pwc-archive/papers-with-abstracts huggingface dataset:
A Mixtures-of-Experts Framework for Multi-Label Classification
Authors: Charmgil Hong, Iyad Batal, Milos Hauskrecht
Date: 2014-09-16 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

We develop a novel probabilistic approach for multi-label classification that
is based on the mixtures-of-experts architecture combined with recently
introduced conditional tree-structured Bayesian networks. Our approach captures
different input-output relations from multi-label data using the efficient
tree-structured classifiers, while the mixtures-of-experts architecture aims to
compensate for the tree-structured restrictions and build a more accurate
model. We develop and present algorithms for learning the model from data and
for performing multi-label predictions on future data instances. Experiments on
multiple benchmark datasets demonstrate that our approach achieves highly
competitive results and outperforms the existing state-of-the-art multi-label
classification methods.
DISA at ImageCLEF 2014 Revised: Search-based Image Annotation with DeCAF Features
Authors: Petra Budikova, Jan Botorek, Michal Batko, Pavel Zezula
Date: 2014-09-16 00:00:00

This paper constitutes an extension to the report on DISA-MU team
participation in the ImageCLEF 2014 Scalable Concept Image Annotation Task as
published in [3]. Specifically, we introduce a new similarity search component
that was implemented into the system, report on the results achieved by
utilizing this component, and analyze the influence of different similarity
search parameters on the annotation quality.
Predictive Capacity of Meteorological Data - Will it rain tomorrow
Authors: Bilal Ahmed
Date: 2014-09-16 00:00:00
Tasks: BIG-bench Machine Learning

With the availability of high precision digital sensors and cheap storage
medium, it is not uncommon to find large amounts of data collected on almost
all measurable attributes, both in nature and man-made habitats. Weather in
particular has been an area of keen interest for researchers to develop more
accurate and reliable prediction models. This paper presents a set of
experiments which involve the use of prevalent machine learning techniques to
build models to predict the day of the week given the weather data for that
particular day i.e. temperature, wind, rain etc., and test their reliability
across four cities in Australia {Brisbane, Adelaide, Perth, Hobart}. The
results provide a comparison of accuracy of these machine learning techniques
and their reliability to predict the day of the week by analysing the weather
data. We then apply the models to predict weather conditions based on the
available data.
The Role of Emotions in Propagating Brands in Social Networks
Authors: Ronald Hochreiter, Christoph Waldhauser
Date: 2014-09-16 00:00:00
Tasks: Marketing

A key aspect of word of mouth marketing are emotions. Emotions in texts help
propagating messages in conventional advertising. In word of mouth scenarios,
emotions help to engage consumers and incite to propagate the message further.
While the function of emotions in offline marketing in general and word of
mouth marketing in particular is rather well understood, online marketing can
only offer a limited view on the function of emotions. In this contribution we
seek to close this gap. We therefore investigate how emotions function in
social media. To do so, we collected more than 30,000 brand marketing messages
from the Google+ social networking site. Using state of the art computational
linguistics classifiers, we compute the sentiment of these messages. Starting
out with Poisson regression-based baseline models, we seek to replicate earlier
findings using this large data set. We extend upon earlier research by
computing multi-level mixed effects models that compare the function of
emotions across different industries. We find that while the well known notion
of activating emotions propagating messages holds in general for our data as
well. But there are significant differences between the observed industries.
Multivariate Comparison of Classification Algorithms
Authors: Olcay Taner Yildiz, Ethem Alpaydin
Date: 2014-09-16 00:00:00
Tasks: Classification, General Classification

Statistical tests that compare classification algorithms are univariate and
use a single performance measure, e.g., misclassification error, $F$ measure,
AUC, and so on. In multivariate tests, comparison is done using multiple
measures simultaneously. For example, error is the sum of false positives and
false negatives and a univariate test on error cannot make a distinction
between these two sources, but a 2-variate test can. Similarly, instead of
combining precision and recall in $F$ measure, we can have a 2-variate test on
(precision, recall). We use Hotelling's multivariate $T^2$ test for comparing
two algorithms, and when we have three or more algorithms we use the
multivariate analysis of variance (MANOVA) followed by pairwise post hoc tests.
In our experiments, we see that multivariate tests have higher power than
univariate tests, that is, they can detect differences that univariate tests
cannot. We also discuss how multivariate analysis allows us to automatically
extract performance measures that best distinguish the behavior of multiple
algorithms.
Improving files availability for BitTorrent using a diffusion model
Authors: Christian Napoli, Giuseppe Pappalardo, Emiliano Tramontana
Date: 2014-09-16 00:00:00

