pwc-archive/papers-with-abstracts huggingface dataset:
NRC-Canada: Building the State-of-the-Art in Sentiment Analysis of Tweets
Authors: Saif M. Mohammad, Svetlana Kiritchenko, Xiaodan Zhu
Date: 2013-08-28 00:00:00
Proceeding: SEMEVAL 2013 6
Tasks: Sentiment Analysis

In this paper, we describe how we created two state-of-the-art SVM
classifiers, one to detect the sentiment of messages such as tweets and SMS
(message-level task) and one to detect the sentiment of a term within a
submissions stood first in both tasks on tweets, obtaining an F-score of 69.02
in the message-level task and 88.93 in the term-level task. We implemented a
variety of surface-form, semantic, and sentiment features. with sentiment-word
hashtags, and one from tweets with emoticons. In the message-level task, the
lexicon-based features provided a gain of 5 F-score points over all others.
Both of our systems can be replicated us available resources.
Bayesian Conditional Gaussian Network Classifiers with Applications to Mass Spectra Classification
Authors: Victor Bellon, JesUs Cerquides, Ivo Grosse
Date: 2013-08-28 00:00:00
Tasks: General Classification

Classifiers based on probabilistic graphical models are very effective. In
continuous domains, maximum likelihood is usually used to assess the
predictions of those classifiers. When data is scarce, this can easily lead to
overfitting. In any probabilistic setting, Bayesian averaging (BA) provides
theoretically optimal predictions and is known to be robust to overfitting. In
this work we introduce Bayesian Conditional Gaussian Network Classifiers, which
efficiently perform exact Bayesian averaging over the parameters. We evaluate
the proposed classifiers against the maximum likelihood alternatives proposed
so far over standard UCI datasets, concluding that performing BA improves the
quality of the assessed probabilities (conditional log likelihood) whilst
maintaining the error rate.
  Overfitting is more likely to occur in domains where the number of data items
is small and the number of variables is large. These two conditions are met in
the realm of bioinformatics, where the early diagnosis of cancer from mass
spectra is a relevant task. We provide an application of our classification
framework to that problem, comparing it with the standard maximum likelihood
alternative, where the improvement of quality in the assessed probabilities is
confirmed.
Identifiability of Gaussian structural equation models with equal error variances
Authors: Jonas Peters, Peter Bühlmann
Date: 2012-05-11 00:00:00
Tasks: Causal Inference

We consider structural equation models in which variables can be written as a
function of their parents and noise terms, which are assumed to be jointly
independent. Corresponding to each structural equation model, there is a
directed acyclic graph describing the relationships between the variables. In
Gaussian structural equation models with linear functions, the graph can be
identified from the joint distribution only up to Markov equivalence classes,
assuming faithfulness. In this work, we prove full identifiability if all noise
variables have the same variances: the directed acyclic graph can be recovered
from the joint Gaussian distribution. Our result has direct implications for
causal inference: if the data follow a Gaussian structural equation model with
equal error variances and assuming that all variables are observed, the causal
structure can be inferred from observational data only. We propose a
statistical method and an algorithm that exploit our theoretical findings.
Preventing Disclosure of Sensitive Knowledge by Hiding Inference
Authors: A. S. Syed Navaz, M. Ravi, T. Prabhu
Date: 2013-08-28 00:00:00

Data Mining is a way of extracting data or uncovering hidden patterns of
information from databases. So, there is a need to prevent the inference rules
from being disclosed such that the more secure data sets cannot be identified
from non sensitive attributes. This can be done through removing or adding
certain item sets in the transactions Sanitization. The purpose is to hide the
Inference rules, so that the user may not be able to discover any valuable
information from other non sensitive data and any organisation can release all
samples of their data without the fear of Knowledge Discovery In Databases
which can be achieved by investigating frequently occurring item sets, rules
that can be mined from them with the objective of hiding them. Another way is
to release only limited samples in the new database so that there is no
information loss and it also satisfies the legitimate needs of the users. The
major problem is uncovering hidden patterns, which causes a threat to the
database security. Sensitive data are inferred from non-sensitive data based on
the semantics of the application the user has, commonly known as the inference
problem. Two fundamental approaches to protect sensitive rules from disclosure
are that, preventing rules from being generated by hiding the frequent sets of
data items and reducing the importance of the rules by setting their confidence
below a user-specified threshold.
Brain MRI Segmentation with Fast and Globally Convex Multiphase Active Contours
Authors: Juan C. Moreno, V. B. S. Prasath, Hugo Proenca, K. Palaniappan
Date: 2013-08-28 00:00:00
Tasks: MRI segmentation

