pwc-archive/papers-with-abstracts huggingface dataset:
A Study on Classification in Imbalanced and Partially-Labelled Data Streams
Authors: R. J. Lyon, J. M. Brooke, J. D. Knowles, B. W. Stappers
Date: 2013-07-30 00:00:00
Tasks: Astronomy, General Classification

The domain of radio astronomy is currently facing significant computational
challenges, foremost amongst which are those posed by the development of the
world's largest radio telescope, the Square Kilometre Array (SKA). Preliminary
specifications for this instrument suggest that the final design will
incorporate between 2000 and 3000 individual 15 metre receiving dishes, which
together can be expected to produce a data rate of many TB/s. Given such a high
data rate, it becomes crucial to consider how this information will be
processed and stored to maximise its scientific utility. In this paper, we
consider one possible data processing scenario for the SKA, for the purposes of
an all-sky pulsar survey. In particular we treat the selection of promising
signals from the SKA processing pipeline as a data stream classification
problem. We consider the feasibility of classifying signals that arrive via an
unlabelled and heavily class imbalanced data stream, using currently available
algorithms and frameworks. Our results indicate that existing stream learners
exhibit unacceptably low recall on real astronomical data when used in standard
configuration; however, good false positive performance and comparable accuracy
to static learners, suggests they have definite potential as an on-line
solution to this particular big data challenge.
Sharp Threshold for Multivariate Multi-Response Linear Regression via Block Regularized Lasso
Authors: Weiguang Wang, Yingbin Liang, Eric P. Xing
Date: 2013-07-30 00:00:00
Tasks: regression

In this paper, we investigate a multivariate multi-response (MVMR) linear
regression problem, which contains multiple linear regression models with
differently distributed design matrices, and different regression and output
vectors. The goal is to recover the support union of all regression vectors
using $l_1/l_2$-regularized Lasso. We characterize sufficient and necessary
conditions on sample complexity \emph{as a sharp threshold} to guarantee
successful recovery of the support union. Namely, if the sample size is above
the threshold, then $l_1/l_2$-regularized Lasso correctly recovers the support
union; and if the sample size is below the threshold, $l_1/l_2$-regularized
Lasso fails to recover the support union. In particular, the threshold
precisely captures the impact of the sparsity of regression vectors and the
statistical properties of the design matrices on sample complexity. Therefore,
the threshold function also captures the advantages of joint support union
recovery using multi-task Lasso over individual support recovery using
single-task Lasso.
Likelihood-ratio calibration using prior-weighted proper scoring rules
Authors: Niko Brümmer, George Doddington
Date: 2013-07-30 00:00:00
Tasks: regression, scoring rule, Speaker Recognition

Prior-weighted logistic regression has become a standard tool for calibration
in speaker recognition. Logistic regression is the optimization of the expected
value of the logarithmic scoring rule. We generalize this via a parametric
family of proper scoring rules. Our theoretical analysis shows how different
members of this family induce different relative weightings over a spectrum of
applications of which the decision thresholds range from low to high. Special
attention is given to the interaction between prior weighting and proper
scoring rule parameters. Experiments on NIST SRE'12 suggest that for
applications with low false-alarm rate requirements, scoring rules tailored to
emphasize higher score thresholds may give better accuracy than logistic
regression.
Connecting Language and Knowledge Bases with Embedding Models for Relation Extraction
Authors: Jason Weston, Antoine Bordes, Oksana Yakhnenko, Nicolas Usunier
Date: 2013-07-30 00:00:00
Proceeding: EMNLP 2013 10
Tasks: Articles, Relation, Relation Extraction

This paper proposes a novel approach for relation extraction from free text
which is trained to jointly use information from the text and from existing
knowledge. Our model is based on two scoring functions that operate by learning
low-dimensional embeddings of words and of entities and relationships from a
knowledge base. We empirically show on New York Times articles aligned with
Freebase relations that our approach is able to efficiently use the extra
information provided by a large subset of Freebase data (4M entities, 23k
relationships) to improve over existing methods that rely on text features
alone.
On the accuracy of the Viterbi alignment
Authors: Kristi Kuljus, Jüri Lember
Date: 2013-07-30 00:00:00

