pwc-archive/papers-with-abstracts huggingface dataset:
From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales
Authors: Saif Mohammad
Date: 2013-09-23 00:00:00
Tasks: Sentiment Analysis

Today we have access to unprecedented amounts of literary texts. However,
search still relies heavily on key words. In this paper, we show how sentiment
analysis can be used in tandem with effective visualizations to quantify and
track emotions in both individual books and across very large collections. We
introduce the concept of emotion word density, and using the Brothers Grimm
fairy tales as example, we show how collections of text can be organized for
better search. Using the Google Books Corpus we show how to determine an
entity's emotion associations from co-occurring words. Finally, we compare
emotion words in fairy tales and novels, to show that fairy tales have a much
wider range of emotion word densities than novels.
Fenchel Duals for Drifting Adversaries
Authors: Suman K. Bera, Anamitra R. Choudhury, Syamantak Das, Sambuddha Roy, Jayram S. Thatchachar
Date: 2013-09-23 00:00:00

We describe a primal-dual framework for the design and analysis of online
convex optimization algorithms for {\em drifting regret}. Existing literature
shows (nearly) optimal drifting regret bounds only for the $\ell_2$ and the
$\ell_1$-norms. Our work provides a connection between these algorithms and the
Online Mirror Descent ($\omd$) updates; one key insight that results from our
work is that in order for these algorithms to succeed, it suffices to have the
gradient of the regularizer to be bounded (in an appropriate norm). For
situations (like for the $\ell_1$ norm) where the vanilla regularizer does not
have this property, we have to {\em shift} the regularizer to ensure this.
Thus, this helps explain the various updates presented in \cite{bansal10,
buchbinder12}. We also consider the online variant of the problem with
1-lookahead, and with movement costs in the $\ell_2$-norm. Our primal dual
approach yields nearly optimal competitive ratios for this problem.
On the Success Rate of Crossover Operators for Genetic Programming with Offspring Selection
Authors: Gabriel Kronberger, Stephan Winkler, Michael Affenzeller, Andreas Beham, Stefan Wagner
Date: 2013-09-23 00:00:00
Tasks: Heuristic Search, Time Series, Time Series Analysis, Time Series Forecasting

Genetic programming is a powerful heuristic search technique that is used for
a number of real world applications to solve among others regression,
classification, and time-series forecasting problems. A lot of progress towards
a theoretic description of genetic programming in form of schema theorems has
been made, but the internal dynamics and success factors of genetic programming
are still not fully understood. In particular, the effects of different
crossover operators in combination with offspring selection are largely
unknown.
  This contribution sheds light on the ability of well-known GP crossover
operators to create better offspring when applied to benchmark problems. We
conclude that standard (sub-tree swapping) crossover is a good default choice
in combination with offspring selection, and that GP with offspring selection
and random selection of crossover operators can improve the performance of the
algorithm in terms of best solution quality when no solution size constraints
are applied.
Smooth minimization of nonsmooth functions with parallel coordinate descent methods
Authors: Olivier Fercoq, Peter Richtárik
Date: 2013-09-23 00:00:00

We study the performance of a family of randomized parallel coordinate
descent methods for minimizing the sum of a nonsmooth and separable convex
functions. The problem class includes as a special case L1-regularized L1
regression and the minimization of the exponential loss ("AdaBoost problem").
We assume the input data defining the loss function is contained in a sparse
$m\times n$ matrix $A$ with at most $\omega$ nonzeros in each row. Our methods
need $O(n \beta/\tau)$ iterations to find an approximate solution with high
probability, where $\tau$ is the number of processors and $\beta = 1 +
(\omega-1)(\tau-1)/(n-1)$ for the fastest variant. The notation hides
dependence on quantities such as the required accuracy and confidence levels
and the distance of the starting iterate from an optimal point. Since
$\beta/\tau$ is a decreasing function of $\tau$, the method needs fewer
iterations when more processors are used. Certain variants of our algorithms
perform on average only $O(\nnz(A)/n)$ arithmetic operations during a single
iteration per processor and, because $\beta$ decreases when $\omega$ does,
fewer iterations are needed for sparser problems.
Scalable Spectral Algorithms for Community Detection in Directed Networks
Authors: Sungmin Kim, Tao Shi
Date: 2012-11-29 00:00:00
Tasks: Community Detection

