pwc-archive/papers-with-abstracts huggingface dataset:
Design and Optimization of a Speech Recognition Front-End for Distant-Talking Control of a Music Playback Device
Authors: Ramin Pichevar, Jason Wung, Daniele Giacobello, Joshua Atkins
Date: 2014-05-05 00:00:00
Tasks: Speech Enhancement, speech-recognition, Speech Recognition

This paper addresses the challenging scenario for the distant-talking control
of a music playback device, a common portable speaker with four small
loudspeakers in close proximity to one microphone. The user controls the device
through voice, where the speech-to-music ratio can be as low as -30 dB during
music playback. We propose a speech enhancement front-end that relies on known
robust methods for echo cancellation, double-talk detection, and noise
suppression, as well as a novel adaptive quasi-binary mask that is well suited
for speech recognition. The optimization of the system is then formulated as a
large scale nonlinear programming problem where the recognition rate is
maximized and the optimal values for the system parameters are found through a
genetic algorithm. We validate our methodology by testing over the TIMIT
database for different music playback levels and noise types. Finally, we show
that the proposed front-end allows a natural interaction with the device for
limited-vocabulary voice commands.
Ridge Fusion in Statistical Learning
Authors: Bradley S. Price, Charles J. Geyer, Adam J. Rothman
Date: 2013-10-15 00:00:00
Tasks: Clustering

We propose a penalized likelihood method to jointly estimate multiple
precision matrices for use in quadratic discriminant analysis and model based
clustering. A ridge penalty and a ridge fusion penalty are used to introduce
shrinkage and promote similarity between precision matrix estimates. Block-wise
coordinate descent is used for optimization, and validation likelihood is used
for tuning parameter selection. Our method is applied in quadratic discriminant
analysis and semi-supervised model based clustering.
The Multi-engine ASP Solver ME-ASP: Progress Report
Authors: Marco Maratea, Luca Pulina, Francesco Ricca
Date: 2014-05-05 00:00:00
Tasks: General Classification

MEASP is a multi-engine solver for ground ASP programs. It exploits algorithm
selection techniques based on classification to select one among a set of
out-of-the-box heterogeneous ASP solvers used as black-box engines. In this
paper we report on (i) a new optimized implementation of MEASP; and (ii) an
attempt of applying algorithm selection to non-ground programs. An experimental
analysis reported in the paper shows that (i) the new implementation of \measp
is substantially faster than the previous version; and (ii) the multi-engine
recipe can be applied to the evaluation of non-ground programs with some
benefits.
K-NS: Section-Based Outlier Detection in High Dimensional Space
Authors: Zhana Bao
Date: 2014-05-05 00:00:00
Tasks: Outlier Detection, Vocal Bursts Intensity Prediction

Finding rare information hidden in a huge amount of data from the Internet is
a necessary but complex issue. Many researchers have studied this issue and
have found effective methods to detect anomaly data in low dimensional space.
However, as the dimension increases, most of these existing methods perform
poorly in detecting outliers because of "high dimensional curse". Even though
some approaches aim to solve this problem in high dimensional space, they can
only detect some anomaly data appearing in low dimensional space and cannot
detect all of anomaly data which appear differently in high dimensional space.
To cope with this problem, we propose a new k-nearest section-based method
(k-NS) in a section-based space. Our proposed approach not only detects
outliers in low dimensional space with section-density ratio but also detects
outliers in high dimensional space with the ratio of k-nearest section against
average value. After taking a series of experiments with the dimension from 10
to 10000, the experiment results show that our proposed method achieves 100%
precision and 100% recall result in the case of extremely high dimensional
space, and better improvement in low dimensional space compared to our
previously proposed method.
Robust Subspace Outlier Detection in High Dimensional Space
Authors: Zhana Bao
Date: 2014-05-05 00:00:00
Tasks: Outlier Detection, Vocal Bursts Intensity Prediction

