pwc-archive/papers-with-abstracts huggingface dataset:
A Novel Block-DCT and PCA Based Image Perceptual Hashing Algorithm
Authors: Zeng Jie
Date: 2013-06-18 00:00:00
Tasks: Management

Image perceptual hashing finds applications in content indexing, large-scale
image database management, certification and authentication and digital
watermarking. We propose a Block-DCT and PCA based image perceptual hash in
this article and explore the algorithm in the application of tamper detection.
The main idea of the algorithm is to integrate color histogram and DCT
coefficients of image blocks as perceptual feature, then to compress perceptual
features as inter-feature with PCA, and to threshold to create a robust hash.
The robustness and discrimination properties of the proposed algorithm are
evaluated in detail. Our algorithms first construct a secondary image, derived
from input image by pseudo-randomly extracting features that approximately
capture semi-global geometric characteristics. From the secondary image (which
does not perceptually resemble the input), we further extract the final
features which can be used as a hash value (and can be further suitably
quantized). In this paper, we use spectral matrix invariants as embodied by
Singular Value Decomposition. Surprisingly, formation of the secondary image
turns out be quite important since it not only introduces further robustness,
but also enhances the security properties. Indeed, our experiments reveal that
our hashing algorithms extract most of the geometric information from the
images and hence are robust to severe perturbations (e.g. up to %50 cropping by
area with 20 degree rotations) on images while avoiding misclassification.
Experimental results show that the proposed image perceptual hash algorithm can
effectively address the tamper detection problem with advantageous robustness
and discrimination.
An Algorithm to Find Optimal Attack Paths in Nondeterministic Scenarios
Authors: Carlos Sarraute, Gerardo Richarte, Jorge Lucangeli Obes
Date: 2013-06-17 00:00:00

As penetration testing frameworks have evolved and have become more complex,
the problem of controlling automatically the pentesting tool has become an
important question. This can be naturally addressed as an attack planning
problem. Previous approaches to this problem were based on modeling the actions
and assets in the PDDL language, and using off-the-shelf AI tools to generate
attack plans. These approaches however are limited. In particular, the planning
is classical (the actions are deterministic) and thus not able to handle the
uncertainty involved in this form of attack planning.
  We herein contribute a planning model that does capture the uncertainty about
the results of the actions, which is modeled as a probability of success of
each action. We present efficient 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).
  We thus show that probabilistic attack planning can be solved efficiently for
the scenarios that arise when assessing the security of large networks. Two
"primitives" are presented, which are used as building blocks in a framework
separating the overall problem into two levels of abstraction. We also present
the experimental results obtained with our implementation, and conclude with
some ideas for further work.
Classification for Big Dataset of Bioacoustic Signals Based on Human Scoring System and Artificial Neural Network
Authors: Mohammad Pourhomayoun, Peter Dugan, Marian Popescu, Denise Risch, Hal Lewis, Christopher Clark
Date: 2013-05-15 00:00:00
Tasks: Classification, General Classification, Sound Classification

In this paper, we propose a method to improve sound classification
performance by combining signal features, derived from the time-frequency
spectrogram, with human perception. The method presented herein exploits an
artificial neural network (ANN) and learns the signal features based on the
human perception knowledge. The proposed method is applied to a large acoustic
dataset containing 24 months of nearly continuous recordings. The results show
a significant improvement in performance of the detection-classification
system; yielding as much as 20% improvement in true positive rate for a given
false positive rate.
Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network
Authors: Mohammad Pourhomayoun, Peter Dugan, Marian Popescu, Christopher Clark
Date: 2013-05-15 00:00:00
Tasks: General Classification

In this paper, we develop a novel method based on machine-learning and image
processing to identify North Atlantic right whale (NARW) up-calls in the
presence of high levels of ambient and interfering noise. We apply a continuous
region algorithm on the spectrogram to extract the regions of interest, and
then use grid masking techniques to generate a small feature set that is then
used in an artificial neural network classifier to identify the NARW up-calls.
It is shown that the proposed technique is effective in detecting and capturing
even very faint up-calls, in the presence of ambient and interfering noises.
The method is evaluated on a dataset recorded in Massachusetts Bay, United
States. The dataset includes 20000 sound clips for training, and 10000 sound
clips for testing. The results show that the proposed technique can achieve an
error rate of less than FPR = 4.5% for a 90% true positive rate.
Discriminating word senses with tourist walks in complex networks
Authors: Thiago C. Silva, Diego R. Amancio
Date: 2013-06-17 00:00:00
Tasks: Attribute, Clustering