Multiphase active contour based models are useful in identifying multiple
regions with different characteristics such as the mean values of regions. This
is relevant in brain magnetic resonance images (MRIs), allowing the
differentiation of white matter against gray matter. We consider a well defined
globally convex formulation of Vese and Chan multiphase active contour model
for segmenting brain MRI images. A well-established theory and an efficient
dual minimization scheme are thoroughly described which guarantees optimal
solutions and provides stable segmentations. Moreover, under the dual
minimization implementation our model perfectly describes disjoint regions by
avoiding local minima solutions. Experimental results indicate that the
proposed approach provides better accuracy than other related multiphase active
contour algorithms even under severe noise, intensity inhomogeneities, and
partial volume effects.
On the Doubt about Margin Explanation of Boosting
Authors: Wei Gao, Zhi-Hua Zhou
Date: 2010-09-19 00:00:00

Margin theory provides one of the most popular explanations to the success of
\texttt{AdaBoost}, where the central point lies in the recognition that
\textit{margin} is the key for characterizing the performance of
\texttt{AdaBoost}. This theory has been very influential, e.g., it has been
used to argue that \texttt{AdaBoost} usually does not overfit since it tends to
enlarge the margin even after the training error reaches zero. Previously the
\textit{minimum margin bound} was established for \texttt{AdaBoost}, however,
\cite{Breiman1999} pointed out that maximizing the minimum margin does not
necessarily lead to a better generalization. Later, \cite{Reyzin:Schapire2006}
emphasized that the margin distribution rather than minimum margin is crucial
to the performance of \texttt{AdaBoost}. In this paper, we first present the
\textit{$k$th margin bound} and further study on its relationship to previous
work such as the minimum margin bound and Emargin bound. Then, we improve the
previous empirical Bernstein bounds
\citep{Maurer:Pontil2009,Audibert:Munos:Szepesvari2009}, and based on such
findings, we defend the margin-based explanation against Breiman's doubts by
proving a new generalization error bound that considers exactly the same
factors as \cite{Schapire:Freund:Bartlett:Lee1998} but is sharper than
\cite{Breiman1999}'s minimum margin bound. By incorporating factors such as
average margin and variance, we present a generalization error bound that is
heavily related to the whole margin distribution. We also provide margin
distribution bounds for generalization error of voting classifiers in finite
VC-dimension space.
Improving Sparse Associative Memories by Escaping from Bogus Fixed Points
Authors: Zhe Yao, Vincent Gripon, Michael Rabbat
Date: 2013-08-27 00:00:00
Tasks: Retrieval

The Gripon-Berrou neural network (GBNN) is a recently invented recurrent
neural network embracing a LDPC-like sparse encoding setup which makes it
extremely resilient to noise and errors. A natural use of GBNN is as an
associative memory. There are two activation rules for the neuron dynamics,
namely sum-of-sum and sum-of-max. The latter outperforms the former in terms of
retrieval rate by a huge margin. In prior discussions and experiments, it is
believed that although sum-of-sum may lead the network to oscillate, sum-of-max
always converges to an ensemble of neuron cliques corresponding to previously
stored patterns. However, this is not entirely correct. In fact, sum-of-max
often converges to bogus fixed points where the ensemble only comprises a small
subset of the converged state. By taking advantage of this overlooked fact, we
can greatly improve the retrieval rate. We discuss this particular issue and
propose a number of heuristics to push sum-of-max beyond these bogus fixed
points. To tackle the problem directly and completely, a novel post-processing
algorithm is also developed and customized to the structure of GBNN.
Experimental results show that the new algorithm achieves a huge performance
boost in terms of both retrieval rate and run-time, compared to the standard
sum-of-max and all the other heuristics.
Hierarchized block wise image approximation by greedy pursuit strategies
Authors: Laura Rebollo-Neira, Ryszard Maciol, Shabnam Bibi
Date: 2013-08-27 00:00:00

