pwc-archive/papers-with-abstracts huggingface dataset:
Detecting Falls with X-Factor Hidden Markov Models
Authors: Shehroz S. Khan, Michelle E. Karg, Dana Kulic, Jesse Hoey
Date: 2015-04-08 00:00:00
Tasks: Activity Recognition, General Classification

Identification of falls while performing normal activities of daily living
(ADL) is important to ensure personal safety and well-being. However, falling
is a short term activity that occurs infrequently. This poses a challenge to
traditional classification algorithms, because there may be very little
training data for falls (or none at all). This paper proposes an approach for
the identification of falls using a wearable device in the absence of training
data for falls but with plentiful data for normal ADL. We propose three
`X-Factor' Hidden Markov Model (XHMMs) approaches. The XHMMs model unseen falls
using "inflated" output covariances (observation models). To estimate the
inflated covariances, we propose a novel cross validation method to remove
"outliers" from the normal ADL that serve as proxies for the unseen falls and
allow learning the XHMMs using only normal activities. We tested the proposed
XHMM approaches on two activity recognition datasets and show high detection
rates for falls in the absence of fall-specific training data. We show that the
traditional method of choosing a threshold based on maximum of negative of
log-likelihood to identify unseen falls is ill-posed for this problem. We also
show that supervised classification methods perform poorly when very limited
fall data are available during the training phase.
Weak Convergence Properties of Constrained Emphatic Temporal-difference Learning with Constant and Slowly Diminishing Stepsize
Authors: Huizhen Yu
Date: 2015-11-23 00:00:00

We consider the emphatic temporal-difference (TD) algorithm, ETD($\lambda$),
for learning the value functions of stationary policies in a discounted, finite
state and action Markov decision process. The ETD($\lambda$) algorithm was
recently proposed by Sutton, Mahmood, and White to solve a long-standing
divergence problem of the standard TD algorithm when it is applied to
off-policy training, where data from an exploratory policy are used to evaluate
other policies of interest. The almost sure convergence of ETD($\lambda$) has
been proved in our recent work under general off-policy training conditions,
but for a narrow range of diminishing stepsize. In this paper we present
convergence results for constrained versions of ETD($\lambda$) with constant
stepsize and with diminishing stepsize from a broad range. Our results
characterize the asymptotic behavior of the trajectory of iterates produced by
those algorithms, and are derived by combining key properties of ETD($\lambda$)
with powerful convergence theorems from the weak convergence methods in
stochastic approximation theory. For the case of constant stepsize, in addition
to analyzing the behavior of the algorithms in the limit as the stepsize
parameter approaches zero, we also analyze their behavior for a fixed stepsize
and bound the deviations of their averaged iterates from the desired solution.
These results are obtained by exploiting the weak Feller property of the Markov
chains associated with the algorithms, and by using ergodic theorems for weak
Feller Markov chains, in conjunction with the convergence results we get from
the weak convergence methods. Besides ETD($\lambda$), our analysis also applies
to the off-policy TD($\lambda$) algorithm, when the divergence issue is avoided
by setting $\lambda$ sufficiently large.
Semi-Supervised Learning on Graphs through Reach and Distance Diffusion
Authors: Edith Cohen
Date: 2016-03-30 00:00:00

Semi-supervised learning (SSL) is an indispensable tool when there are few
labeled entities and many unlabeled entities for which we want to predict
labels. With graph-based methods, entities correspond to nodes in a graph and
edges represent strong relations. At the heart of SSL algorithms is the
specification of a dense {\em kernel} of pairwise affinity values from the
graph structure. A learning algorithm is then trained on the kernel together
with labeled entities. The most popular kernels are {\em spectral} and include
the highly scalable "symmetric" Laplacian methods, that compute a soft labels
using Jacobi iterations, and "asymmetric" methods including Personalized Page
Rank (PPR) which use short random walks and apply with directed relations, such
as like, follow, or hyperlinks.
  We introduce {\em Reach diffusion} and {\em Distance diffusion} kernels that
build on powerful social and economic models of centrality and influence in
networks and capture the directed pairwise relations that underline social
influence. Inspired by the success of social influence as an alternative to
spectral centrality such as Page Rank, we explore SSL with our kernels and
develop highly scalable algorithms for parameter setting, label learning, and
sampling. We perform preliminary experiments that demonstrate the properties
and potential of our kernels.
"Flow Size Difference" Can Make a Difference: Detecting Malicious TCP Network Flows Based on Benford's Law
Authors: Aamo Iorliam, Santosh Tirunagari, Anthony T. S. Ho, Shujun Li, Adrian Waller, Norman Poh
Date: 2016-09-14 00:00:00
Tasks: Intrusion Detection, Network Intrusion Detection

