pwc-archive/papers-with-abstracts huggingface dataset:
Dynamic Hierarchical Bayesian Network for Arabic Handwritten Word Recognition
Authors: Khaoula jayech, Nesrine Trimech, Mohamed Ali Mahjoub, Najoua Essoukri Ben Amara
Date: 2014-05-20 00:00:00
Tasks: Segmentation, Translation

This paper presents a new probabilistic graphical model used to model and
recognize words representing the names of Tunisian cities. In fact, this work
is based on a dynamic hierarchical Bayesian network. The aim is to find the
best model of Arabic handwriting to reduce the complexity of the recognition
process by permitting the partial recognition. Actually, we propose a
segmentation of the word based on smoothing the vertical histogram projection
using different width values to reduce the error of segmentation. Then, we
extract the characteristics of each cell using the Zernike and HU moments,
which are invariant to rotation, translation and scaling. Our approach is
tested using the IFN / ENIT database, and the experiment results are very
promising.
Sequential Advantage Selection for Optimal Treatment Regimes
Authors: Ailin Fan, Wenbin Lu, Rui Song
Date: 2014-05-20 00:00:00
Tasks: Decision Making, Variable Selection

Variable selection for optimal treatment regime in a clinical trial or an
observational study is getting more attention. Most existing variable selection
techniques focused on selecting variables that are important for prediction,
therefore some variables that are poor in prediction but are critical for
decision-making may be ignored. A qualitative interaction of a variable with
treatment arises when treatment effect changes direction as the value of this
variable varies. The qualitative interaction indicates the importance of this
variable for decision-making. Gunter et al. (2011) proposed S-score which
characterizes the magnitude of qualitative interaction of each variable with
treatment individually. In this article, we developed a sequential advantage
selection method based on the modified S-score. Our method selects
qualitatively interacted variables sequentially, and hence excludes marginally
important but jointly unimportant variables {or vice versa}. The optimal
treatment regime based on variables selected via joint model is more
comprehensive and reliable. With the proposed stopping criteria, our method can
handle a large amount of covariates even if sample size is small. Simulation
results show our method performs well in practical settings. We further applied
our method to data from a clinical trial for depression.
Scalable Recommendation with Poisson Factorization
Authors: Prem Gopalan, Jake M. Hofman, David M. Blei
Date: 2013-11-07 00:00:00
Tasks: Variational Inference

We develop a Bayesian Poisson matrix factorization model for forming
recommendations from sparse user behavior data. These data are large user/item
matrices where each user has provided feedback on only a small subset of items,
either explicitly (e.g., through star ratings) or implicitly (e.g., through
views or purchases). In contrast to traditional matrix factorization
approaches, Poisson factorization implicitly models each user's limited
attention to consume items. Moreover, because of the mathematical form of the
Poisson likelihood, the model needs only to explicitly consider the observed
entries in the matrix, leading to both scalable computation and good predictive
performance. We develop a variational inference algorithm for approximate
posterior inference that scales up to massive data sets. This is an efficient
algorithm that iterates over the observed entries and adjusts an approximate
posterior over the user/item representations. We apply our method to large
real-world user data containing users rating movies, users listening to songs,
and users reading scientific papers. In all these settings, Bayesian Poisson
factorization outperforms state-of-the-art matrix factorization methods.
Constructing Time Series Shape Association Measures: Minkowski Distance and Data Standardization
Authors: Ildar Batyrshin
Date: 2013-11-07 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

It is surprising that last two decades many works in time series data mining
and clustering were concerned with measures of similarity of time series but
not with measures of association that can be used for measuring possible direct
and inverse relationships between time series. Inverse relationships can exist
between dynamics of prices and sell volumes, between growth patterns of
competitive companies, between well production data in oilfields, between wind
velocity and air pollution concentration etc. The paper develops a theoretical
basis for analysis and construction of time series shape association measures.
Starting from the axioms of time series shape association measures it studies
the methods of construction of measures satisfying these axioms. Several
general methods of construction of such measures suitable for measuring time
series shape similarity and shape association are proposed. Time series shape
association measures based on Minkowski distance and data standardization
methods are considered. The cosine similarity and the Pearsons correlation
coefficient are obtained as particular cases of the proposed general methods
that can be used also for construction of new association measures in data
analysis.
Opposition Based ElectromagnetismLike for Global Optimization
Authors: Erik Cuevas, Diego Oliva, Daniel Zaldivar, Marco Perez, Gonzalo Pajares
Date: 2014-05-20 00:00:00
Tasks: Diversity, global-optimization

