7,957 research outputs found
Minimax Iterative Dynamic Game: Application to Nonlinear Robot Control Tasks
Multistage decision policies provide useful control strategies in
high-dimensional state spaces, particularly in complex control tasks. However,
they exhibit weak performance guarantees in the presence of disturbance, model
mismatch, or model uncertainties. This brittleness limits their use in
high-risk scenarios. We present how to quantify the sensitivity of such
policies in order to inform of their robustness capacity. We also propose a
minimax iterative dynamic game framework for designing robust policies in the
presence of disturbance/uncertainties. We test the quantification hypothesis on
a carefully designed deep neural network policy; we then pose a minimax
iterative dynamic game (iDG) framework for improving policy robustness in the
presence of adversarial disturbances. We evaluate our iDG framework on a
mecanum-wheeled robot, whose goal is to find a ocally robust optimal multistage
policy that achieve a given goal-reaching task. The algorithm is simple and
adaptable for designing meta-learning/deep policies that are robust against
disturbances, model mismatch, or model uncertainties, up to a disturbance
bound. Videos of the results are on the author's website,
http://ecs.utdallas.edu/~opo140030/iros18/iros2018.html, while the codes for
reproducing our experiments are on github,
https://github.com/lakehanne/youbot/tree/rilqg. A self-contained environment
for reproducing our results is on docker,
https://hub.docker.com/r/lakehanne/youbotbuntu14/Comment: 2018 International Conference on Intelligent Robots and System
Scalable Bayesian model averaging through local information propagation
We show that a probabilistic version of the classical forward-stepwise
variable inclusion procedure can serve as a general data-augmentation scheme
for model space distributions in (generalized) linear models. This latent
variable representation takes the form of a Markov process, thereby allowing
information propagation algorithms to be applied for sampling from model space
posteriors. In particular, we propose a sequential Monte Carlo method for
achieving effective unbiased Bayesian model averaging in high-dimensional
problems, utilizing proposal distributions constructed using local information
propagation. We illustrate our method---called LIPS for local information
propagation based sampling---through real and simulated examples with
dimensionality ranging from 15 to 1,000, and compare its performance in
estimating posterior inclusion probabilities and in out-of-sample prediction to
those of several other methods---namely, MCMC, BAS, iBMA, and LASSO. In
addition, we show that the latent variable representation can also serve as a
modeling tool for specifying model space priors that account for knowledge
regarding model complexity and conditional inclusion relationships
An Exploratory Study of Patient Falls
Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged â„ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body
Towards the Model-Driven Engineering of Secure yet Safe Embedded Systems
We introduce SysML-Sec, a SysML-based Model-Driven Engineering environment
aimed at fostering the collaboration between system designers and security
experts at all methodological stages of the development of an embedded system.
A central issue in the design of an embedded system is the definition of the
hardware/software partitioning of the architecture of the system, which should
take place as early as possible. SysML-Sec aims to extend the relevance of this
analysis through the integration of security requirements and threats. In
particular, we propose an agile methodology whose aim is to assess early on the
impact of the security requirements and of the security mechanisms designed to
satisfy them over the safety of the system. Security concerns are captured in a
component-centric manner through existing SysML diagrams with only minimal
extensions. After the requirements captured are derived into security and
cryptographic mechanisms, security properties can be formally verified over
this design. To perform the latter, model transformation techniques are
implemented in the SysML-Sec toolchain in order to derive a ProVerif
specification from the SysML models. An automotive firmware flashing procedure
serves as a guiding example throughout our presentation.Comment: In Proceedings GraMSec 2014, arXiv:1404.163
Text detection and recognition in natural scene images
This thesis addresses the problem of end-to-end text detection and recognition in
natural scene images based on deep neural networks. Scene text detection and recognition
aim to find regions in an image that are considered as text by human beings,
generate a bounding box for each word and output a corresponding sequence of
characters. As a useful task in image analysis, scene text detection and recognition
attract much attention in computer vision field. In this thesis, we tackle this problem
by taking advantage of the success in deep learning techniques.
Car license plates can be viewed as a spacial case of scene text, as they both consist
of characters and appear in natural scenes. Nevertheless, they have their respective
specificities. During the research progress, we start from car license plate detection
and recognition. Then we extend the methods to general scene text, with additional
ideas proposed.
For both tasks, we develop two approaches respectively: a stepwise one and
an integrated one. Stepwise methods tackle text detection and recognition step by
step by respective models; while integrated methods handle both text detection and
recognition simultaneously via one model. All approaches are based on the powerful
deep Convolutional Neural Networks (CNNs) and Recurrent Neural Networks
(RNNs), considering the tremendous breakthroughs they brought into the computer
vision community.
To begin with, a stepwise framework is proposed to tackle text detection and
recognition, with its application to car license plates and general scene text respectively.
A character CNN classifier is well trained to detect characters from an image
in a sliding window manner. The detected characters are then grouped together as
license plates or text lines according to some heuristic rules. A sequence labeling
based method is proposed to recognize the whole license plate or text line without
character level segmentation.
On the basis of the sequence labeling based recognition method, to accelerate the
processing speed, an integrated deep neural network is then proposed to address
car license plate detection and recognition concurrently. It integrates both CNNs
and RNNs in one network, and can be trained end-to-end. Both car license plate
bounding boxes and their labels are generated in a single forward evaluation of the
network. The whole process involves no heuristic rule, and avoids intermediate
procedures like image cropping or feature recalculation, which not only prevents
error accumulation, but also reduces computation burden.
Lastly, the unified network is extended to simultaneous general text detection and
recognition in natural scene. In contrast to the one for car license plates, some innovations
are proposed to accommodate the special characteristics of general text. A
varying-size RoI encoding method is proposed to handle the various aspect ratios of general text. An attention-based sequence-to-sequence learning structure is adopted
for word recognition. It is expected that a character-level language model can be
learnt in this manner. The whole framework can be trained end-to-end, requiring
only images, the ground-truth bounding boxes and text labels. Through end-to-end
training, the learned features can be more discriminative, which improves the overall
performance. The convolutional features are calculated only once and shared by both
detection and recognition, which saves the processing time. The proposed method
has achieved state-of-the-art performance on several standard benchmark datasets.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 201
Logic-Based Specification Languages for Intelligent Software Agents
The research field of Agent-Oriented Software Engineering (AOSE) aims to find
abstractions, languages, methodologies and toolkits for modeling, verifying,
validating and prototyping complex applications conceptualized as Multiagent
Systems (MASs). A very lively research sub-field studies how formal methods can
be used for AOSE. This paper presents a detailed survey of six logic-based
executable agent specification languages that have been chosen for their
potential to be integrated in our ARPEGGIO project, an open framework for
specifying and prototyping a MAS. The six languages are ConGoLog, Agent-0, the
IMPACT agent programming language, DyLog, Concurrent METATEM and Ehhf. For each
executable language, the logic foundations are described and an example of use
is shown. A comparison of the six languages and a survey of similar approaches
complete the paper, together with considerations of the advantages of using
logic-based languages in MAS modeling and prototyping.Comment: 67 pages, 1 table, 1 figure. Accepted for publication by the Journal
"Theory and Practice of Logic Programming", volume 4, Maurice Bruynooghe
Editor-in-Chie
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