The BitTorrent mechanism effectively spreads file fragments by copying the
rarest fragments first. We propose to apply a mathematical model for the
diffusion of fragments on a P2P in order to take into account both the effects
of peer distances and the changing availability of peers while time goes on.
Moreover, we manage to provide a forecast on the availability of a torrent
thanks to a neural network that models the behaviour of peers on the P2P
system. The combination of the mathematical model and the neural network
provides a solution for choosing file fragments that need to be copied first,
in order to ensure their continuous availability, counteracting possible
disconnections by some peers.
A Combined Method Of Fractal And GLCM Features For MRI And CT Scan Images Classification
Authors: Redouan Korchiyne, Sidi Mohamed Farssi, Abderrahmane Sbihi, Rajaa Touahni, Mustapha Tahiri Alaoui
Date: 2014-09-16 00:00:00
Tasks: General Classification

Fractal analysis has been shown to be useful in image processing for
characterizing shape and gray-scale complexity. The fractal feature is a
compact descriptor used to give a numerical measure of the degree of
irregularity of the medical images. This descriptor property does not give
ownership of the local image structure. In this paper, we present a combination
of this parameter based on Box Counting with GLCM Features. This powerful
combination has proved good results especially in classification of medical
texture from MRI and CT Scan images of trabecular bone. This method has the
potential to improve clinical diagnostics tests for osteoporosis pathologies.
Semidefinite Programming Based Preconditioning for More Robust Near-Separable Nonnegative Matrix Factorization
Authors: Nicolas Gillis, Stephen A. Vavasis
Date: 2013-10-08 00:00:00
Tasks: Document Classification, Hyperspectral Unmixing, Single Particle Analysis

Nonnegative matrix factorization (NMF) under the separability assumption can
provably be solved efficiently, even in the presence of noise, and has been
shown to be a powerful technique in document classification and hyperspectral
unmixing. This problem is referred to as near-separable NMF and requires that
there exists a cone spanned by a small subset of the columns of the input
nonnegative matrix approximately containing all columns. In this paper, we
propose a preconditioning based on semidefinite programming making the input
matrix well-conditioned. This in turn can improve significantly the performance
of near-separable NMF algorithms which is illustrated on the popular successive
projection algorithm (SPA). The new preconditioned SPA is provably more robust
to noise, and outperforms SPA on several synthetic data sets. We also show how
an active-set method allow us to apply the preconditioning on large-scale
real-world hyperspectral images.
Collapsed Variational Bayes Inference of Infinite Relational Model
Authors: Katsuhiko Ishiguro, Issei Sato, Naonori Ueda
Date: 2014-09-16 00:00:00
Tasks: Clustering, model

The Infinite Relational Model (IRM) is a probabilistic model for relational
data clustering that partitions objects into clusters based on observed
relationships. This paper presents Averaged CVB (ACVB) solutions for IRM,
convergence-guaranteed and practically useful fast Collapsed Variational Bayes
(CVB) inferences. We first derive ordinary CVB and CVB0 for IRM based on the
lower bound maximization. CVB solutions yield deterministic iterative
procedures for inferring IRM given the truncated number of clusters. Our
proposal includes CVB0 updates of hyperparameters including the concentration
parameter of the Dirichlet Process, which has not been studied in the
literature. To make the CVB more practically useful, we further study the CVB
inference in two aspects. First, we study the convergence issues and develop a
convergence-guaranteed algorithm for any CVB-based inferences called ACVB,
which enables automatic convergence detection and frees non-expert
practitioners from difficult and costly manual monitoring of inference
processes. Second, we present a few techniques for speeding up IRM inferences.
In particular, we describe the linear time inference of CVB0, allowing the IRM
for larger relational data uses. The ACVB solutions of IRM showed comparable or
better performance compared to existing inference methods in experiments, and
provide deterministic, faster, and easier convergence detection.
A Robust and Efficient Method for Improving Accuracy of License Plate Characters Recognition
Authors: Reza Azad, Hamid Reza Shayegh, Hamed Amiri
Date: 2014-07-24 00:00:00
Tasks: License Plate Recognition, Management