Electromagnetismlike Optimization (EMO) is a global optimization algorithm,
particularly well suited to solve problems featuring nonlinear and multimodal
cost functions. EMO employs searcher agents that emulate a population of
charged particles which interact to each other according to electromagnetisms
laws of attraction and repulsion. However, EMO usually requires a large number
of iterations for a local search procedure; any reduction or cancelling over
such number, critically perturb other issues such as convergence, exploration,
population diversity and accuracy. This paper presents an enhanced EMO
algorithm called OBEMO, which employs the Opposition-Based Learning (OBL)
approach to accelerate the global convergence speed. OBL is a machine
intelligence strategy which considers the current candidate solution and its
opposite value at the same time, achieving a faster exploration of the search
space. The proposed OBEMO method significantly reduces the required
computational effort yet avoiding any detriment to the good search capabilities
of the original EMO algorithm. Experiments are conducted over a comprehensive
set of benchmark functions, showing that OBEMO obtains promising performance
for most of the discussed test problems.
Multi-ellipses detection on images inspired by collective animal behavior
Authors: Erik Cuevas, Maurici Gonzalez, Daniel Zaldivar, Marco Perez
Date: 2014-05-20 00:00:00

This paper presents a novel and effective technique for extracting multiple
ellipses from an image. The approach employs an evolutionary algorithm to mimic
the way animals behave collectively assuming the overall detection process as a
multi-modal optimization problem. In the algorithm, searcher agents emulate a
group of animals that interact to each other using simple biological rules
which are modeled as evolutionary operators. In turn, such operators are
applied to each agent considering that the complete group has a memory to store
optimal solutions (ellipses) seen so-far by applying a competition principle.
The detector uses a combination of five edge points as parameters to determine
ellipse candidates (possible solutions) while a matching function determines if
such ellipse candidates are actually present in the image. Guided by the values
of such matching functions, the set of encoded candidate ellipses are evolved
through the evolutionary algorithm so that the best candidates can be fitted
into the actual ellipses within the image. Just after the optimization process
ends, an analysis over the embedded memory is executed in order to find the
best obtained solution (the best ellipse) and significant local minima
(remaining ellipses). Experimental results over several complex synthetic and
natural images have validated the efficiency of the proposed technique
regarding accuracy, speed and robustness.
Gaussian Approximation of Collective Graphical Models
Authors: Li-Ping Liu, Daniel Sheldon, Thomas G. Dietterich
Date: 2014-05-20 00:00:00

The Collective Graphical Model (CGM) models a population of independent and
identically distributed individuals when only collective statistics (i.e.,
counts of individuals) are observed. Exact inference in CGMs is intractable,
and previous work has explored Markov Chain Monte Carlo (MCMC) and MAP
approximations for learning and inference. This paper studies Gaussian
approximations to the CGM. As the population grows large, we show that the CGM
distribution converges to a multivariate Gaussian distribution (GCGM) that
maintains the conditional independence properties of the original CGM. If the
observations are exact marginals of the CGM or marginals that are corrupted by
Gaussian noise, inference in the GCGM approximation can be computed efficiently
in closed form. If the observations follow a different noise model (e.g.,
Poisson), then expectation propagation provides efficient and accurate
approximate inference. The accuracy and speed of GCGM inference is compared to
the MCMC and MAP methods on a simulated bird migration problem. The GCGM
matches or exceeds the accuracy of the MAP method while being significantly
faster.
Predicting Online Video Engagement Using Clickstreams
Authors: Everaldo Aguiar, Saurabh Nagrecha, Nitesh V. Chawla
Date: 2014-05-20 00:00:00

In the nascent days of e-content delivery, having a superior product was
enough to give companies an edge against the competition. With today's fiercely
competitive market, one needs to be multiple steps ahead, especially when it
comes to understanding consumers. Focusing on a large set of web portals owned
and managed by a private communications company, we propose methods by which
these sites' clickstream data can be used to provide a deep understanding of
their visitors, as well as their interests and preferences. We further expand
the use of this data to show that it can be effectively used to predict user
engagement to video streams.
Deep AutoRegressive Networks
Authors: Karol Gregor, Ivo Danihelka, andriy mnih, Charles Blundell, Daan Wierstra
Date: 2013-10-31 00:00:00
Tasks: Atari Games, parameter estimation

We introduce a deep, generative autoencoder capable of learning hierarchies
of distributed representations from data. Successive deep stochastic hidden
layers are equipped with autoregressive connections, which enable the model to
be sampled from quickly and exactly via ancestral sampling. We derive an
efficient approximate parameter estimation method based on the minimum
description length (MDL) principle, which can be seen as maximising a
variational lower bound on the log-likelihood, with a feedforward neural
network implementing approximate inference. We demonstrate state-of-the-art
generative performance on a number of classic data sets: several UCI data sets,
MNIST and Atari 2600 games.
Unimodal Bandits: Regret Lower Bounds and Optimal Algorithms
Authors: Richard Combes, Alexandre Proutiere
Date: 2014-05-20 00:00:00
Tasks: Multi-Armed Bandits