In a hidden Markov model, the underlying Markov chain is usually hidden.
Often, the maximum likelihood alignment (Viterbi alignment) is used as its
estimate. Although having the biggest likelihood, the Viterbi alignment can
behave very untypically by passing states that are at most unexpected. To avoid
such situations, the Viterbi alignment can be modified by forcing it not to
pass these states. In this article, an iterative procedure for improving the
Viterbi alignment is proposed and studied. The iterative approach is compared
with a simple bunch approach where a number of states with low probability are
all replaced at the same time. It can be seen that the iterative way of
adjusting the Viterbi alignment is more efficient and it has several advantages
over the bunch approach. The same iterative algorithm for improving the Viterbi
alignment can be used in the case of peeping, that is when it is possible to
reveal hidden states. In addition, lower bounds for classification
probabilities of the Viterbi alignment under different conditions on the model
parameters are studied.
Energy Distribution of EEG Signals: EEG Signal Wavelet-Neural Network Classifier
Authors: Ibrahim Omerhodzic, Samir Avdakovic, Amir Nuhanovic, Kemal Dizdarevic
Date: 2013-07-30 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

In this paper, a wavelet-based neural network (WNN) classifier for
recognizing EEG signals is implemented and tested under three sets EEG signals
(healthy subjects, patients with epilepsy and patients with epileptic syndrome
during the seizure). First, the Discrete Wavelet Transform (DWT) with the
Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at
resolution levels of the components of the EEG signal (delta, theta, alpha,
beta and gamma) and the Parsevals theorem are employed to extract the
percentage distribution of energy features of the EEG signal at different
resolution levels. Second, the neural network (NN) classifies these extracted
features to identify the EEGs type according to the percentage distribution of
energy features. The performance of the proposed algorithm has been evaluated
using in total 300 EEG signals. The results showed that the proposed classifier
has the ability of recognizing and classifying EEG signals efficiently.
6th International Symposium on Attention in Cognitive Systems 2013
Authors: Lucas Paletta, Laurent Itti, Björn Schuller, Fang Fang
Date: 2013-07-22 00:00:00

This volume contains the papers accepted at the 6th International Symposium
on Attention in Cognitive Systems (ISACS 2013), held in Beijing, August 5,
2013. The aim of this symposium is to highlight the central role of attention
on various kinds of performance in cognitive systems processing. It brings
together researchers and developers from both academia and industry, from
computer vision, robotics, perception psychology, psychophysics and
neuroscience, in order to provide an interdisciplinary forum to present and
communicate on computational models of attention, with the focus on
interdependencies with visual cognition. Furthermore, it intends to investigate
relevant objectives for performance comparison, to document and to investigate
promising application domains, and to discuss visual attention with reference
to other aspects of AI enabled systems.
Scalable $k$-NN graph construction
Authors: Jingdong Wang, Jing Wang, Gang Zeng, Zhuowen Tu, Rui Gan, Shipeng Li
Date: 2013-07-30 00:00:00
Tasks: graph construction

The $k$-NN graph has played a central role in increasingly popular
data-driven techniques for various learning and vision tasks; yet, finding an
efficient and effective way to construct $k$-NN graphs remains a challenge,
especially for large-scale high-dimensional data. In this paper, we propose a
new approach to construct approximate $k$-NN graphs with emphasis in:
efficiency and accuracy. We hierarchically and randomly divide the data points
into subsets and build an exact neighborhood graph over each subset, achieving
a base approximate neighborhood graph; we then repeat this process for several
times to generate multiple neighborhood graphs, which are combined to yield a
more accurate approximate neighborhood graph. Furthermore, we propose a
neighborhood propagation scheme to further enhance the accuracy. We show both
theoretical and empirical accuracy and efficiency of our approach to $k$-NN
graph construction and demonstrate significant speed-up in dealing with large
scale visual data.
Hybrid Affinity Propagation
Authors: Jingdong Wang, Hao Xu, Xian-Sheng Hua, Shipeng Li
Date: 2013-07-30 00:00:00