Rare data in a large-scale database are called outliers that reveal
significant information in the real world. The subspace-based outlier detection
is regarded as a feasible approach in very high dimensional space. However, the
outliers found in subspaces are only part of the true outliers in high
dimensional space, indeed. The outliers hidden in normal-clustered points are
sometimes neglected in the projected dimensional subspace. In this paper, we
propose a robust subspace method for detecting such inner outliers in a given
dataset, which uses two dimensional-projections: detecting outliers in
subspaces with local density ratio in the first projected dimensions; finding
outliers by comparing neighbor's positions in the second projected dimensions.
Each point's weight is calculated by summing up all related values got in the
two steps projected dimensions, and then the points scoring the largest weight
values are taken as outliers. By taking a series of experiments with the number
of dimensions from 10 to 10000, the results show that our proposed method
achieves high precision in the case of extremely high dimensional space, and
works well in low dimensional space.
Finding Inner Outliers in High Dimensional Space
Authors: Zhana Bao
Date: 2014-05-05 00:00:00
Tasks: Outlier Detection, Vocal Bursts Intensity Prediction

Outlier detection in a large-scale database is a significant and complex
issue in knowledge discovering field. As the data distributions are obscure and
uncertain in high dimensional space, most existing solutions try to solve the
issue taking into account the two intuitive points: first, outliers are
extremely far away from other points in high dimensional space; second,
outliers are detected obviously different in projected-dimensional subspaces.
However, for a complicated case that outliers are hidden inside the normal
points in all dimensions, existing detection methods fail to find such inner
outliers. In this paper, we propose a method with twice dimension-projections,
which integrates primary subspace outlier detection and secondary
point-projection between subspaces, and sums up the multiple weight values for
each point. The points are computed with local density ratio separately in
twice-projected dimensions. After the process, outliers are those points
scoring the largest values of weight. The proposed method succeeds to find all
inner outliers on the synthetic test datasets with the dimension varying from
100 to 10000. The experimental results also show that the proposed algorithm
can work in low dimensional space and can achieve perfect performance in high
dimensional space. As for this reason, our proposed approach has considerable
potential to apply it in multimedia applications helping to process images or
video with large-scale attributes.
Generalized Risk-Aversion in Stochastic Multi-Armed Bandits
Authors: Alexander Zimin, Rasmus Ibsen-Jensen, Krishnendu Chatterjee
Date: 2014-05-05 00:00:00
Tasks: Multi-Armed Bandits

We consider the problem of minimizing the regret in stochastic multi-armed
bandit, when the measure of goodness of an arm is not the mean return, but some
general function of the mean and the variance.We characterize the conditions
under which learning is possible and present examples for which no natural
algorithm can achieve sublinear regret.
Implementing Default and Autoepistemic Logics via the Logic of GK
Authors: Jianmin Ji, Hannes Strass
Date: 2014-05-05 00:00:00
Tasks: Translation

The logic of knowledge and justified assumptions, also known as logic of
grounded knowledge (GK), was proposed by Lin and Shoham as a general logic for
nonmonotonic reasoning. To date, it has been used to embed in it default logic
(propositional case), autoepistemic logic, Turner's logic of universal
causation, and general logic programming under stable model semantics. Besides
showing the generality of GK as a logic for nonmonotonic reasoning, these
embeddings shed light on the relationships among these other logics. In this
paper, for the first time, we show how the logic of GK can be embedded into
disjunctive logic programming in a polynomial but non-modular translation with
new variables. The result can then be used to compute the extension/expansion
semantics of default logic, autoepistemic logic and Turner's logic of universal
causation by disjunctive ASP solvers such as claspD(-2), DLV, GNT and cmodels.
On the Relative Expressiveness of Argumentation Frameworks, Normal Logic Programs and Abstract Dialectical Frameworks
Authors: Hannes Strass
Date: 2014-05-05 00:00:00
Tasks: Abstract Argumentation