License Plate Recognition (LPR) plays an important role on the traffic
monitoring and parking management. A robust and efficient method for enhancing
accuracy of license plate characters recognition based on K Nearest Neighbours
(K-NN) classifier is presented in this paper. The system first prepares a
contour form of the extracted character, then the angle and distance feature
information about the character is extracted and finally K-NN classifier is
used to character recognition. Angle and distance features of a character have
been computed based on distribution of points on the bitmap image of character.
In K-NN method, the Euclidean distance between testing point and reference
points is calculated in order to find the k-nearest neighbours. We evaluated
our method on the available dataset that contain 1200 sample. Using 70% samples
for training, we tested our method on whole samples and obtained 99% correct
recognition rate.Further, we achieved average 99.41% accuracy using
three/strategy validation technique on 1200 dataset.
Voting for Deceptive Opinion Spam Detection
Authors: Tao Wang, Hua Zhu
Date: 2014-09-16 00:00:00
Tasks: Deception Detection, General Classification, Spam detection

Consumers' purchase decisions are increasingly influenced by user-generated
online reviews. Accordingly, there has been growing concern about the potential
for posting deceptive opinion spam fictitious reviews that have been
deliberately written to sound authentic, to deceive the readers. Existing
approaches mainly focus on developing automatic supervised learning based
methods to help users identify deceptive opinion spams.
  This work, we used the LSI and Sprinkled LSI technique to reduce the
dimension for deception detection. We make our contribution to demonstrate what
LSI is capturing in latent semantic space and reveal how deceptive opinions can
be recognized automatically from truthful opinions. Finally, we proposed a
voting scheme which integrates different approaches to further improve the
classification performance.
Real-time Crowd Tracking using Parameter Optimized Mixture of Motion Models
Authors: Aniket Bera, David Wolinski, Julien Pettré, Dinesh Manocha
Date: 2014-09-16 00:00:00
Tasks: Combinatorial Optimization, CPU, parameter estimation

We present a novel, real-time algorithm to track the trajectory of each
pedestrian in moderately dense crowded scenes. Our formulation is based on an
adaptive particle-filtering scheme that uses a combination of various
multi-agent heterogeneous pedestrian simulation models. We automatically
compute the optimal parameters for each of these different models based on
prior tracked data and use the best model as motion prior for our
particle-filter based tracking algorithm. We also use our "mixture of motion
models" for adaptive particle selection and accelerate the performance of the
online tracking algorithm. The motion model parameter estimation is formulated
as an optimization problem, and we use an approach that solves this
combinatorial optimization problem in a model independent manner and hence
scalable to any multi-agent pedestrian motion model. We evaluate the
performance of our approach on different crowd video datasets and highlight the
improvement in accuracy over homogeneous motion models and a baseline
mean-shift based tracker. In practice, our formulation can compute trajectories
of tens of pedestrians on a multi-core desktop CPU in in real time and offer
higher accuracy as compared to prior real time pedestrian tracking algorithms.
Convolutional Networks for Image Processing by Coupled Oscillator Arrays
Authors: Dmitri E. Nikonov, Ian A. Young, George I. Bourianoff
Date: 2014-09-15 00:00:00

A coupled oscillator array is shown to approximate convolutions with Gabor
filters for image processing tasks. Pixelated image fragments and filter
functions are converted to voltages, differenced, and input into a
corresponding array of weakly coupled Voltage Controlled Oscillators (VCOs).
This is referred to as Frequency Shift Keying (FSK). Upon synchronization of
the array, the common node amplitude provides a metric for the degree of match
between the image fragment and the filter function. The optimal oscillator
parameters for synchronization are determined and favor a moderate value of the
Q-factor.
The Randomized Causation Coefficient
Authors: David Lopez-Paz, Krikamol Muandet, Benjamin Recht
Date: 2014-09-15 00:00:00
Tasks: Causal Inference, Feature Engineering