In this paper, we address a problem of managing tagged images with hybrid
summarization. We formulate this problem as finding a few image exemplars to
represent the image set semantically and visually, and solve it in a hybrid way
by exploiting both visual and textual information associated with images. We
propose a novel approach, called homogeneous and heterogeneous message
propagation ($\text{H}^\text{2}\text{MP}$). Similar to the affinity propagation
(AP) approach, $\text{H}^\text{2}\text{MP}$ reduce the conventional
\emph{vector} message propagation to \emph{scalar} message propagation to make
the algorithm more efficient. Beyond AP that can only handle homogeneous data,
$\text{H}^\text{2}\text{MP}$ generalizes it to exploit extra heterogeneous
relations and the generalization is non-trivial as the reduction to scalar
messages from vector messages is more challenging. The main advantages of our
approach lie in 1) that $\text{H}^\text{2}\text{MP}$ exploits visual similarity
and in addition the useful information from the associated tags, including the
associations relation between images and tags and the relations within tags,
and 2) that the summary is both visually and semantically satisfactory. In
addition, our approach can also present a textual summary to a tagged image
collection, which can be used to automatically generate a textual description.
The experimental results demonstrate the effectiveness and efficiency of the
roposed approach.
An Integrated System for 3D Gaze Recovery and Semantic Analysis of Human Attention
Authors: Lucas Paletta, Katrin Santner, Gerald Fritz
Date: 2013-07-30 00:00:00

This work describes a computer vision system that enables pervasive mapping
and monitoring of human attention. The key contribution is that our methodology
enables full 3D recovery of the gaze pointer, human view frustum and associated
human centered measurements directly into an automatically computed 3D model in
real-time. We apply RGB-D SLAM and descriptor matching methodologies for the 3D
modeling, localization and fully automated annotation of ROIs (regions of
interest) within the acquired 3D model. This innovative methodology will open
new avenues for attention studies in real world environments, bringing new
potential into automated processing for human factors technologies.
Les POMDP font de meilleurs hackers: Tenir compte de l'incertitude dans les tests de penetration
Authors: Carlos Sarraute, Olivier Buffet, Joerg Hoffmann
Date: 2013-07-30 00:00:00

Penetration Testing is a methodology for assessing network security, by
generating and executing possible hacking attacks. Doing so automatically
allows for regular and systematic testing. A key question is how to generate
the attacks. This is naturally formulated as planning under uncertainty, i.e.,
under incomplete knowledge about the network configuration. Previous work uses
classical planning, and requires costly pre-processes reducing this uncertainty
by extensive application of scanning methods. By contrast, we herein model the
attack planning problem in terms of partially observable Markov decision
processes (POMDP). This allows to reason about the knowledge available, and to
intelligently employ scanning actions as part of the attack. As one would
expect, this accurate solution does not scale. We devise a method that relies
on POMDPs to find good attacks on individual machines, which are then composed
into an attack on the network as a whole. This decomposition exploits network
structure to the extent possible, making targeted approximations (only) where
needed. Evaluating this method on a suitably adapted industrial test suite, we
demonstrate its effectiveness in both runtime and solution quality.
Automated Attack Planning
Authors: Carlos Sarraute
Date: 2013-07-30 00:00:00