We analyse the expressiveness of the two-valued semantics of abstract
argumentation frameworks, normal logic programs and abstract dialectical
frameworks. By expressiveness we mean the ability to encode a desired set of
two-valued interpretations over a given propositional signature using only
atoms from that signature. While the computational complexity of the two-valued
model existence problem for all these languages is (almost) the same, we show
that the languages form a neat hierarchy with respect to their expressiveness.
Belief revision in the propositional closure of a qualitative algebra (extended version)
Authors: Valmi Dufour-Lussier, Alice Hermann, Florence Le Ber, Jean Lieber
Date: 2014-05-05 00:00:00

Belief revision is an operation that aims at modifying old beliefs so that
they become consistent with new ones. The issue of belief revision has been
studied in various formalisms, in particular, in qualitative algebras (QAs) in
which the result is a disjunction of belief bases that is not necessarily
representable in a QA. This motivates the study of belief revision in
formalisms extending QAs, namely, their propositional closures: in such a
closure, the result of belief revision belongs to the formalism. Moreover, this
makes it possible to define a contraction operator thanks to the Harper
identity. Belief revision in the propositional closure of QAs is studied, an
algorithm for a family of revision operators is designed, and an open-source
implementation is made freely available on the web. (This is the extended
version of an article originally presented at the 14th International Conference
on Principles of Knowledge Representation and Reasoning.)
On Exact Learning Monotone DNF from Membership Queries
Authors: Hasan Abasi, Nader H. Bshouty, Hanna Mazzawi
Date: 2014-05-05 00:00:00

In this paper, we study the problem of learning a monotone DNF with at most
$s$ terms of size (number of variables in each term) at most $r$ ($s$ term
$r$-MDNF) from membership queries. This problem is equivalent to the problem of
learning a general hypergraph using hyperedge-detecting queries, a problem
motivated by applications arising in chemical reactions and genome sequencing.
  We first present new lower bounds for this problem and then present
deterministic and randomized adaptive algorithms with query complexities that
are almost optimal. All the algorithms we present in this paper run in time
linear in the query complexity and the number of variables $n$. In addition,
all of the algorithms we present in this paper are asymptotically tight for
fixed $r$ and/or $s$.
A Structural Approach to Coordinate-Free Statistics
Authors: Tom LaGatta, P. Richard Hahn
Date: 2014-05-01 00:00:00
Tasks: BIG-bench Machine Learning

We consider the question of learning in general topological vector spaces. By
exploiting known (or parametrized) covariance structures, our Main Theorem
demonstrates that any continuous linear map corresponds to a certain
isomorphism of embedded Hilbert spaces. By inverting this isomorphism and
extending continuously, we construct a version of the Ordinary Least Squares
estimator in absolute generality. Our Gauss-Markov theorem demonstrates that
OLS is a "best linear unbiased estimator", extending the classical result. We
construct a stochastic version of the OLS estimator, which is a continuous
disintegration exactly for the class of "uncorrelated implies independent"
(UII) measures. As a consequence, Gaussian measures always exhibit continuous
disintegrations through continuous linear maps, extending a theorem of the
first author. Applying this framework to some problems in machine learning, we
prove a useful representation theorem for covariance tensors, and show that OLS
defines a good kriging predictor for vector-valued arrays on general index
spaces. We also construct a support-vector machine classifier in this setting.
We hope that our article shines light on some deeper connections between
probability theory, statistics and machine learning, and may serve as a point
of intersection for these three communities.
Analysis Tool for UNL-Based Knowledge Representation
Authors: Shamim Ripon, Aoyan Barua, Mohammad Salah Uddin
Date: 2014-05-04 00:00:00
Tasks: Machine Translation, Sentence, Translation