Statistical characteristics of network traffic have attracted a significant
amount of research for automated network intrusion detection, some of which
looked at applications of natural statistical laws such as Zipf's law,
Benford's law and the Pareto distribution. In this paper, we present the
application of Benford's law to a new network flow metric "flow size
difference", which have not been studied before by other researchers, to build
an unsupervised flow-based intrusion detection system (IDS). The method was
inspired by our observation on a large number of TCP flow datasets where normal
flows tend to follow Benford's law closely but malicious flows tend to deviate
significantly from it. The proposed IDS is unsupervised, so it can be easily
deployed without any training. It has two simple operational parameters with a
clear semantic meaning, allowing the IDS operator to set and adapt their values
intuitively to adjust the overall performance of the IDS. We tested the
proposed IDS on two (one closed and one public) datasets, and proved its
efficiency in terms of AUC (area under the ROC curve). Our work showed the
"flow size difference" has a great potential to improve the performance of any
flow-based network IDSs.
Multiplex visibility graphs to investigate recurrent neural networks dynamics
Authors: Filippo Maria Bianchi, Lorenzo Livi, Cesare Alippi, Robert Jenssen
Date: 2016-09-10 00:00:00
Tasks: Time Series, Time Series Analysis

A recurrent neural network (RNN) is a universal approximator of dynamical
systems, whose performance often depends on sensitive hyperparameters. Tuning
of such hyperparameters may be difficult and, typically, based on a
trial-and-error approach. In this work, we adopt a graph-based framework to
interpret and characterize the internal RNN dynamics. Through this insight, we
are able to design a principled unsupervised method to derive configurations
with maximized performances, in terms of prediction error and memory capacity.
In particular, we propose to model time series of neurons activations with the
recently introduced horizontal visibility graphs, whose topological properties
reflect important dynamical features of the underlying dynamic system.
Successively, each graph becomes a layer of a larger structure, called
multiplex. We show that topological properties of such a multiplex reflect
important features of RNN dynamics and are used to guide the tuning procedure.
To validate the proposed method, we consider a class of RNNs called echo state
networks. We perform experiments and discuss results on several benchmarks and
real-world dataset of call data records.
On Randomized Distributed Coordinate Descent with Quantized Updates
Authors: Mostafa El Gamal, Lifeng Lai
Date: 2016-09-18 00:00:00
Tasks: Quantization, regression

In this paper, we study the randomized distributed coordinate descent
algorithm with quantized updates. In the literature, the iteration complexity
of the randomized distributed coordinate descent algorithm has been
characterized under the assumption that machines can exchange updates with an
infinite precision. We consider a practical scenario in which the messages
exchange occurs over channels with finite capacity, and hence the updates have
to be quantized. We derive sufficient conditions on the quantization error such
that the algorithm with quantized update still converge. We further verify our
theoretical results by running an experiment, where we apply the algorithm with
quantized updates to solve a linear regression problem.
End-To-End Visual Speech Recognition With LSTMs
Authors: Stavros Petridis, Zuwei Li, Maja Pantic
Date: 2017-01-20 00:00:00
Tasks: Classification, General Classification, speech-recognition, Speech Recognition, Visual Speech Recognition