Patterns of topological arrangement are widely used for both animal and human
brains in the learning process. Nevertheless, automatic learning techniques
frequently overlook these patterns. In this paper, we apply a learning
technique based on the structural organization of the data in the attribute
space to the problem of discriminating the senses of 10 polysemous words. Using
two types of characterization of meanings, namely semantical and topological
approaches, we have observed significative accuracy rates in identifying the
suitable meanings in both techniques. Most importantly, we have found that the
characterization based on the deterministic tourist walk improves the
disambiguation process when one compares with the discrimination achieved with
traditional complex networks measurements such as assortativity and clustering
coefficient. To our knowledge, this is the first time that such deterministic
walk has been applied to such a kind of problem. Therefore, our finding
suggests that the tourist walk characterization may be useful in other related
applications.
On Finding the Largest Mean Among Many
Authors: Kevin Jamieson, Matthew Malloy, Robert Nowak, Sebastien Bubeck
Date: 2013-06-17 00:00:00
Tasks: Multi-Armed Bandits

Sampling from distributions to find the one with the largest mean arises in a
broad range of applications, and it can be mathematically modeled as a
multi-armed bandit problem in which each distribution is associated with an
arm. This paper studies the sample complexity of identifying the best arm
(largest mean) in a multi-armed bandit problem. Motivated by large-scale
applications, we are especially interested in identifying situations where the
total number of samples that are necessary and sufficient to find the best arm
scale linearly with the number of arms. We present a single-parameter
multi-armed bandit model that spans the range from linear to superlinear sample
complexity. We also give a new algorithm for best arm identification, called
PRISM, with linear sample complexity for a wide range of mean distributions.
The algorithm, like most exploration procedures for multi-armed bandits, is
adaptive in the sense that the next arms to sample are selected based on
previous samples. We compare the sample complexity of adaptive procedures with
simpler non-adaptive procedures using new lower bounds. For many problem
instances, the increased sample complexity required by non-adaptive procedures
is a polynomial factor of the number of arms.
Stability of Multi-Task Kernel Regression Algorithms
Authors: Julien Audiffren, Hachem Kadri
Date: 2013-06-17 00:00:00
Tasks: Multi-Task Learning, regression

We study the stability properties of nonlinear multi-task regression in
reproducing Hilbert spaces with operator-valued kernels. Such kernels, a.k.a.
multi-task kernels, are appropriate for learning prob- lems with nonscalar
outputs like multi-task learning and structured out- put prediction. We show
that multi-task kernel regression algorithms are uniformly stable in the
general case of infinite-dimensional output spaces. We then derive under mild
assumption on the kernel generaliza- tion bounds of such algorithms, and we
show their consistency even with non Hilbert-Schmidt operator-valued kernels .
We demonstrate how to apply the results to various multi-task kernel regression
methods such as vector-valued SVR and functional ridge regression.
The Rise and Fall of Semantic Rule Updates Based on SE-Models
Authors: Martin Slota, João Leite
Date: 2013-06-17 00:00:00

Logic programs under the stable model semantics, or answer-set programs,
provide an expressive rule-based knowledge representation framework, featuring
a formal, declarative and well-understood semantics. However, handling the
evolution of rule bases is still a largely open problem. The AGM framework for
belief change was shown to give inappropriate results when directly applied to
logic programs under a non-monotonic semantics such as the stable models. The
approaches to address this issue, developed so far, proposed update semantics
based on manipulating the syntactic structure of programs and rules.
  More recently, AGM revision has been successfully applied to a significantly
more expressive semantic characterisation of logic programs based on SE-models.
This is an important step, as it changes the focus from the evolution of a
syntactic representation of a rule base to the evolution of its semantic
content.
  In this paper, we borrow results from the area of belief update to tackle the
problem of updating (instead of revising) answer-set programs. We prove a
representation theorem which makes it possible to constructively define any
operator satisfying a set of postulates derived from Katsuno and Mendelzon's
postulates for belief update. We define a specific operator based on this
theorem, examine its computational complexity and compare the behaviour of this
operator with syntactic rule update semantics from the literature. Perhaps
surprisingly, we uncover a serious drawback of all rule update operators based
on Katsuno and Mendelzon's approach to update and on SE-models.
Bayesian methods for low-rank matrix estimation: short survey and theoretical study
Authors: Pierre Alquier
Date: 2013-06-17 00:00:00

The problem of low-rank matrix estimation recently received a lot of
attention due to challenging applications. A lot of work has been done on
rank-penalized methods and convex relaxation, both on the theoretical and
applied sides. However, only a few papers considered Bayesian estimation. In
this paper, we review the different type of priors considered on matrices to
favour low-rank. We also prove that the obtained Bayesian estimators, under
suitable assumptions, enjoys the same optimality properties as the ones based
on penalization.
Cluster coloring of the Self-Organizing Map: An information visualization perspective
Authors: Peter Sarlin, Samuel Rönnqvist
Date: 2013-06-17 00:00:00