Community detection has been one of the central problems in network studies
and directed network is particularly challenging due to asymmetry among its
links. In this paper, we found that incorporating the direction of links
reveals new perspectives on communities regarding to two different roles,
source and terminal, that a node plays in each community. Intriguingly, such
communities appear to be connected with unique spectral property of the graph
Laplacian of the adjacency matrix and we exploit this connection by using
regularized SVD methods. We propose harvesting algorithms, coupled with
regularized SVDs, that are linearly scalable for efficient identification of
communities in huge directed networks. The proposed algorithm shows great
performance and scalability on benchmark networks in simulations and
successfully recovers communities in real network applications.
Macro-Economic Time Series Modeling and Interaction Networks
Authors: Gabriel Kronberger, Stefan Fink, Michael Kommenda, Michael Affenzeller
Date: 2012-12-10 00:00:00
Tasks: regression, Symbolic Regression, Time Series, Time Series Analysis

Macro-economic models describe the dynamics of economic quantities. The
estimations and forecasts produced by such models play a substantial role for
financial and political decisions. In this contribution we describe an approach
based on genetic programming and symbolic regression to identify variable
interactions in large datasets. In the proposed approach multiple symbolic
regression runs are executed for each variable of the dataset to find
potentially interesting models. The result is a variable interaction network
that describes which variables are most relevant for the approximation of each
variable of the dataset. This approach is applied to a macro-economic dataset
with monthly observations of important economic indicators in order to identify
potentially interesting dependencies of these indicators. The resulting
interaction network of macro-economic indicators is briefly discussed and two
of the identified models are presented in detail. The two models approximate
the help wanted index and the CPI inflation in the US.
Sentiment Analysis: How to Derive Prior Polarities from SentiWordNet
Authors: Marco Guerini, Lorenzo Gatti, Marco Turchi
Date: 2013-09-23 00:00:00
Proceeding: EMNLP 2013 10
Tasks: General Classification, Sentiment Analysis

Assigning a positive or negative score to a word out of context (i.e. a
word's prior polarity) is a challenging task for sentiment analysis. In the
literature, various approaches based on SentiWordNet have been proposed. In
this paper, we compare the most often used techniques together with newly
proposed ones and incorporate all of them in a learning framework to see
whether blending them can further improve the estimation of prior polarity
scores. Using two different versions of SentiWordNet and testing regression and
classification models across tasks and datasets, our learning approach
consistently outperforms the single metrics, providing a new state-of-the-art
approach in computing words' prior polarity for sentiment analysis. We conclude
our investigation showing interesting biases in calculated prior polarity
scores when word Part of Speech and annotator gender are considered.
A Kernel Classification Framework for Metric Learning
Authors: Faqiang Wang, WangMeng Zuo, Lei Zhang, Deyu Meng, David Zhang
Date: 2013-09-23 00:00:00
Tasks: Classification, General Classification, Metric Learning, Triplet

Learning a distance metric from the given training samples plays a crucial
role in many machine learning tasks, and various models and optimization
algorithms have been proposed in the past decade. In this paper, we generalize
several state-of-the-art metric learning methods, such as large margin nearest
neighbor (LMNN) and information theoretic metric learning (ITML), into a kernel
classification framework. First, doublets and triplets are constructed from the
training samples, and a family of degree-2 polynomial kernel functions are
proposed for pairs of doublets or triplets. Then, a kernel classification
framework is established, which can not only generalize many popular metric
learning methods such as LMNN and ITML, but also suggest new metric learning
methods, which can be efficiently implemented, interestingly, by using the
standard support vector machine (SVM) solvers. Two novel metric learning
methods, namely doublet-SVM and triplet-SVM, are then developed under the
proposed framework. Experimental results show that doublet-SVM and triplet-SVM
achieve competitive classification accuracies with state-of-the-art metric
learning methods such as ITML and LMNN but with significantly less training
time.
Feature Learning with Gaussian Restricted Boltzmann Machine for Robust Speech Recognition
Authors: Xin Zheng, Zhiyong Wu, Helen Meng, Weifeng Li, Lianhong Cai
Date: 2013-09-23 00:00:00
Tasks: Robust Speech Recognition, speech-recognition, Speech Recognition