The fundamental issue in knowledge representation is to provide a precise
definition of the knowledge that they possess in a manner that is independent
of procedural considerations, context free and easy to manipulate, exchange and
reason about. Knowledge must be accessible to everyone regardless of their
native languages. Universal Networking Language (UNL) is a declarative formal
language and a generalized form of human language in a machine independent
digital platform for defining, recapitulating, amending, storing and
dissipating knowledge among people of different affiliations. UNL extracts
semantic data from a native language for Interlingua machine translation. This
paper presents the development of a graphical tool that incorporates UNL to
provide a visual mean to represent the semantic data available in a native
text. UNL represents the semantics of a sentence as a conceptual hyper-graph.
We translate this information into XML format and create a graph from XML,
representing the actual concepts available in the native language
Probabilistic Inductive Logic Programming Based on Answer Set Programming
Authors: Matthias Nickles, Alessandra Mileo
Date: 2014-05-04 00:00:00
Tasks: Inductive logic programming, parameter estimation

We propose a new formal language for the expressive representation of
probabilistic knowledge based on Answer Set Programming (ASP). It allows for
the annotation of first-order formulas as well as ASP rules and facts with
probabilities and for learning of such weights from data (parameter
estimation). Weighted formulas are given a semantics in terms of soft and hard
constraints which determine a probability distribution over answer sets. In
contrast to related approaches, we approach inference by optionally utilizing
so-called streamlining XOR constraints, in order to reduce the number of
computed answer sets. Our approach is prototypically implemented. Examples
illustrate the introduced concepts and point at issues and topics for future
research.
"Translation can't change a name": Using Multilingual Data for Named Entity Recognition
Authors: Manaal Faruqui
Date: 2014-05-04 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Translation

Named Entities (NEs) are often written with no orthographic changes across
different languages that share a common alphabet. We show that this can be
leveraged so as to improve named entity recognition (NER) by using unsupervised
word clusters from secondary languages as features in state-of-the-art
discriminative NER systems. We observe significant increases in performance,
finding that person and location identification is particularly improved, and
that phylogenetically close languages provide more valuable features than more
distant languages.
Review of Statistical Shape Spaces for 3D Data with Comparative Analysis for Human Faces
Authors: Alan Brunton, Augusto Salazar, Timo Bolkart, Stefanie Wuhrer
Date: 2012-09-28 00:00:00

With systems for acquiring 3D surface data being evermore commonplace, it has
become important to reliably extract specific shapes from the acquired data. In
the presence of noise and occlusions, this can be done through the use of
statistical shape models, which are learned from databases of clean examples of
the shape in question. In this paper, we review, analyze and compare different
statistical models: from those that analyze the variation in geometry globally
to those that analyze the variation in geometry locally. We first review how
different types of models have been used in the literature, then proceed to
define the models and analyze them theoretically, in terms of both their
statistical and computational aspects. We then perform extensive experimental
comparison on the task of model fitting, and give intuition about which type of
model is better for a few applications. Due to the wide availability of
databases of high-quality data, we use the human face as the specific shape we
wish to extract from corrupted data.
Feature Selection On Boolean Symbolic Objects
Authors: Djamal Ziani
Date: 2014-05-04 00:00:00
Tasks: feature selection

With the boom in IT technology, the data sets used in application are more
and more larger and are described by a huge number of attributes, therefore,
the feature selection become an important discipline in Knowledge discovery and
data mining, allowing the experts to select the most relevant features to
improve the quality of their studies and to reduce the time processing of their
algorithm. In addition to that, the data used by the applications become
richer. They are now represented by a set of complex and structured objects,
instead of simple numerical matrixes. The purpose of our algorithm is to do
feature selection on rich data, called Boolean Symbolic Objects (BSOs). These
objects are described by multivalued features. The BSOs are considered as
higher level units which can model complex data, such as cluster of
individuals, aggregated data or taxonomies. In this paper we will introduce a
new feature selection criterion for BSOs, and we will explain how we improved
its complexity.
Rule of Three for Superresolution of Still Images with Applications to Compression and Denoising
Authors: Mario Mastriani
Date: 2014-05-04 00:00:00
Tasks: Denoising