An approach for effective implementation of greedy selection methodologies,
to approximate an image partitioned into blocks, is proposed. The method is
specially designed for approximating partitions on a transformed image. It
evolves by selecting, at each iteration step, i) the elements for approximating
each of the blocks partitioning the image and ii) the hierarchized sequence in
which the blocks are approximated to reach the required global condition on
sparsity.
Backhaul-Aware Interference Management in the Uplink of Wireless Small Cell Networks
Authors: Sumudu Samarakoon, Mehdi Bennis, Walid Saad, Matti Latva-aho
Date: 2013-08-27 00:00:00
Tasks: Management

The design of distributed mechanisms for interference management is one of
the key challenges in emerging wireless small cell networks whose backhaul is
capacity limited and heterogeneous (wired, wireless and a mix thereof). In this
paper, a novel, backhaul-aware approach to interference management in wireless
small cell networks is proposed. The proposed approach enables macrocell user
equipments (MUEs) to optimize their uplink performance, by exploiting the
presence of neighboring small cell base stations. The problem is formulated as
a noncooperative game among the MUEs that seek to optimize their delay-rate
tradeoff, given the conditions of both the radio access network and the --
possibly heterogeneous -- backhaul. To solve this game, a novel, distributed
learning algorithm is proposed using which the MUEs autonomously choose their
optimal uplink transmission strategies, given a limited amount of available
information. The convergence of the proposed algorithm is shown and its
properties are studied. Simulation results show that, under various types of
backhauls, the proposed approach yields significant performance gains, in terms
of both average throughput and delay for the MUEs, when compared to existing
benchmark algorithms.
Multi-Objective Particle Swarm Optimization for Facility Location Problem in Wireless Mesh Networks
Authors: Tarik Mountassir Bouchaib Nassereddine, Abdelkrim Haqiq, Samir Bennani
Date: 2013-08-27 00:00:00

Wireless mesh networks have seen a real progress due of their implementation
at a low cost. They present one of Next Generation Networks technologies and
can serve as home, companies and universities networks. In this paper, we
propose and discuss a new multi-objective model for nodes deployment
optimization in Multi-Radio Multi-Channel Wireless Mesh Networks. We exploit
the trade-off between network cost and the overall network performance. This
optimization problem is solved simultaneously by using a meta-heuristic method
that returns a non-dominated set of near optimal solutions. A comparative study
was driven to evaluate the efficiency of the proposed model.
The Generalized Mean Information Coefficient
Authors: Alexander Luedtke, Linh Tran
Date: 2013-08-26 00:00:00

Reshef & Reshef recently published a paper in which they present a method
called the Maximal Information Coefficient (MIC) that can detect all forms of
statistical dependence between pairs of variables as sample size goes to
infinity. While this method has been praised by some, it has also been
criticized for its lack of power in finite samples. We seek to modify MIC so
that it has higher power in detecting associations for limited sample sizes.
Here we present the Generalized Mean Information Coefficient (GMIC), a
generalization of MIC which incorporates a tuning parameter that can be used to
modify the complexity of the association favored by the measure. We define GMIC
and prove it maintains several key asymptotic properties of MIC. Its increased
power over MIC is demonstrated using a simulation of eight different functional
relationships at sixty different noise levels. The results are compared to the
Pearson correlation, distance correlation, and MIC. Simulation results suggest
that while generally GMIC has slightly lower power than the distance
correlation measure, it achieves higher power than MIC for many forms of
underlying association. For some functional relationships, GMIC surpasses all
other statistics calculated. Preliminary results suggest choosing a moderate
value of the tuning parameter for GMIC will yield a test that is robust across
underlying relationships. GMIC is a promising new method that mitigates the
power issues suffered by MIC, at the possible expense of equitability.
Nonetheless, distance correlation was in our simulations more powerful for many
forms of underlying relationships. At a minimum, this work motivates further
consideration of maximal information-based nonparametric exploration (MINE)
methods as statistical tests of independence.
Detection of copy-move forgery in digital images based on DCT
Authors: Nathalie Diane Wandji, Sun Xingming, Moise Fah Kue
Date: 2013-08-26 00:00:00