In this paper, we first present a new variant of Gaussian restricted
Boltzmann machine (GRBM) called multivariate Gaussian restricted Boltzmann
machine (MGRBM), with its definition and learning algorithm. Then we propose
using a learned GRBM or MGRBM to extract better features for robust speech
recognition. Our experiments on Aurora2 show that both GRBM-extracted and
MGRBM-extracted feature performs much better than Mel-frequency cepstral
coefficient (MFCC) with either HMM-GMM or hybrid HMM-deep neural network (DNN)
acoustic model, and MGRBM-extracted feature is slightly better.
Measure Transformer Semantics for Bayesian Machine Learning
Authors: Johannes Borgström, Andrew D. Gordon, Michael Greenberg, James Margetson, Jurgen Van Gael
Date: 2013-08-03 00:00:00
Tasks: BIG-bench Machine Learning

The Bayesian approach to machine learning amounts to computing posterior
distributions of random variables from a probabilistic model of how the
variables are related (that is, a prior distribution) and a set of observations
of variables. There is a trend in machine learning towards expressing Bayesian
models as probabilistic programs. As a foundation for this kind of programming,
we propose a core functional calculus with primitives for sampling prior
distributions and observing variables. We define measure-transformer
combinators inspired by theorems in measure theory, and use these to give a
rigorous semantics to our core calculus. The original features of our semantics
include its support for discrete, continuous, and hybrid measures, and, in
particular, for observations of zero-probability events. We compile our core
language to a small imperative language that is processed by an existing
inference engine for factor graphs, which are data structures that enable many
efficient inference algorithms. This allows efficient approximate inference of
posterior marginal distributions, treating thousands of observations per second
for large instances of realistic models.
Spike Synchronization Dynamics of Small-World Networks
Authors: Derek Harter
Date: 2013-09-22 00:00:00
Tasks: Clustering

In this research report, we examine the effects of small-world network
organization on spike synchronization dynamics in networks of Izhikevich
spiking units. We interpolate network organizations from regular ring lattices,
through the small-world region, to random networks, and measure global spike
synchronization dynamics. We examine how average path length and clustering
effect the dynamics of global and neighborhood clique spike organization and
propagation. We show that the emergence of global synchronization undergoes a
phase transition in the small-world region, between the clustering and path
length phase transitions that are known to exist. We add additional realistic
constraints on the dynamics by introducing propagation delays of spiking
signals proportional to wiring length. The addition of delays interferes with
the ability of random networks to sustain global synchronization, in relation
to the breakdown of clustering in the networks. The addition of delays further
enhances the finding that small-world organization is beneficial for balancing
neighborhood synchronized waves of organization with global synchronization
dynamics.
A Hybrid Algorithm for Matching Arabic Names
Authors: T. El-Shishtawy
Date: 2013-09-22 00:00:00

In this paper, a new hybrid algorithm which combines both of token-based and
character-based approaches is presented. The basic Levenshtein approach has
been extended to token-based distance metric. The distance metric is enhanced
to set the proper granularity level behavior of the algorithm. It smoothly maps
a threshold of misspellings differences at the character level, and the
importance of token level errors in terms of token's position and frequency.
Using a large Arabic dataset, the experimental results show that the proposed
algorithm overcomes successfully many types of errors such as: typographical
errors, omission or insertion of middle name components, omission of
non-significant popular name components, and different writing styles character
variations. When compared the results with other classical algorithms, using
the same dataset, the proposed algorithm was found to increase the minimum
success level of best tested algorithms, while achieving higher upper limits .
LDC Arabic Treebanks and Associated Corpora: Data Divisions Manual
Authors: Mona Diab, Nizar Habash, Owen Rambow, Ryan Roth
Date: 2013-09-22 00:00:00