We describe a new method for superresolution of still images (in the wavelet
domain) based on the reconstruction of missing details subbands pixels at a
given ith level via Rule of Three (Ro3) between pixels of approximation subband
of such level, and pixels of approximation and detail subbands of (i+1)th
level. The histogramic profiles demonstrate that Ro3 is the appropriate
mechanism to recover missing detail subband pixels in these cases. Besides,
with the elimination of the details subbands pixels (in an eventual compression
scheme), we obtain a bigger compression rate. Experimental results demonstrate
that our approach compares favorably to more typical methods of denoising and
compression in wavelet domain. Our method does not compress, but facilitates
the action of the real compressor, in our case, Joint Photographic Experts
Group (JPEG) and JPEg2000, that is, Ro3 acts as a catalyst compression
Automated Attribution and Intertextual Analysis
Authors: James Brofos, Ajay Kannan, Rui Shu
Date: 2014-05-03 00:00:00
Tasks: Author Attribution, Authorship Attribution, BIG-bench Machine Learning

In this work, we employ quantitative methods from the realm of statistics and
machine learning to develop novel methodologies for author attribution and
textual analysis. In particular, we develop techniques and software suitable
for applications to Classical study, and we illustrate the efficacy of our
approach in several interesting open questions in the field. We apply our
numerical analysis techniques to questions of authorship attribution in the
case of the Greek tragedian Euripides, to instances of intertextuality and
influence in the poetry of the Roman statesman Seneca the Younger, and to cases
of "interpolated" text with respect to the histories of Livy.
Extracting Family Relationship Networks from Novels
Authors: Aibek Makazhanov, Denilson Barbosa, Grzegorz Kondrak
Date: 2014-05-03 00:00:00

We present an approach to the extraction of family relations from literary
narrative, which incorporates a technique for utterance attribution proposed
recently by Elson and McKeown (2010). In our work this technique is used in
combination with the detection of vocatives - the explicit forms of address
used by the characters in a novel. We take advantage of the fact that certain
vocatives indicate family relations between speakers. The extracted relations
are then propagated using a set of rules. We report the results of the
application of our method to Jane Austen's Pride and Prejudice.
Why (and When and How) Contrastive Divergence Works
Authors: Ian E Fellows
Date: 2014-05-03 00:00:00

Contrastive divergence (CD) is a promising method of inference in high
dimensional distributions with intractable normalizing constants, however, the
theoretical foundations justifying its use are somewhat shaky. This document
proposes a framework for understanding CD inference, how/when it works, and
provides multiple justifications for the CD moment conditions, including
framing them as a variational approximation. Algorithms for performing
inference are discussed and are applied to social network data using an
exponential-family random graph models (ERGM). The framework also provides
guidance about how to construct MCMC kernels providing good CD inference, which
turn out to be quite different from those used typically to provide fast global
mixing.
Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision
Authors: Xuehan Xiong, Fernando de la Torre
Date: 2014-05-03 00:00:00
Tasks: 3D Pose Estimation, Camera Calibration, Pose Estimation

Many computer vision problems (e.g., camera calibration, image alignment,
structure from motion) are solved with nonlinear optimization methods. It is
generally accepted that second order descent methods are the most robust, fast,
and reliable approaches for nonlinear optimization of a general smooth
function. However, in the context of computer vision, second order descent
methods have two main drawbacks: (1) the function might not be analytically
differentiable and numerical approximations are impractical, and (2) the
Hessian may be large and not positive definite. To address these issues, this
paper proposes generic descent maps, which are average "descent directions" and
rescaling factors learned in a supervised fashion. Using generic descent maps,
we derive a practical algorithm - Supervised Descent Method (SDM) - for
minimizing Nonlinear Least Squares (NLS) problems. During training, SDM learns
a sequence of decent maps that minimize the NLS. In testing, SDM minimizes the
NLS objective using the learned descent maps without computing the Jacobian or
the Hessian. We prove the conditions under which the SDM is guaranteed to
converge. We illustrate the effectiveness and accuracy of SDM in three computer
vision problems: rigid image alignment, non-rigid image alignment, and 3D pose
estimation. In particular, we show how SDM achieves state-of-the-art
performance in the problem of facial feature detection. The code has been made
available at www.humansensing.cs.cmu.edu/intraface.