Penetration Testing is a methodology for assessing network security, by
generating and executing possible attacks. Doing so automatically allows for
regular and systematic testing. A key question then is how to automatically
generate the attacks. A natural way to address this issue is as an attack
planning problem. In this thesis, we are concerned with the specific context of
regular automated pentesting, and use the term "attack planning" in that sense.
The following three research directions are investigated.
  First, we introduce a conceptual model of computer network attacks, based on
an analysis of the penetration testing practices. We study how this attack
model can be represented in the PDDL language. Then we describe an
implementation that integrates a classical planner with a penetration testing
tool. This allows us to automatically generate attack paths for real world
pentesting scenarios, and to validate these attacks by executing them.
  Secondly, we present efficient probabilistic planning algorithms,
specifically designed for this problem, that achieve industrial-scale runtime
performance (able to solve scenarios with several hundred hosts and exploits).
These algorithms take into account the probability of success of the actions
and their expected cost (for example in terms of execution time, or network
traffic generated).
  Finally, we take a different direction: instead of trying to improve the
efficiency of the solutions developed, we focus on improving the model of the
attacker. We model the attack planning problem in terms of partially observable
Markov decision processes (POMDP). This grounds penetration testing in a
well-researched formalism. POMDPs allow the modelling of information gathering
as an integral part of the problem, thus providing for the first time a means
to intelligently mix scanning actions with actual exploits.
Efficient Energy Minimization for Enforcing Statistics
Authors: Yongsub Lim, Kyomin Jung, Pushmeet Kohli
Date: 2013-07-30 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Energy minimization algorithms, such as graph cuts, enable the computation of
the MAP solution under certain probabilistic models such as Markov random
fields. However, for many computer vision problems, the MAP solution under the
model is not the ground truth solution. In many problem scenarios, the system
has access to certain statistics of the ground truth. For instance, in image
segmentation, the area and boundary length of the object may be known. In these
cases, we want to estimate the most probable solution that is consistent with
such statistics, i.e., satisfies certain equality or inequality constraints.
  The above constrained energy minimization problem is NP-hard in general, and
is usually solved using Linear Programming formulations, which relax the
integrality constraints. This paper proposes a novel method that finds the
discrete optimal solution of such problems by maximizing the corresponding
Lagrangian dual. This method can be applied to any constrained energy
minimization problem whose unconstrained version is polynomial time solvable,
and can handle multiple, equality or inequality, and linear or non-linear
constraints. We demonstrate the efficacy of our method on the
foreground/background image segmentation problem, and show that it produces
impressive segmentation results with less error, and runs more than 20 times
faster than the state-of-the-art LP relaxation based approaches.
Protein (Multi-)Location Prediction: Using Location Inter-Dependencies in a Probabilistic Framework
Authors: Ramanuja Simha, Hagit Shatkay
Date: 2013-07-30 00:00:00

Knowing the location of a protein within the cell is important for
understanding its function, role in biological processes, and potential use as
a drug target. Much progress has been made in developing computational methods
that predict single locations for proteins, assuming that proteins localize to
a single location. However, it has been shown that proteins localize to
multiple locations. While a few recent systems have attempted to predict
multiple locations of proteins, they typically treat locations as independent
or capture inter-dependencies by treating each locations-combination present in
the training set as an individual location-class. We present a new method and a
preliminary system we have developed that directly incorporates
inter-dependencies among locations into the multiple-location-prediction
process, using a collection of Bayesian network classifiers. We evaluate our
system on a dataset of single- and multi-localized proteins. Our results,
obtained by incorporating inter-dependencies are significantly higher than
those obtained by classifiers that do not use inter-dependencies. The
performance of our system on multi-localized proteins is comparable to a top
performing system (YLoc+), without restricting predictions to be based only on
location-combinations present in the training set.
Multi-dimensional Parametric Mincuts for Constrained MAP Inference
Authors: Yongsub Lim, Kyomin Jung, Pushmeet Kohli
Date: 2013-07-30 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

In this paper, we propose novel algorithms for inferring the Maximum a
Posteriori (MAP) solution of discrete pairwise random field models under
multiple constraints. We show how this constrained discrete optimization
problem can be formulated as a multi-dimensional parametric mincut problem via
its Lagrangian dual, and prove that our algorithm isolates all constraint
instances for which the problem can be solved exactly. These multiple solutions
enable us to even deal with `soft constraints' (higher order penalty
functions). Moreover, we propose two practical variants of our algorithm to
solve problems with hard constraints. We also show how our method can be
applied to solve various constrained discrete optimization problems such as
submodular minimization and shortest path computation. Experimental evaluation
using the foreground-background image segmentation problem with statistic
constraints reveals that our method is faster and its results are closer to the
ground truth labellings compared with the popular continuous relaxation based
methods.
Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection
Authors: Yiyuan She, Huanghuang Li, Jiangping Wang, Dapeng Wu
Date: 2012-07-28 00:00:00
Tasks: compressed sensing, Super-Resolution