This paper takes an information visualization perspective to visual
representations in the general SOM paradigm. This involves viewing SOM-based
visualizations through the eyes of Bertin's and Tufte's theories on data
graphics. The regular grid shape of the Self-Organizing Map (SOM), while being
a virtue for linking visualizations to it, restricts representation of cluster
structures. From the viewpoint of information visualization, this paper
provides a general, yet simple, solution to projection-based coloring of the
SOM that reveals structures. First, the proposed color space is easy to
construct and customize to the purpose of use, while aiming at being
perceptually correct and informative through two separable dimensions. Second,
the coloring method is not dependent on any specific method of projection, but
is rather modular to fit any objective function suitable for the task at hand.
The cluster coloring is illustrated on two datasets: the iris data, and welfare
and poverty indicators.
Non-Uniform Blind Deblurring with a Spatially-Adaptive Sparse Prior
Authors: Haichao Zhang, David Wipf
Date: 2013-06-17 00:00:00
Tasks: Bayesian Inference, Deblurring

Typical blur from camera shake often deviates from the standard uniform
convolutional script, in part because of problematic rotations which create
greater blurring away from some unknown center point. Consequently, successful
blind deconvolution requires the estimation of a spatially-varying or
non-uniform blur operator. Using ideas from Bayesian inference and convex
analysis, this paper derives a non-uniform blind deblurring algorithm with
several desirable, yet previously-unexplored attributes. The underlying
objective function includes a spatially adaptive penalty which couples the
latent sharp image, non-uniform blur operator, and noise level together. This
coupling allows the penalty to automatically adjust its shape based on the
estimated degree of local blur and image structure such that regions with large
blur or few prominent edges are discounted. Remaining regions with modest blur
and revealing edges therefore dominate the overall estimation process without
explicitly incorporating structure-selection heuristics. The algorithm can be
implemented using a majorization-minimization strategy that is virtually
parameter free. Detailed theoretical analysis and empirical validation on real
images serve to validate the proposed method.
A Behavioural Foundation for Natural Computing and a Programmability Test
Authors: Hector Zenil
Date: 2013-03-23 00:00:00

What does it mean to claim that a physical or natural system computes? One
answer, endorsed here, is that computing is about programming a system to
behave in different ways. This paper offers an account of what it means for a
physical system to compute based on this notion. It proposes a behavioural
characterisation of computing in terms of a measure of programmability, which
reflects a system's ability to react to external stimuli. The proposed measure
of programmability is useful for classifying computers in terms of the apparent
algorithmic complexity of their evolution in time. I make some specific
proposals in this connection and discuss this approach in the context of other
behavioural approaches, notably Turing's test of machine intelligence. I also
anticipate possible objections and consider the applicability of these
proposals to the task of relating abstract computation to nature-like
computation.
Discrete perceptrons
Authors: Mihailo Stojnic
Date: 2013-06-17 00:00:00

Perceptrons have been known for a long time as a promising tool within the
neural networks theory. The analytical treatment for a special class of
perceptrons started in seminal work of Gardner \cite{Gar88}. Techniques
initially employed to characterize perceptrons relied on a statistical
mechanics approach. Many of such predictions obtained in \cite{Gar88} (and in a
follow-up \cite{GarDer88}) were later on established rigorously as mathematical
facts (see, e.g.
\cite{SchTir02,SchTir03,TalBook,StojnicGardGen13,StojnicGardSphNeg13,StojnicGardSphErr13}).
These typically related to spherical perceptrons. A lot of work has been done
related to various other types of perceptrons. Among the most challenging ones
are what we will refer to as the discrete perceptrons. An introductory
statistical mechanics treatment of such perceptrons was given in
\cite{GutSte90}. Relying on results of \cite{Gar88}, \cite{GutSte90}
characterized many of the features of several types of discrete perceptrons. We
in this paper, consider a similar subclass of discrete perceptrons and provide
a mathematically rigorous set of results related to their performance. As it
will turn out, many of the statistical mechanics predictions obtained for
discrete predictions will in fact appear as mathematically provable bounds.
This will in a way emulate a similar type of behavior we observed in
\cite{StojnicGardGen13,StojnicGardSphNeg13,StojnicGardSphErr13} when studying
spherical perceptrons.
Spherical perceptron as a storage memory with limited errors
Authors: Mihailo Stojnic
Date: 2013-06-17 00:00:00

