pwc-archive/papers-with-abstracts huggingface dataset:
Improving Statistical Machine Translation for a Resource-Poor Language Using Related Resource-Rich Languages
Authors: Preslav Ivanov Nakov, Hwee Tou Ng
Date: 2014-01-23 00:00:00
Tasks: Machine Translation, Translation, Transliteration

We propose a novel language-independent approach for improving machine
translation for resource-poor languages by exploiting their similarity to
resource-rich ones. More precisely, we improve the translation from a
resource-poor source language X_1 into a resource-rich language Y given a
bi-text containing a limited number of parallel sentences for X_1-Y and a
larger bi-text for X_2-Y for some resource-rich language X_2 that is closely
related to X_1. This is achieved by taking advantage of the opportunities that
vocabulary overlap and similarities between the languages X_1 and X_2 in
spelling, word order, and syntax offer: (1) we improve the word alignments for
the resource-poor language, (2) we further augment it with additional
translation options, and (3) we take care of potential spelling differences
through appropriate transliteration. The evaluation for Indonesian- >English
using Malay and for Spanish -> English using Portuguese and pretending Spanish
is resource-poor shows an absolute gain of up to 1.35 and 3.37 BLEU points,
respectively, which is an improvement over the best rivaling approaches, while
using much less additional data. Overall, our method cuts the amount of
necessary "real training data by a factor of 2--5.
Algorithms and Limits for Compact Plan Representations
Authors: Christer Bäckström, Peter Jonsson
Date: 2014-01-23 00:00:00

Compact representations of objects is a common concept in computer science.
Automated planning can be viewed as a case of this concept: a planning instance
is a compact implicit representation of a graph and the problem is to find a
path (a plan) in this graph. While the graphs themselves are represented
compactly as planning instances, the paths are usually represented explicitly
as sequences of actions. Some cases are known where the plans always have
compact representations, for example, using macros. We show that these results
do not extend to the general case, by proving a number of bounds for compact
representations of plans under various criteria, like efficient sequential or
random access of actions. In addition to this, we show that our results have
consequences for what can be gained from reformulating planning into some other
problem. As a contrast to this we also prove a number of positive results,
demonstrating restricted cases where plans do have useful compact
representations, as well as proving that macro plans have favourable access
properties. Our results are finally discussed in relation to other relevant
contexts.
Collaborative Regression
Authors: Samuel M. Gross, Robert Tibshirani
Date: 2014-01-22 00:00:00
Tasks: regression

We consider the scenario where one observes an outcome variable and sets of
features from multiple assays, all measured on the same set of samples. One
approach that has been proposed for dealing with this type of data is ``sparse
multiple canonical correlation analysis'' (sparse mCCA). All of the current
sparse mCCA techniques are biconvex and thus have no guarantees about reaching
a global optimum. We propose a method for performing sparse supervised
canonical correlation analysis (sparse sCCA), a specific case of sparse mCCA
when one of the datasets is a vector. Our proposal for sparse sCCA is convex
and thus does not face the same difficulties as the other methods. We derive
efficient algorithms for this problem, and illustrate their use on simulated
and real data.
GGP with Advanced Reasoning and Board Knowledge Discovery
Authors: Adrian Łańcucki
Date: 2014-01-22 00:00:00