Recently, sparsity-based algorithms are proposed for super-resolution
spectrum estimation. However, to achieve adequately high resolution in
real-world signal analysis, the dictionary atoms have to be close to each other
in frequency, thereby resulting in a coherent design. The popular convex
compressed sensing methods break down in presence of high coherence and large
noise. We propose a new regularization approach to handle model collinearity
and obtain parsimonious frequency selection simultaneously. It takes advantage
of the pairing structure of sine and cosine atoms in the frequency dictionary.
A probabilistic spectrum screening is also developed for fast computation in
high dimensions. A data-resampling version of high-dimensional Bayesian
Information Criterion is used to determine the regularization parameters.
Experiments show the efficacy and efficiency of the proposed algorithms in
challenging situations with small sample size, high frequency resolution, and
low signal-to-noise ratio.
Sample Distortion for Compressed Imaging
Authors: Chunli Guo, Mike E. Davies
Date: 2013-03-22 00:00:00
Tasks: compressed sensing, Decoder

We propose the notion of a sample distortion (SD) function for independent
and identically distributed (i.i.d) compressive distributions to fundamentally
quantify the achievable reconstruction performance of compressed sensing for
certain encoder-decoder pairs at a given sampling ratio. Two lower bounds on
the achievable performance and the intrinsic convexity property is derived. A
zeroing procedure is then introduced to improve non convex SD functions. The SD
framework is then applied to analyse compressed imaging with a multi-resolution
statistical image model using both the generalized Gaussian distribution and
the two-state Gaussian mixture distribution. We subsequently focus on the
Gaussian encoder-Bayesian optimal approximate message passing (AMP) decoder
pair, whose theoretical SD function is provided by the rigorous analysis of the
AMP algorithm. Given the image statistics, analytic bandwise sample allocation
for bandwise independent model is derived as a reverse water-filling scheme.
Som and Schniter's turbo message passing approach is further deployed to
integrate the bandwise sampling with the exploitation of the hidden Markov tree
structure of wavelet coefficients. Natural image simulations confirm that with
oracle image statistics, the SD function associated with the optimized sample
allocation can accurately predict the possible compressed sensing gains.
Finally, a general sample allocation profile based on average image statistics
not only illustrates preferable performance but also makes the scheme
practical.
Herding the Crowd: Automated Planning for Crowdsourced Planning
Authors: Kartik Talamadupula, Subbarao Kambhampati
Date: 2013-07-29 00:00:00
Tasks: Scheduling

There has been significant interest in crowdsourcing and human computation.
One subclass of human computation applications are those directed at tasks that
involve planning (e.g. travel planning) and scheduling (e.g. conference
scheduling). Much of this work appears outside the traditional automated
planning forums, and at the outset it is not clear whether automated planning
has much of a role to play in these human computation systems. Interestingly
however, work on these systems shows that even primitive forms of automated
oversight of the human planner does help in significantly improving the
effectiveness of the humans/crowd. In this paper, we will argue that the
automated oversight used in these systems can be viewed as a primitive
automated planner, and that there are several opportunities for more
sophisticated automated planning in effectively steering crowdsourced planning.
Straightforward adaptation of current planning technology is however hampered
by the mismatch between the capabilities of human workers and automated
planners. We identify two important challenges that need to be overcome before
such adaptation of planning technology can occur: (i) interpreting the inputs
of the human workers (and the requester) and (ii) steering or critiquing the
plans being produced by the human workers armed only with incomplete domain and
preference models. In this paper, we discuss approaches for handling these
challenges, and characterize existing human computation systems in terms of the
specific choices they make in handling these challenges.