Traditional visual speech recognition systems consist of two stages, feature
extraction and classification. Recently, several deep learning approaches have
been presented which automatically extract features from the mouth images and
aim to replace the feature extraction stage. However, research on joint
learning of features and classification is very limited. In this work, we
present an end-to-end visual speech recognition system based on Long-Short
Memory (LSTM) networks. To the best of our knowledge, this is the first model
which simultaneously learns to extract features directly from the pixels and
perform classification and also achieves state-of-the-art performance in visual
speech classification. The model consists of two streams which extract features
directly from the mouth and difference images, respectively. The temporal
dynamics in each stream are modelled by an LSTM and the fusion of the two
streams takes place via a Bidirectional LSTM (BLSTM). An absolute improvement
of 9.7% over the base line is reported on the OuluVS2 database, and 1.5% on the
CUAVE database when compared with other methods which use a similar visual
front-end.
Case Study of a highly automated Layout Analysis and OCR of an incunabulum: 'Der Heiligen Leben' (1488)
Authors: Christian Reul, Marco Dittrich, Martin Gruner
Date: 2017-01-20 00:00:00
Tasks: Optical Character Recognition (OCR)

This paper provides the first thorough documentation of a high quality
digitization process applied to an early printed book from the incunabulum
period (1450-1500). The entire OCR related workflow including preprocessing,
layout analysis and text recognition is illustrated in detail using the example
of 'Der Heiligen Leben', printed in Nuremberg in 1488. For each step the
required time expenditure was recorded. The character recognition yielded
excellent results both on character (97.57%) and word (92.19%) level.
Furthermore, a comparison of a highly automated (LAREX) and a manual (Aletheia)
method for layout analysis was performed. By considerably automating the
segmentation the required human effort was reduced significantly from over 100
hours to less than six hours, resulting in only a slight drop in OCR accuracy.
Realistic estimates for the human effort necessary for full text extraction
from incunabula can be derived from this study. The printed pages of the
complete work together with the OCR result is available online ready to be
inspected and downloaded.
A Variational Bayesian Approach for Image Restoration. Application to Image Deblurring with Poisson-Gaussian Noise
Authors: Yosra Marnissi, Yuling Zheng, Emilie Chouzenoux, Jean-Christophe Pesquet
Date: 2016-10-24 00:00:00
Tasks: Deblurring, Image Deblurring, Image Restoration

In this paper, a methodology is investigated for signal recovery in the
presence of non-Gaussian noise. In contrast with regularized minimization
approaches often adopted in the literature, in our algorithm the regularization
parameter is reliably estimated from the observations. As the posterior density
of the unknown parameters is analytically intractable, the estimation problem
is derived in a variational Bayesian framework where the goal is to provide a
good approximation to the posterior distribution in order to compute posterior
mean estimates. Moreover, a majorization technique is employed to circumvent
the difficulties raised by the intricate forms of the non-Gaussian likelihood
and of the prior density. We demonstrate the potential of the proposed approach
through comparisons with state-of-the-art techniques that are specifically
tailored to signal recovery in the presence of mixed Poisson-Gaussian noise.
Results show that the proposed approach is efficient and achieves performance
comparable with other methods where the regularization parameter is manually
tuned from the ground truth.
Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper)
Authors: Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
Date: 2017-01-20 00:00:00

In this work, we present a novel module to perform fusion of heterogeneous
data using fully convolutional networks for semantic labeling. We introduce
residual correction as a way to learn how to fuse predictions coming out of a
dual stream architecture. Especially, we perform fusion of DSM and IRRG optical
data on the ISPRS Vaihingen dataset over a urban area and obtain new
state-of-the-art results.
Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks
Authors: Sungho Jeon, Jong-Woo Shin, Young-Jun Lee, Woong-Hee Kim, YoungHyoun Kwon, Hae-Yong Yang
Date: 2017-01-20 00:00:00
Tasks: Binary Classification, Event Detection, General Classification, Sound Classification, Sound Event Detection