The Linguistic Data Consortium (LDC) has developed hundreds of data corpora
for natural language processing (NLP) research. Among these are a number of
annotated treebank corpora for Arabic. Typically, these corpora consist of a
single collection of annotated documents. NLP research, however, usually
requires multiple data sets for the purposes of training models, developing
techniques, and final evaluation. Therefore it becomes necessary to divide the
corpora used into the required data sets (divisions). This document details a
set of rules that have been defined to enable consistent divisions for old and
new Arabic treebanks (ATB) and related corpora.
Stochastic Bound Majorization
Authors: Anna Choromanska, Tony Jebara
Date: 2013-09-22 00:00:00
Tasks: Stochastic Optimization

Recently a majorization method for optimizing partition functions of
log-linear models was proposed alongside a novel quadratic variational
upper-bound. In the batch setting, it outperformed state-of-the-art first- and
second-order optimization methods on various learning tasks. We propose a
stochastic version of this bound majorization method as well as a low-rank
modification for high-dimensional data-sets. The resulting stochastic
second-order method outperforms stochastic gradient descent (across variations
and various tunings) both in terms of the number of iterations and computation
time till convergence while finding a better quality parameter setting. The
proposed method bridges first- and second-order stochastic optimization methods
by maintaining a computational complexity that is linear in the data dimension
and while exploiting second order information about the pseudo-global curvature
of the objective function (as opposed to the local curvature in the Hessian).
A Meta-heuristically Approach of the Spatial Assignment Problem of Human Resources in Multi-sites Enterprise
Authors: Tkatek Said, Abdoun Otman, Abouchabaka Jaafar, Rafalia Najat
Date: 2013-09-22 00:00:00
Tasks: Combinatorial Optimization

The aim of this work is to present a meta-heuristically approach of the
spatial assignment problem of human resources in multi-sites enterprise.
Usually, this problem consists to move employees from one site to another based
on one or more criteria. Our goal in this new approach is to improve the
quality of service and performance of all sites with maximizing an objective
function under some managers imposed constraints. The formulation presented
here of this problem coincides perfectly with a Combinatorial Optimization
Problem (COP) which is in the most cases NP-hard to solve optimally. To avoid
this difficulty, we have opted to use a meta-heuristic popular method, which is
the genetic algorithm, to solve this problem in concrete cases. The results
obtained have shown the effectiveness of our approach, which remains until now
very costly in time. But the reduction of the time can be obtained by different
ways that we plan to do in the next work.
Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming
Authors: Saeed Ghadimi, Guanghui Lan
Date: 2013-09-22 00:00:00

In this paper, we introduce a new stochastic approximation (SA) type
algorithm, namely the randomized stochastic gradient (RSG) method, for solving
an important class of nonlinear (possibly nonconvex) stochastic programming
(SP) problems. We establish the complexity of this method for computing an
approximate stationary point of a nonlinear programming problem. We also show
that this method possesses a nearly optimal rate of convergence if the problem
is convex. We discuss a variant of the algorithm which consists of applying a
post-optimization phase to evaluate a short list of solutions generated by
several independent runs of the RSG method, and show that such modification
allows to improve significantly the large-deviation properties of the
algorithm. These methods are then specialized for solving a class of
simulation-based optimization problems in which only stochastic zeroth-order
information is available.
Latent Fisher Discriminant Analysis
Authors: Gang Chen
Date: 2013-09-21 00:00:00
Tasks: Dimensionality Reduction, General Classification, object-detection, Object Detection