We are interested in learning causal relationships between pairs of random
variables, purely from observational data. To effectively address this task,
the state-of-the-art relies on strong assumptions regarding the mechanisms
mapping causes to effects, such as invertibility or the existence of additive
noise, which only hold in limited situations. On the contrary, this short paper
proposes to learn how to perform causal inference directly from data, and
without the need of feature engineering. In particular, we pose causality as a
kernel mean embedding classification problem, where inputs are samples from
arbitrary probability distributions on pairs of random variables, and labels
are types of causal relationships. We validate the performance of our method on
synthetic and real-world data against the state-of-the-art. Moreover, we
submitted our algorithm to the ChaLearn's "Fast Causation Coefficient
Challenge" competition, with which we won the fastest code prize and ranked
third in the overall leaderboard.
Computational Algorithms Based on the Paninian System to Process Euphonic Conjunctions for Word Searches
Authors: S. V. Kasmir Raja, V. Rajitha, Meenakshi Lakshmanan
Date: 2014-09-15 00:00:00

Searching for words in Sanskrit E-text is a problem that is accompanied by
complexities introduced by features of Sanskrit such as euphonic conjunctions
or sandhis. A word could occur in an E-text in a transformed form owing to the
operation of rules of sandhi. Simple word search would not yield these
transformed forms of the word. Further, there is no search engine in the
literature that can comprehensively search for words in Sanskrit E-texts taking
euphonic conjunctions into account. This work presents an optimal binary
representational schema for letters of the Sanskrit alphabet along with
algorithms to efficiently process the sandhi rules of Sanskrit grammar. The
work further presents an algorithm that uses the sandhi processing algorithm to
perform a comprehensive word search on E-text.
A Binary Schema and Computational Algorithms to Process Vowel-based Euphonic Conjunctions for Word Searches
Authors: S. V. Kasmir Raja, V. Rajitha, Meenakshi Lakshmanan
Date: 2014-09-15 00:00:00

Comprehensively searching for words in Sanskrit E-text is a non-trivial
problem because words could change their forms in different contexts. One such
context is sandhi or euphonic conjunctions, which cause a word to change owing
to the presence of adjacent letters or words. The change wrought by these
possible conjunctions can be so significant in Sanskrit that a simple search
for the word in its given form alone can significantly reduce the success level
of the search. This work presents a representational schema that represents
letters in a binary format and reduces Paninian rules of euphonic conjunctions
to simple bit set-unset operations. The work presents an efficient algorithm to
process vowel-based sandhis using this schema. It further presents another
algorithm that uses the sandhi processor to generate the possible transformed
word forms of a given word to use in a comprehensive word search.
On the optimality of shape and data representation in the spectral domain
Authors: Yonathan Aflalo, Haim Brezis, Ron Kimmel
Date: 2014-09-15 00:00:00

A proof of the optimality of the eigenfunctions of the Laplace-Beltrami
operator (LBO) in representing smooth functions on surfaces is provided and
adapted to the field of applied shape and data analysis. It is based on the
Courant-Fischer min-max principle adapted to our case. % The theorem we present
supports the new trend in geometry processing of treating geometric structures
by using their projection onto the leading eigenfunctions of the decomposition
of the LBO. Utilisation of this result can be used for constructing numerically
efficient algorithms to process shapes in their spectrum. We review a couple of
applications as possible practical usage cases of the proposed optimality
criteria. % We refer to a scale invariant metric, which is also invariant to
bending of the manifold. This novel pseudo-metric allows constructing an LBO by
which a scale invariant eigenspace on the surface is defined. We demonstrate
the efficiency of an intermediate metric, defined as an interpolation between
the scale invariant and the regular one, in representing geometric structures
while capturing both coarse and fine details. Next, we review a numerical
acceleration technique for classical scaling, a member of a family of
flattening methods known as multidimensional scaling (MDS). There, the
optimality is exploited to efficiently approximate all geodesic distances
between pairs of points on a given surface, and thereby match and compare
between almost isometric surfaces. Finally, we revisit the classical principal
component analysis (PCA) definition by coupling its variational form with a
Dirichlet energy on the data manifold. By pairing the PCA with the LBO we can
handle cases that go beyond the scope defined by the observation set that is
handled by regular PCA.
Novel and Fast Algorithm for Extracting License Plate Location Based on Edge Analysis
Authors: Reza Azad, Mohammad Baghdadi
Date: 2014-07-24 00:00:00
Tasks: Edge Detection, License Plate Recognition