With rapid advances in digital information processing systems, and more
specifically in digital image processing software, there is a widespread
development of advanced tools and techniques for digital image forgery. One of
the techniques most commonly used is the Copy-move forgery which proceeds by
copying a part of an image and pasting it into the same image, in order to
maliciously hide an object or a region. In this paper, we propose a method to
detect this specific kind of counterfeit. Firstly, the color image is converted
from RGB color space to YCbCr color space and then the R, G, B and Y-component
are splitted into fixed-size overlapping blocks and, features are extracted
from the R, G and B-components image blocks on one hand and on the other, from
the DCT representation of the R, G, B and Ycomponent image block. The feature
vectors obtained are then lexicographically sorted to make similar image blocks
neighbors and duplicated image blocks are identified using Euclidean distance
as similarity criterion. Experimental results showed that the proposed method
can detect the duplicated regions when there is more than one copy move forged
area in the image and even in case of slight rotations, JPEG compression,
shift, scale, blur and noise addition.
A Comparison of Algorithms for Learning Hidden Variables in Normal Graphs
Authors: Francesco A. N. Palmieri
Date: 2013-08-26 00:00:00

A Bayesian factor graph reduced to normal form consists in the
interconnection of diverter units (or equal constraint units) and
Single-Input/Single-Output (SISO) blocks. In this framework localized
adaptation rules are explicitly derived from a constrained maximum likelihood
(ML) formulation and from a minimum KL-divergence criterion using KKT
conditions. The learning algorithms are compared with two other updating
equations based on a Viterbi-like and on a variational approximation
respectively. The performance of the various algorithm is verified on synthetic
data sets for various architectures. The objective of this paper is to provide
the programmer with explicit algorithms for rapid deployment of Bayesian graphs
in the applications.
Sparse and Non-Negative BSS for Noisy Data
Authors: Jérémy Rapin, Jérôme Bobin, Anthony Larue, Jean-Luc Starck
Date: 2013-08-26 00:00:00
Tasks: blind source separation, Diversity

Non-negative blind source separation (BSS) has raised interest in various
fields of research, as testified by the wide literature on the topic of
non-negative matrix factorization (NMF). In this context, it is fundamental
that the sources to be estimated present some diversity in order to be
efficiently retrieved. Sparsity is known to enhance such contrast between the
sources while producing very robust approaches, especially to noise. In this
paper we introduce a new algorithm in order to tackle the blind separation of
non-negative sparse sources from noisy measurements. We first show that
sparsity and non-negativity constraints have to be carefully applied on the
sought-after solution. In fact, improperly constrained solutions are unlikely
to be stable and are therefore sub-optimal. The proposed algorithm, named nGMCA
(non-negative Generalized Morphological Component Analysis), makes use of
proximal calculus techniques to provide properly constrained solutions. The
performance of nGMCA compared to other state-of-the-art algorithms is
demonstrated by numerical experiments encompassing a wide variety of settings,
with negligible parameter tuning. In particular, nGMCA is shown to provide
robustness to noise and performs well on synthetic mixtures of real NMR
spectra.
Linear models and linear mixed effects models in R with linguistic applications
Authors: Bodo Winter
Date: 2013-08-26 00:00:00

This text is a conceptual introduction to mixed effects modeling with
linguistic applications, using the R programming environment. The reader is
introduced to linear modeling and assumptions, as well as to mixed
effects/multilevel modeling, including a discussion of random intercepts,
random slopes and likelihood ratio tests. The example used throughout the text
focuses on the phonetic analysis of voice pitch data.
Fast learning rate of multiple kernel learning: Trade-off between sparsity and smoothness
Authors: Taiji Suzuki, Masashi Sugiyama
Date: 2012-03-02 00:00:00

We investigate the learning rate of multiple kernel learning (MKL) with
$\ell_1$ and elastic-net regularizations. The elastic-net regularization is a
composition of an $\ell_1$-regularizer for inducing the sparsity and an
$\ell_2$-regularizer for controlling the smoothness. We focus on a sparse
setting where the total number of kernels is large, but the number of nonzero
components of the ground truth is relatively small, and show sharper
convergence rates than the learning rates have ever shown for both $\ell_1$ and
elastic-net regularizations. Our analysis reveals some relations between the
choice of a regularization function and the performance. If the ground truth is
smooth, we show a faster convergence rate for the elastic-net regularization
with less conditions than $\ell_1$-regularization; otherwise, a faster
convergence rate for the $\ell_1$-regularization is shown.
Stability of Phase Retrievable Frames
Authors: Radu Balan
Date: 2013-08-25 00:00:00