It has been known for a long time that the classical spherical perceptrons
can be used as storage memories. Seminal work of Gardner, \cite{Gar88}, started
an analytical study of perceptrons storage abilities. Many of the Gardner's
predictions obtained through statistical mechanics tools have been rigorously
justified. Among the most important ones are of course the storage capacities.
The first rigorous confirmations were obtained in \cite{SchTir02,SchTir03} for
the storage capacity of the so-called positive spherical perceptron. These were
later reestablished in \cite{TalBook} and a bit more recently in
\cite{StojnicGardGen13}. In this paper we consider a variant of the spherical
perceptron that operates as a storage memory but allows for a certain fraction
of errors. In Gardner's original work the statistical mechanics predictions in
this directions were presented sa well. Here, through a mathematically rigorous
analysis, we confirm that the Gardner's predictions in this direction are in
fact provable upper bounds on the true values of the storage capacity.
Moreover, we then present a mechanism that can be used to lower these bounds.
Numerical results that we present indicate that the Garnder's storage capacity
predictions may, in a fairly wide range of parameters, be not that far away
from the true values.
Navigation domain representation for interactive multiview imaging
Authors: Thomas Maugey, Ismael Daribo, Gene Cheung, Pascal Frossard
Date: 2012-10-18 00:00:00
Tasks: Decoder, Navigate

Enabling users to interactively navigate through different viewpoints of a
static scene is a new interesting functionality in 3D streaming systems. While
it opens exciting perspectives towards rich multimedia applications, it
requires the design of novel representations and coding techniques in order to
solve the new challenges imposed by interactive navigation. Interactivity
clearly brings new design constraints: the encoder is unaware of the exact
decoding process, while the decoder has to reconstruct information from
incomplete subsets of data since the server can generally not transmit images
for all possible viewpoints due to resource constrains. In this paper, we
propose a novel multiview data representation that permits to satisfy bandwidth
and storage constraints in an interactive multiview streaming system. In
particular, we partition the multiview navigation domain into segments, each of
which is described by a reference image and some auxiliary information. The
auxiliary information enables the client to recreate any viewpoint in the
navigation segment via view synthesis. The decoder is then able to navigate
freely in the segment without further data request to the server; it requests
additional data only when it moves to a different segment. We discuss the
benefits of this novel representation in interactive navigation systems and
further propose a method to optimize the partitioning of the navigation domain
into independent segments, under bandwidth and storage constraints.
Experimental results confirm the potential of the proposed representation;
namely, our system leads to similar compression performance as classical
inter-view coding, while it provides the high level of flexibility that is
required for interactive streaming. Hence, our new framework represents a
promising solution for 3D data representation in novel interactive multimedia
services.
Spectral Experts for Estimating Mixtures of Linear Regressions
Authors: Arun Tejasvi Chaganty, Percy Liang
Date: 2013-06-17 00:00:00
Tasks: regression

Discriminative latent-variable models are typically learned using EM or
gradient-based optimization, which suffer from local optima. In this paper, we
develop a new computationally efficient and provably consistent estimator for a
mixture of linear regressions, a simple instance of a discriminative
latent-variable model. Our approach relies on a low-rank linear regression to
recover a symmetric tensor, which can be factorized into the parameters using a
tensor power method. We prove rates of convergence for our estimator and
provide an empirical evaluation illustrating its strengths relative to local
optimization (EM).
Bayesian test of significance for conditional independence: The multinomial model
Authors: Pablo de Morais Andrade, Julio Michael Stern, Carlos Alberto de Bragança Pereira
Date: 2013-06-16 00:00:00

Conditional independence tests (CI tests) have received special attention
lately in Machine Learning and Computational Intelligence related literature as
an important indicator of the relationship among the variables used by their
models. In the field of Probabilistic Graphical Models (PGM)--which includes
Bayesian Networks (BN) models--CI tests are especially important for the task
of learning the PGM structure from data. In this paper, we propose the Full
Bayesian Significance Test (FBST) for tests of conditional independence for
discrete datasets. FBST is a powerful Bayesian test for precise hypothesis, as
an alternative to frequentist's significance tests (characterized by the
calculation of the \emph{p-value}).
Recurrent Convolutional Neural Networks for Discourse Compositionality
Authors: Nal Kalchbrenner, Phil Blunsom
Date: 2013-06-15 00:00:00
Proceeding: WS 2013 8
Tasks: Dialogue Act Classification, Feature Engineering, Sentence

The compositionality of meaning extends beyond the single sentence. Just as
words combine to form the meaning of sentences, so do sentences combine to form
the meaning of paragraphs, dialogues and general discourse. We introduce both a
sentence model and a discourse model corresponding to the two levels of
compositionality. The sentence model adopts convolution as the central
operation for composing semantic vectors and is based on a novel hierarchical
convolutional neural network. The discourse model extends the sentence model
and is based on a recurrent neural network that is conditioned in a novel way
both on the current sentence and on the current speaker. The discourse model is
able to capture both the sequentiality of sentences and the interaction between
different speakers. Without feature engineering or pretraining and with simple
greedy decoding, the discourse model coupled to the sentence model obtains
state of the art performance on a dialogue act classification experiment.