This work aims to investigate the use of deep neural network to detect
commercial hobby drones in real-life environments by analyzing their sound
data. The purpose of work is to contribute to a system for detecting drones
used for malicious purposes, such as for terrorism. Specifically, we present a
method capable of detecting the presence of commercial hobby drones as a binary
classification problem based on sound event detection. We recorded the sound
produced by a few popular commercial hobby drones, and then augmented this data
with diverse environmental sound data to remedy the scarcity of drone sound
data in diverse environments. We investigated the effectiveness of
state-of-the-art event sound classification methods, i.e., a Gaussian Mixture
Model (GMM), Convolutional Neural Network (CNN), and Recurrent Neural Network
(RNN), for drone sound detection. Our empirical results, which were obtained
with a testing dataset collected on an urban street, confirmed the
effectiveness of these models for operating in a real environment. In summary,
our RNN models showed the best detection performance with an F-Score of 0.8009
with 240 ms of input audio with a short processing time, indicating their
applicability to real-time detection systems.
Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods
Authors: Min Lu, Saad Sadiq, Daniel J. Feaster, Hemant Ishwaran
Date: 2017-01-19 00:00:00
Tasks: counterfactual, Selection bias

Estimation of individual treatment effect in observational data is
complicated due to the challenges of confounding and selection bias. A useful
inferential framework to address this is the counterfactual (potential
outcomes) model which takes the hypothetical stance of asking what if an
individual had received both treatments. Making use of random forests (RF)
within the counterfactual framework we estimate individual treatment effects by
directly modeling the response. We find accurate estimation of individual
treatment effects is possible even in complex heterogeneous settings but that
the type of RF approach plays an important role in accuracy. Methods designed
to be adaptive to confounding, when used in parallel with out-of-sample
estimation, do best. One method found to be especially promising is
counterfactual synthetic forests. We illustrate this new methodology by
applying it to a large comparative effectiveness trial, Project Aware, in order
to explore the role drug use plays in sexual risk. The analysis reveals
important connections between risky behavior, drug usage, and sexual risk.
Multivariate Confidence Intervals
Authors: Jussi Korpela, Emilia Oikarinen, Kai Puolamäki, Antti Ukkonen
Date: 2017-01-20 00:00:00

Confidence intervals are a popular way to visualize and analyze data
distributions. Unlike p-values, they can convey information both about
statistical significance as well as effect size. However, very little work
exists on applying confidence intervals to multivariate data. In this paper we
define confidence intervals for multivariate data that extend the
one-dimensional definition in a natural way. In our definition every variable
is associated with its own confidence interval as usual, but a data vector can
be outside of a few of these, and still be considered to be within the
confidence area. We analyze the problem and show that the resulting confidence
areas retain the good qualities of their one-dimensional counterparts: they are
informative and easy to interpret. Furthermore, we show that the problem of
finding multivariate confidence intervals is hard, but provide efficient
approximate algorithms to solve the problem.
Robust Intrinsic and Extrinsic Calibration of RGB-D Cameras
Authors: Filippo Basso, Emanuele Menegatti, Alberto Pretto
Date: 2017-01-20 00:00:00
Tasks: Object Recognition

Color-depth cameras (RGB-D cameras) have become the primary sensors in most
robotics systems, from service robotics to industrial robotics applications.
Typical consumer-grade RGB-D cameras are provided with a coarse intrinsic and
extrinsic calibration that generally does not meet the accuracy requirements
needed by many robotics applications (e.g., highly accurate 3D environment
reconstruction and mapping, high precision object recognition and localization,
...). In this paper, we propose a human-friendly, reliable and accurate
calibration framework that enables to easily estimate both the intrinsic and
extrinsic parameters of a general color-depth sensor couple. Our approach is
based on a novel two components error model. This model unifies the error
sources of RGB-D pairs based on different technologies, such as
structured-light 3D cameras and time-of-flight cameras. Our method provides
some important advantages compared to other state-of-the-art systems: it is
general (i.e., well suited for different types of sensors), based on an easy
and stable calibration protocol, provides a greater calibration accuracy, and
has been implemented within the ROS robotics framework. We report detailed
experimental validations and performance comparisons to support our statements.
LAREX - A semi-automatic open-source Tool for Layout Analysis and Region Extraction on Early Printed Books
Authors: Christian Reul, Uwe Springmann, Frank Puppe
Date: 2017-01-20 00:00:00
Tasks: Optical Character Recognition (OCR)