Quality of General Game Playing (GGP) matches suffers from slow
state-switching and weak knowledge modules. Instantiation and Propositional
Networks offer great performance gains over Prolog-based reasoning, but do not
scale well. In this publication mGDL, a variant of GDL stripped of function
constants, has been defined as a basis for simple reasoning machines. mGDL
allows to easily map rules to C++ functions. 253 out of 270 tested GDL rule
sheets conformed to mGDL without any modifications; the rest required minor
changes. A revised (m)GDL to C++ translation scheme has been reevaluated; it
brought gains ranging from 28% to 7300% over YAP Prolog, managing to compile
even demanding rule sheets under few seconds. For strengthening game knowledge,
spatial features inspired by similar successful techniques from computer Go
have been proposed. For they required an Euclidean metric, a small board
extension to GDL has been defined through a set of ground atomic sentences. An
SGA-based genetic algorithm has been designed for tweaking game parameters and
conducting self-plays, so the features could be mined from meaningful game
records. The approach has been tested on a small cluster, giving performance
gains up to 20% more wins against the baseline UCT player. Implementations of
proposed ideas constitutes the core of GGP Spatium - a small C++/Python GGP
framework, created for developing compact GGP Players and problem solvers.
Node-Based Learning of Multiple Gaussian Graphical Models
Authors: Karthik Mohan, Palma London, Maryam Fazel, Daniela Witten, Su-In Lee
Date: 2013-03-21 00:00:00

We consider the problem of estimating high-dimensional Gaussian graphical
models corresponding to a single set of variables under several distinct
conditions. This problem is motivated by the task of recovering transcriptional
regulatory networks on the basis of gene expression data {containing
heterogeneous samples, such as different disease states, multiple species, or
different developmental stages}. We assume that most aspects of the conditional
dependence networks are shared, but that there are some structured differences
between them. Rather than assuming that similarities and differences between
networks are driven by individual edges, we take a node-based approach, which
in many cases provides a more intuitive interpretation of the network
differences. We consider estimation under two distinct assumptions: (1)
differences between the K networks are due to individual nodes that are
perturbed across conditions, or (2) similarities among the K networks are due
to the presence of common hub nodes that are shared across all K networks.
Using a row-column overlap norm penalty function, we formulate two convex
optimization problems that correspond to these two assumptions. We solve these
problems using an alternating direction method of multipliers algorithm, and we
derive a set of necessary and sufficient conditions that allows us to decompose
the problem into independent subproblems so that our algorithm can be scaled to
high-dimensional settings. Our proposal is illustrated on synthetic data, a
webpage data set, and a brain cancer gene expression data set.
A Unified Approach to Universal Prediction: Generalized Upper and Lower Bounds
Authors: N. Denizcan Vanli, Suleyman S. Kozat
Date: 2013-11-25 00:00:00
Tasks: Learning Theory, Mixture-of-Experts

We study sequential prediction of real-valued, arbitrary and unknown
sequences under the squared error loss as well as the best parametric predictor
out of a large, continuous class of predictors. Inspired by recent results from
computational learning theory, we refrain from any statistical assumptions and
define the performance with respect to the class of general parametric
predictors. In particular, we present generic lower and upper bounds on this
relative performance by transforming the prediction task into a parameter
learning problem. We first introduce the lower bounds on this relative
performance in the mixture of experts framework, where we show that for any
sequential algorithm, there always exists a sequence for which the performance
of the sequential algorithm is lower bounded by zero. We then introduce a
sequential learning algorithm to predict such arbitrary and unknown sequences,
and calculate upper bounds on its total squared prediction error for every
bounded sequence. We further show that in some scenarios we achieve matching
lower and upper bounds demonstrating that our algorithms are optimal in a
strong minimax sense such that their performances cannot be improved further.
As an interesting result we also prove that for the worst case scenario, the
performance of randomized algorithms can be achieved by sequential algorithms
so that randomized algorithms does not improve the performance.
Numerical weather prediction or stochastic modeling: an objective criterion of choice for the global radiation forecasting
Authors: Cyril Voyant, Gilles Notton, Christophe Paoli, Marie Laure Nivet, Marc Muselli, Kahina Dahmani
Date: 2014-01-22 00:00:00

Numerous methods exist and were developed for global radiation forecasting.
The two most popular types are the numerical weather predictions (NWP) and the
predictions using stochastic approaches. We propose to compute a parameter
noted constructed in part from the mutual information which is a quantity that
measures the mutual dependence of two variables. Both of these are calculated
with the objective to establish the more relevant method between NWP and
stochastic models concerning the current problem.
Finding the True Frequent Itemsets
Authors: Matteo Riondato, Fabio Vandin
Date: 2013-01-07 00:00:00
Tasks: Learning Theory