Nowadays in developing or developed countries, the Intelligent Transportation
System (ITS) technology has attracted so much attention to itself. License
Plate Recognition (LPR) systems have many applications in ITSs, such as the
payment of parking fee, controlling the traffic volume, traffic data
collection, etc. This paper presents a new and fast method for license plate
extraction based on edge analysis. our proposed method consist of four stage,
which are edge detection, non-useable edge and noise removing, edge analysis
and morphology-based license plate extraction. In the result part, the proposed
algorithm is applied on vehicle database and the accuracy rate reached 98%.
From the experimental results it is shown that the proposed method gives fairly
acceptable level of accuracy for practical license plate recognition system.
Anytime Computation of Cautious Consequences in Answer Set Programming
Authors: Mario Alviano, Carmine Dodaro, Francesco Ricca
Date: 2014-05-14 00:00:00

Query answering in Answer Set Programming (ASP) is usually solved by
computing (a subset of) the cautious consequences of a logic program. This task
is computationally very hard, and there are programs for which computing
cautious consequences is not viable in reasonable time. However, current ASP
solvers produce the (whole) set of cautious consequences only at the end of
their computation. This paper reports on strategies for computing cautious
consequences, also introducing anytime algorithms able to produce sound answers
during the computation.
Speeding-up Graphical Model Optimization via a Coarse-to-fine Cascade of Pruning Classifiers
Authors: B. Conejo, N. Komodakis, S. Leprince, J. P. Avouac
Date: 2014-09-15 00:00:00
Tasks: Model Optimization

We propose a general and versatile framework that significantly speeds-up
graphical model optimization while maintaining an excellent solution accuracy.
The proposed approach relies on a multi-scale pruning scheme that is able to
progressively reduce the solution space by use of a novel strategy based on a
coarse-to-fine cascade of learnt classifiers. We thoroughly experiment with
classic computer vision related MRF problems, where our framework constantly
yields a significant time speed-up (with respect to the most efficient
inference methods) and obtains a more accurate solution than directly
optimizing the MRF.
Selecting Near-Optimal Approximate State Representations in Reinforcement Learning
Authors: Ronald Ortner, Odalric-Ambrym Maillard, Daniil Ryabko
Date: 2014-05-12 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We consider a reinforcement learning setting introduced in (Maillard et al.,
NIPS 2011) where the learner does not have explicit access to the states of the
underlying Markov decision process (MDP). Instead, she has access to several
models that map histories of past interactions to states. Here we improve over
known regret bounds in this setting, and more importantly generalize to the
case where the models given to the learner do not contain a true model
resulting in an MDP representation but only approximations of it. We also give
improved error bounds for state aggregation.
An Algorithm Based on Empirical Methods, for Text-to-Tuneful-Speech Synthesis of Sanskrit Verse
Authors: Rama N., Meenakshi Lakshmanan
Date: 2014-09-15 00:00:00
Tasks: Speech Synthesis, text-to-speech, Text to Speech

The rendering of Sanskrit poetry from text to speech is a problem that has
not been solved before. One reason may be the complications in the language
itself. We present unique algorithms based on extensive empirical analysis, to
synthesize speech from a given text input of Sanskrit verses. Using a
pre-recorded audio units database which is itself tremendously reduced in size
compared to the colossal size that would otherwise be required, the algorithms
work on producing the best possible, tunefully rendered chanting of the given
verse. His would enable the visually impaired and those with reading
disabilities to easily access the contents of Sanskrit verses otherwise
available only in writing.