A semi-automatic open-source tool for layout analysis on early printed books
is presented. LAREX uses a rule based connected components approach which is
very fast, easily comprehensible for the user and allows an intuitive manual
correction if necessary. The PageXML format is used to support integration into
existing OCR workflows. Evaluations showed that LAREX provides an efficient and
flexible way to segment pages of early printed books.
Using LLVM-based JIT Compilation in Genetic Programming
Authors: Michal Gregor, Juraj Spalek
Date: 2017-01-20 00:00:00

The paper describes an approach to implementing genetic programming, which
uses the LLVM library to just-in-time compile/interpret the evolved abstract
syntax trees. The solution is described in some detail, including a parser
(based on FlexC++ and BisonC++) that can construct the trees from a simple toy
language with C-like syntax. The approach is compared with a previous
implementation (based on direct execution of trees using polymorphic functors)
in terms of execution speed.
Logical Inferences with Contexts of RDF Triples
Authors: Vinh Nguyen, Amit Sheth
Date: 2017-01-20 00:00:00
Tasks: World Knowledge

Logical inference, an integral feature of the Semantic Web, is the process of
deriving new triples by applying entailment rules on knowledge bases. The
entailment rules are determined by the model-theoretic semantics. Incorporating
context of an RDF triple (e.g., provenance, time, and location) into the
inferencing process requires the formal semantics to be capable of describing
the context of RDF triples also in the form of triples, or in other words, RDF
contextual triples about triples. The formal semantics should also provide the
rules that could entail new contextual triples about triples. In this paper, we
propose the first inferencing mechanism that allows context of RDF triples,
represented in the form of RDF triples about triples, to be the first-class
citizens in the model-theoretic semantics and in the logical rules. Our
inference mechanism is well-formalized with all new concepts being captured in
the model-theoretic semantics. This formal semantics also allows us to derive a
new set of entailment rules that could entail new contextual triples about
triples. To demonstrate the feasibility and the scalability of the proposed
mechanism, we implement a new tool in which we transform the existing knowledge
bases to our representation of RDF triples about triples and provide the option
for this tool to compute the inferred triples for the proposed rules. We
evaluate the computation of the proposed rules on a large scale using various
real-world knowledge bases such as Bio2RDF NCBI Genes and DBpedia. The results
show that the computation of the inferred triples can be highly scalable. On
average, one billion inferred triples adds 5-6 minutes to the overall
transformation process. NCBI Genes, with 20 billion triples in total, took only
232 minutes for the transformation of 12 billion triples and added 42 minutes
for inferring 8 billion triples to the overall process.
Inference algorithms for pattern-based CRFs on sequence data
Authors: Rustem Takhanov, Vladimir Kolmogorov
Date: 2012-10-01 00:00:00

We consider Conditional Random Fields (CRFs) with pattern-based potentials
defined on a chain. In this model the energy of a string (labeling) $x_1...x_n$
is the sum of terms over intervals $[i,j]$ where each term is non-zero only if
the substring $x_i...x_j$ equals a prespecified pattern $\alpha$. Such CRFs can
be naturally applied to many sequence tagging problems.
  We present efficient algorithms for the three standard inference tasks in a
CRF, namely computing (i) the partition function, (ii) marginals, and (iii)
computing the MAP. Their complexities are respectively $O(n L)$, $O(n L
\ell_{max})$ and $O(n L \min\{|D|,\log (\ell_{max}+1)\})$ where $L$ is the
combined length of input patterns, $\ell_{max}$ is the maximum length of a
pattern, and $D$ is the input alphabet. This improves on the previous
algorithms of (Ye et al., 2009) whose complexities are respectively $O(n L
|D|)$, $O(n |\Gamma| L^2 \ell_{max}^2)$ and $O(n L |D|)$, where $|\Gamma|$ is
the number of input patterns.
  In addition, we give an efficient algorithm for sampling. Finally, we
consider the case of non-positive weights. (Komodakis & Paragios, 2009) gave an
$O(n L)$ algorithm for computing the MAP. We present a modification that has
the same worst-case complexity but can beat it in the best case.