In this paper we study the property of phase retrievability by redundant
sysems of vectors under perturbations of the frame set. Specifically we show
that if a set $\fc$ of $m$ vectors in the complex Hilbert space of dimension n
allows for vector reconstruction from magnitudes of its coefficients, then
there is a perturbation bound $\rho$ so that any frame set within $\rho$ from
$\fc$ has the same property. In particular this proves the recent construction
in \cite{BH13} is stable under perturbations. By the same token we reduce the
critical cardinality conjectured in \cite{BCMN13a} to proving a stability
result for non phase-retrievable frames.
A Max-Product EM Algorithm for Reconstructing Markov-tree Sparse Signals from Compressive Samples
Authors: Zhao Song, Aleksandar Dogandzic
Date: 2012-09-05 00:00:00
Tasks: Image Reconstruction

We propose a Bayesian expectation-maximization (EM) algorithm for
reconstructing Markov-tree sparse signals via belief propagation. The
measurements follow an underdetermined linear model where the
regression-coefficient vector is the sum of an unknown approximately sparse
signal and a zero-mean white Gaussian noise with an unknown variance. The
signal is composed of large- and small-magnitude components identified by
binary state variables whose probabilistic dependence structure is described by
a Markov tree. Gaussian priors are assigned to the signal coefficients given
their state variables and the Jeffreys' noninformative prior is assigned to the
noise variance. Our signal reconstruction scheme is based on an EM iteration
that aims at maximizing the posterior distribution of the signal and its state
variables given the noise variance. We construct the missing data for the EM
iteration so that the complete-data posterior distribution corresponds to a
hidden Markov tree (HMT) probabilistic graphical model that contains no loops
and implement its maximization (M) step via a max-product algorithm. This EM
algorithm estimates the vector of state variables as well as solves iteratively
a linear system of equations to obtain the corresponding signal estimate. We
select the noise variance so that the corresponding estimated signal and state
variables obtained upon convergence of the EM iteration have the largest
marginal posterior distribution. We compare the proposed and existing
state-of-the-art reconstruction methods via signal and image reconstruction
experiments.
On the Achievability of Cramér-Rao Bound In Noisy Compressed Sensing
Authors: Rad Niazadeh, Masoud Babaie-Zadeh, Christian Jutten
Date: 2010-06-13 00:00:00
Tasks: compressed sensing, LEMMA

Recently, it has been proved in Babadi et al. that in noisy compressed
sensing, a joint typical estimator can asymptotically achieve the Cramer-Rao
lower bound of the problem.To prove this result, this paper used a lemma,which
is provided in Akcakaya et al,that comprises the main building block of the
proof. This lemma is based on the assumption of Gaussianity of the measurement
matrix and its randomness in the domain of noise. In this correspondence, we
generalize the results obtained in Babadi et al by dropping the Gaussianity
assumption on the measurement matrix. In fact, by considering the measurement
matrix as a deterministic matrix in our analysis, we find a theorem similar to
the main theorem of Babadi et al for a family of randomly generated (but
deterministic in the noise domain) measurement matrices that satisfy a
generalized condition known as The Concentration of Measures Inequality. By
this, we finally show that under our generalized assumptions, the Cramer-Rao
bound of the estimation is achievable by using the typical estimator introduced
in Babadi et al.
A Literature Review: Stemming Algorithms for Indian Languages
Authors: M. Thangarasu, R. Manavalan
Date: 2013-08-25 00:00:00
Tasks: Survey

Stemming is the process of extracting root word from the given inflection
word. It also plays significant role in numerous application of Natural
Language Processing (NLP). The stemming problem has addressed in many contexts
and by researchers in many disciplines. This expository paper presents survey
of some of the latest developments on stemming algorithms in data mining and
also presents with some of the solutions for various Indian language stemming
algorithms along with the results.