Frequent Itemsets (FIs) mining is a fundamental primitive in data mining. It
requires to identify all itemsets appearing in at least a fraction $\theta$ of
a transactional dataset $\mathcal{D}$. Often though, the ultimate goal of
mining $\mathcal{D}$ is not an analysis of the dataset \emph{per se}, but the
understanding of the underlying process that generated it. Specifically, in
many applications $\mathcal{D}$ is a collection of samples obtained from an
unknown probability distribution $\pi$ on transactions, and by extracting the
FIs in $\mathcal{D}$ one attempts to infer itemsets that are frequently (i.e.,
with probability at least $\theta$) generated by $\pi$, which we call the True
Frequent Itemsets (TFIs). Due to the inherently stochastic nature of the
generative process, the set of FIs is only a rough approximation of the set of
TFIs, as it often contains a huge number of \emph{false positives}, i.e.,
spurious itemsets that are not among the TFIs. In this work we design and
analyze an algorithm to identify a threshold $\hat{\theta}$ such that the
collection of itemsets with frequency at least $\hat{\theta}$ in $\mathcal{D}$
contains only TFIs with probability at least $1-\delta$, for some
user-specified $\delta$. Our method uses results from statistical learning
theory involving the (empirical) VC-dimension of the problem at hand. This
allows us to identify almost all the TFIs without including any false positive.
We also experimentally compare our method with the direct mining of
$\mathcal{D}$ at frequency $\theta$ and with techniques based on widely-used
standard bounds (i.e., the Chernoff bounds) of the binomial distribution, and
show that our algorithm outperforms these methods and achieves even better
results than what is guaranteed by the theoretical analysis.
A new keyphrases extraction method based on suffix tree data structure for arabic documents clustering
Authors: Issam Sahmoudi, Hanane Froud, Abdelmonaime Lachkar
Date: 2014-01-22 00:00:00
Tasks: Clustering

Document Clustering is a branch of a larger area of scientific study known as
data mining .which is an unsupervised classification using to find a structure
in a collection of unlabeled data. The useful information in the documents can
be accompanied by a large amount of noise words when using Full Text
Representation, and therefore will affect negatively the result of the
clustering process. So it is with great need to eliminate the noise words and
keeping just the useful information in order to enhance the quality of the
clustering results. This problem occurs with different degree for any language
such as English, European, Hindi, Chinese, and Arabic Language. To overcome
this problem, in this paper, we propose a new and efficient Keyphrases
extraction method based on the Suffix Tree data structure (KpST), the extracted
Keyphrases are then used in the clustering process instead of Full Text
Representation. The proposed method for Keyphrases extraction is language
independent and therefore it may be applied to any language. In this
investigation, we are interested to deal with the Arabic language which is one
of the most complex languages. To evaluate our method, we conduct an
experimental study on Arabic Documents using the most popular Clustering
approach of Hierarchical algorithms: Agglomerative Hierarchical algorithm with
seven linkage techniques and a variety of distance functions and similarity
measures to perform Arabic Document Clustering task. The obtained results show
that our method for extracting Keyphrases increases the quality of the
clustering results. We propose also to study the effect of using the stemming
for the testing dataset to cluster it with the same documents clustering
techniques and similarity/distance measures.
Causal Discovery in a Binary Exclusive-or Skew Acyclic Model: BExSAM
Authors: Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki, Akihiro Yamamoto, Yoshinobu Kawahara
Date: 2014-01-22 00:00:00
Tasks: Causal Discovery

Discovering causal relations among observed variables in a given data set is
a major objective in studies of statistics and artificial intelligence.
Recently, some techniques to discover a unique causal model have been explored
based on non-Gaussianity of the observed data distribution. However, most of
these are limited to continuous data. In this paper, we present a novel causal
model for binary data and propose an efficient new approach to deriving the
unique causal model governing a given binary data set under skew distributions
of external binary noises. Experimental evaluation shows excellent performance
for both artificial and real world data sets.
Enhancing Template Security of Face Biometrics by Using Edge Detection and Hashing
Authors: Manoj Krishnaswamy, G. Hemantha Kumar
Date: 2014-01-22 00:00:00
Tasks: Edge Detection, Ethics

