networks for Realistic Image Synthesis

Recent work has shown significant progress in the direction of synthetic data generation using Generative Adversarial Networks (GANs). GANs have been applied in many fields of computer vision including text-to-image conversion, domain transfer, super-resolution, and image-to-video applications. In computer vision, traditional GANs are based on deep convolutional neural networks. However, deep convolutional neural networks can require extensive computational resources because they are based on multiple operations performed by convolutional layers, which can consist of millions of trainable parameters. Training a GAN model can be difficult and it takes a significant amount of time to reach an equilibrium point. In this paper, we investigate the use of depthwise separable convolutions to reduce training time while maintaining data generation performance. Our results show that a DepthwiseGAN architecture can generate realistic images in shorter training periods when compared to a StarGan architecture, but that model capacity still plays a significant role in generative modelling. In addition, we show that depthwise separable convolutions perform best when only applied to the generator. For quality evaluation of generated images, we use the Fréchet Inception Distance (FID), which compares the similarity between the generated image distribution and that of the training dataset.

cs.CV↗

arXiv2015

Finding Tizen security bugs through whole-system static analysis

Tizen is a new Linux-based open source platform for consumer devices including smartphones, televisions, vehicles, and wearables. While Tizen provides kernel-level mandatory policy enforcement, it has a large collection of libraries, implemented in a mix of C and C++, which make their own security checks. In this research, we describe the design and engineering of a static analysis engine which drives a full information flow analysis for apps and a control flow analysis for the full library stack. We implemented these static analyses as extensions to LLVM, requiring us to improve LLVM's native analysis features to get greater precision and scalability, including knotty issues like the coexistence of C++ inheritance with C function pointer use. With our tools, we found several unexpected behaviors in the Tizen system, including paths through the system libraries that did not have inline security checks. We show how our tools can help the Tizen app store to verify important app properties as well as helping the Tizen development process avoid the accidental introduction of subtle vulnerabilities.

cs.CR↗

arXiv2014

Single camera pose estimation using Bayesian filtering and Kinect motion priors

Traditional approaches to upper body pose estimation using monocular vision rely on complex body models and a large variety of geometric constraints. We argue that this is not ideal and somewhat inelegant as it results in large processing burdens, and instead attempt to incorporate these constraints through priors obtained directly from training data. A prior distribution covering the probability of a human pose occurring is used to incorporate likely human poses. This distribution is obtained offline, by fitting a Gaussian mixture model to a large dataset of recorded human body poses, tracked using a Kinect sensor. We combine this prior information with a random walk transition model to obtain an upper body model, suitable for use within a recursive Bayesian filtering framework. Our model can be viewed as a mixture of discrete Ornstein-Uhlenbeck processes, in that states behave as random walks, but drift towards a set of typically observed poses. This model is combined with measurements of the human head and hand positions, using recursive Bayesian estimation to incorporate temporal information. Measurements are obtained using face detection and a simple skin colour hand detector, trained using the detected face. The suggested model is designed with analytical tractability in mind and we show that the pose tracking can be Rao-Blackwellised using the mixture Kalman filter, allowing for computational efficiency while still incorporating bio-mechanical properties of the upper body. In addition, the use of the proposed upper body model allows reliable three-dimensional pose estimates to be obtained indirectly for a number of joints that are often difficult to detect using traditional object recognition strategies. Comparisons with Kinect sensor results and the state of the art in 2D pose estimation highlight the efficacy of the proposed approach.

cs.CV↗

arXiv2012

MMT & Magellan Infrared Spectrograph

The MMT and Magellan infrared spectrograph (MMIRS) is a cryogenic multiple slit spectrograph operating in the wavelength range 0.9-2.4 micron. MMIRS' refractive optics offer a 6.9 by 6.9 arcmin field of view for imaging with a spatial resolution of 0.2 arcsec per pixel on a HAWAII-2 array. For spectroscopy, MMIRS can be used with long slits up to 6.9 arcmin long, or with custom slit masks having slitlets distributed over a 4 by 6.9 arcmin area. A range of dispersers offer spectral resolutions of 800 to 3000. MMIRS is designed to be used at the f/5 foci of the MMT or Magellan Clay 6.5m telescopes. MMIRS was commissioned in 2009 at the MMT and has been in routine operation at the Magellan Clay Telescope since 2010. MMIRS is being used for a wide range of scientific investigations from exoplanet atmospheres to Ly-alpha emitters.

astro-ph.IM↗

arXiv2010

Distributed Fault-Tolerant Avionic Systems - A Real-Time Perspective

This paper examines the problem of introducing advanced forms of fault-tolerance via reconfiguration into safety-critical avionic systems. This is required to enable increased availability after fault occurrence in distributed integrated avionic systems(compared to static federated systems). The approach taken is to identify a migration path from current architectures to those that incorporate re-configuration to a lesser or greater degree. Other challenges identified include change of the development process; incremental and flexible timing and safety analyses; configurable kernels applicable for safety-critical systems.

cs.DC↗