Linear Discriminant Analysis (LDA) is a well-known method for dimensionality
reduction and classification. Previous studies have also extended the
binary-class case into multi-classes. However, many applications, such as
object detection and keyframe extraction cannot provide consistent
instance-label pairs, while LDA requires labels on instance level for training.
Thus it cannot be directly applied for semi-supervised classification problem.
In this paper, we overcome this limitation and propose a latent variable Fisher
discriminant analysis model. We relax the instance-level labeling into
bag-level, is a kind of semi-supervised (video-level labels of event type are
required for semantic frame extraction) and incorporates a data-driven prior
over the latent variables. Hence, our method combines the latent variable
inference and dimension reduction in an unified bayesian framework. We test our
method on MUSK and Corel data sets and yield competitive results compared to
the baseline approach. We also demonstrate its capacity on the challenging
TRECVID MED11 dataset for semantic keyframe extraction and conduct a
human-factors ranking-based experimental evaluation, which clearly demonstrates
our proposed method consistently extracts more semantically meaningful
keyframes than challenging baselines.
Scan-based Compressed Terahertz Imaging and Real-Time Reconstruction via the Complex-valued Fast Block Sparse Bayesian Learning Algorithm
Authors: Benyuan Liu, Hongqi Fan, Zaiqi Lu, Qiang Fu
Date: 2013-09-20 00:00:00
Tasks: compressed sensing

Compressed Sensing based Terahertz imaging (CS-THz) is a computational
imaging technique. It uses only one THz receiver to accumulate the random
modulated image measurements where the original THz image is reconstruct from
these measurements using compressed sensing solvers. The advantage of the
CS-THz is its reduced acquisition time compared with the raster scan mode.
However, when it applied to large-scale two-dimensional (2D) imaging, the
increased dimension resulted in both high computational complexity and
excessive memory usage. In this paper, we introduced a novel CS-based THz
imaging system that progressively compressed the THz image column by column.
Therefore, the CS-THz system could be simplified with a much smaller sized
modulator and reduced dimension. In order to utilize the block structure and
the correlation of adjacent columns of the THz image, a complex-valued block
sparse Bayesian learning algorithm was proposed. We conducted systematic
evaluation of state-of-the-art CS algorithms under the scan based CS-THz
architecture. The compression ratios and the choices of the sensing matrices
were analyzed in detail using both synthetic and real-life THz images.
Simulation results showed that both the scan based architecture and the
proposed recovery algorithm were superior and efficient for large scale CS-THz
applications.
Nonmyopic View Planning for Active Object Detection
Authors: Nikolay Atanasov, Bharath Sankaran, Jerome Le Ny, George J. Pappas, Kostas Daniilidis
Date: 2013-09-20 00:00:00
Tasks: Active Object Detection, Object, object-detection, Object Detection, Two-sample testing

One of the central problems in computer vision is the detection of
semantically important objects and the estimation of their pose. Most of the
work in object detection has been based on single image processing and its
performance is limited by occlusions and ambiguity in appearance and geometry.
This paper proposes an active approach to object detection by controlling the
point of view of a mobile depth camera. When an initial static detection phase
identifies an object of interest, several hypotheses are made about its class
and orientation. The sensor then plans a sequence of views, which balances the
amount of energy used to move with the chance of identifying the correct
hypothesis. We formulate an active hypothesis testing problem, which includes
sensor mobility, and solve it using a point-based approximate POMDP algorithm.
The validity of our approach is verified through simulation and real-world
experiments with the PR2 robot. The results suggest that our approach
outperforms the widely-used greedy view point selection and provides a
significant improvement over static object detection.
Colourful Language: Measuring Word-Colour Associations
Authors: Saif Mohammad
Date: 2013-09-20 00:00:00
Tasks: Marketing

Since many real-world concepts are associated with colour, for example danger
with red, linguistic information is often complimented with the use of
appropriate colours in information visualization and product marketing. Yet,
there is no comprehensive resource that captures concept-colour associations.
We present a method to create a large word-colour association lexicon by
crowdsourcing. We focus especially on abstract concepts and emotions to show
that even though they cannot be physically visualized, they too tend to have
strong colour associations. Finally, we show how word-colour associations
manifest themselves in language, and quantify usefulness of co-occurrence and
polarity cues in automatically detecting colour associations.