In this paper we address the issues of using edge detection techniques on facial images to produce cancellable biometric templates and a novel method for template verification against tampering. With increasing use of biometrics, there is a real threat for the conventional systems using face databases, which store images of users in raw and unaltered form. If compromised not only it is irrevocable, but can be misused for cross-matching across different databases. So it is desirable to generate and store revocable templates for the same user in different applications to prevent cross-matching and to enhance security, while maintaining privacy and ethics. By comparing different edge detection methods it has been observed that the edge detection based on the Roberts Cross operator performs consistently well across multiple face datasets, in which the face images have been taken under a variety of conditions. We have proposed a novel scheme using hashing, for extra verification, in order to harden the security of the stored biometric templates.
Identifiability of an Integer Modular Acyclic Additive Noise Model and its Causal Structure Discovery
Authors: Joe Suzuki, Takanori Inazumi, Takashi Washio, Shohei Shimizu
Date: 2014-01-22 00:00:00

The notion of causality is used in many situations dealing with uncertainty.
We consider the problem whether causality can be identified given data set
generated by discrete random variables rather than continuous ones. In
particular, for non-binary data, thus far it was only known that causality can
be identified except rare cases. In this paper, we present necessary and
sufficient condition for an integer modular acyclic additive noise (IMAN) of
two variables. In addition, we relate bivariate and multivariate causal
identifiability in a more explicit manner, and develop a practical algorithm to
find the order of variables and their parent sets. We demonstrate its
performance in applications to artificial data and real world body motion data
with comparisons to conventional methods.
The Gabor-Einstein Wavelet: A Model for the Receptive Fields of V1 to MT Neurons
Authors: Stephen G. Odaibo
Date: 2014-01-22 00:00:00

Our visual system is astonishingly efficient at detecting moving objects.
This process is mediated by the neurons which connect the primary visual cortex
(V1) to the middle temporal (MT) area. Interestingly, since Kuffler's
pioneering experiments on retinal ganglion cells, mathematical models have been
vital for advancing our understanding of the receptive fields of visual
neurons. However, existing models were not designed to describe the most
salient attributes of the highly specialized neurons in the V1 to MT motion
processing stream; and they have not been able to do so. Here, we introduce the
Gabor-Einstein wavelet, a new family of functions for representing the
receptive fields of V1 to MT neurons. We show that the way space and time are
mixed in the visual cortex is analogous to the way they are mixed in the
special theory of relativity (STR). Hence we constrained the Gabor-Einstein
model by requiring: (i) relativistic-invariance of the wave carrier, and (ii)
the minimum possible number of parameters. From these two constraints, the sinc
function emerged as a natural descriptor of the wave carrier. The particular
distribution of lowpass to bandpass temporal frequency filtering properties of
V1 to MT neurons (Foster et al 1985; DeAngelis et al 1993b; Hawken et al 1996)
is clearly explained by the Gabor-Einstein basis. Furthermore, it does so in a
manner innately representative of the motion-processing stream's neuronal
hierarchy. Our analysis and computer simulations show that the distribution of
temporal frequency filtering properties along the motion processing stream is a
direct effect of the way the brain jointly encodes space and time. We uncovered
this fundamental link by demonstrating that analogous mathematical structures
underlie STR and joint cortical spacetime encoding. This link will provide new
physiological insights into how the brain represents visual information.
License Plate Recognition (LPR): A Review with Experiments for Malaysia Case Study
Authors: Nuzulha Khilwani Ibrahim, Emaliana Kasmuri, Norazira A Jalil, Mohd Adili Norasikin, Sazilah Salam, Mohamad Riduwan Md Nawawi
Date: 2014-01-22 00:00:00
Tasks: License Plate Recognition, Management

Most vehicle license plate recognition use neural network techniques to
enhance its computing capability. The image of the vehicle license plate is
captured and processed to produce a textual output for further processing. This
paper reviews image processing and neural network techniques applied at
different stages which are preprocessing, filtering, feature extraction,
segmentation and recognition in such way to remove the noise of the image, to
enhance the image quality and to expedite the computing process by converting
the characters in the image into respective text. An exemplar experiment has
been done in MATLAB to show the basic process of the image processing
especially for license plate in Malaysia case study. An algorithm is adapted
into the solution for parking management system. The solution then is
implemented as proof of concept to the algorithm.
On the symmetrical Kullback-Leibler Jeffreys centroids
Authors: Frank Nielsen
Date: 2013-03-29 00:00:00
Tasks: Clustering

Due to the success of the bag-of-word modeling paradigm, clustering
histograms has become an important ingredient of modern information processing.
Clustering histograms can be performed using the celebrated $k$-means
centroid-based algorithm. From the viewpoint of applications, it is usually
required to deal with symmetric distances. In this letter, we consider the
Jeffreys divergence that symmetrizes the Kullback-Leibler divergence, and
investigate the computation of Jeffreys centroids. We first prove that the
Jeffreys centroid can be expressed analytically using the Lambert $W$ function
for positive histograms. We then show how to obtain a fast guaranteed
approximation when dealing with frequency histograms. Finally, we conclude with
some remarks on the $k$-means histogram clustering.
Distributed Online Learning in Social Recommender Systems
Authors: Cem Tekin, Simpson Zhang, Mihaela van der Schaar
Date: 2013-09-26 00:00:00
Tasks: Decision Making, Recommendation Systems, Sequential Decision Making

In this paper, we consider decentralized sequential decision making in
distributed online recommender systems, where items are recommended to users
based on their search query as well as their specific background including
history of bought items, gender and age, all of which comprise the context
information of the user. In contrast to centralized recommender systems, in
which there is a single centralized seller who has access to the complete
inventory of items as well as the complete record of sales and user
information, in decentralized recommender systems each seller/learner only has
access to the inventory of items and user information for its own products and
not the products and user information of other sellers, but can get commission
if it sells an item of another seller. Therefore the sellers must distributedly
find out for an incoming user which items to recommend (from the set of own
items or items of another seller), in order to maximize the revenue from own
sales and commissions. We formulate this problem as a cooperative contextual
bandit problem, analytically bound the performance of the sellers compared to
the best recommendation strategy given the complete realization of user
arrivals and the inventory of items, as well as the context-dependent purchase
probabilities of each item, and verify our results via numerical examples on a
distributed data set adapted based on Amazon data. We evaluate the dependence
of the performance of a seller on the inventory of items the seller has, the
number of connections it has with the other sellers, and the commissions which
the seller gets by selling items of other sellers to its users.
Guaranteed Model Order Estimation and Sample Complexity Bounds for LDA
Authors: E. D. Gutiérrez
Date: 2013-12-10 00:00:00

The question of how to determine the number of independent latent factors
(topics) in mixture models such as Latent Dirichlet Allocation (LDA) is of
great practical importance. In most applications, the exact number of topics is
unknown, and depends on the application and the size of the data set. Bayesian
nonparametric methods can avoid the problem of topic number selection, but they
can be impracticably slow for large sample sizes and are subject to local
optima. We develop a guaranteed procedure for topic number recovery that does
not necessitate learning the model's latent parameters beforehand. Our
procedure relies on adapting results from random matrix theory. Performance of
our topic number recovery procedure is superior to hLDA, a nonparametric
method. We also discuss some implications of our results on the sample
complexity and accuracy of popular spectral learning algorithms for LDA. Our
results and procedure can be extended to spectral learning algorithms for other
exchangeable mixture models as well as Hidden Markov Models.