We consider stochastic multi-armed bandits where the expected reward is a
unimodal function over partially ordered arms. This important class of problems
has been recently investigated in (Cope 2009, Yu 2011). The set of arms is
either discrete, in which case arms correspond to the vertices of a finite
graph whose structure represents similarity in rewards, or continuous, in which
case arms belong to a bounded interval. For discrete unimodal bandits, we
derive asymptotic lower bounds for the regret achieved under any algorithm, and
propose OSUB, an algorithm whose regret matches this lower bound. Our algorithm
optimally exploits the unimodal structure of the problem, and surprisingly, its
asymptotic regret does not depend on the number of arms. We also provide a
regret upper bound for OSUB in non-stationary environments where the expected
rewards smoothly evolve over time. The analytical results are supported by
numerical experiments showing that OSUB performs significantly better than the
state-of-the-art algorithms. For continuous sets of arms, we provide a brief
discussion. We show that combining an appropriate discretization of the set of
arms with the UCB algorithm yields an order-optimal regret, and in practice,
outperforms recently proposed algorithms designed to exploit the unimodal
structure.
An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation
Authors: Erik Cuevas, Alonso Echavarria, Marte A. Ramirez-Ortegon
Date: 2014-05-20 00:00:00
Tasks: Evolutionary Algorithms

The ability of an Evolutionary Algorithm (EA) to find a global optimal
solution depends on its capacity to find a good rate between exploitation of
found so far elements and exploration of the search space. Inspired by natural
phenomena, researchers have developed many successful evolutionary algorithms
which, at original versions, define operators that mimic the way nature solves
complex problems, with no actual consideration of the exploration/exploitation
balance. In this paper, a novel nature-inspired algorithm called the States of
Matter Search (SMS) is introduced. The SMS algorithm is based on the simulation
of the states of matter phenomenon. In SMS, individuals emulate molecules which
interact to each other by using evolutionary operations which are based on the
physical principles of the thermal-energy motion mechanism. The algorithm is
devised by considering each state of matter at one different
exploration/exploitation ratio. The evolutionary process is divided into three
phases which emulate the three states of matter: gas, liquid and solid. In each
state, molecules (individuals) exhibit different movement capacities. Beginning
from the gas state (pure exploration), the algorithm modifies the intensities
of exploration and exploitation until the solid state (pure exploitation) is
reached. As a result, the approach can substantially improve the balance
between exploration/exploitation, yet preserving the good search capabilities
of an evolutionary approach.
A Genetic Algorithm for solving Quadratic Assignment Problem(QAP)
Authors: Hosein Azarbonyad, Reza Babazadeh
Date: 2014-05-20 00:00:00
Tasks: Combinatorial Optimization

The Quadratic Assignment Problem (QAP) is one of the models used for the
multi-row layout problem with facilities of equal area. There are a set of n
facilities and a set of n locations. For each pair of locations, a distance is
specified and for each pair of facilities a weight or flow is specified (e.g.,
the amount of supplies transported between the two facilities). The problem is
to assign all facilities to different locations with the aim of minimizing the
sum of the distances multiplied by the corresponding flows. The QAP is among
the most difficult NP-hard combinatorial optimization problems. Because of
this, this paper presents an efficient Genetic algorithm (GA) to solve this
problem in reasonable time. For validation the proposed GA some examples are
selected from QAP library. The obtained results in reasonable time show the
efficiency of proposed GA.
Towards A Theory-Of-Mind-Inspired Generic Decision-Making Framework
Authors: Mihai Polceanu, Cédric Buche
Date: 2014-05-20 00:00:00
Tasks: Decision Making

Simulation is widely used to make model-based predictions, but few approaches
have attempted this technique in dynamic physical environments of medium to
high complexity or in general contexts. After an introduction to the cognitive
science concepts from which this work is inspired and the current development
in the use of simulation as a decision-making technique, we propose a generic
framework based on theory of mind, which allows an agent to reason and perform
actions using multiple simulations of automatically created or externally
inputted models of the perceived environment. A description of a partial
implementation is given, which aims to solve a popular game within the
IJCAI2013 AIBirds contest. Results of our approach are presented, in comparison
with the competition benchmark. Finally, future developments regarding the
framework are discussed.
A Parallel Way to Select the Parameters of SVM Based on the Ant Optimization Algorithm
Authors: Chao Zhang, Hong-cen Mei, Hao Yang
Date: 2014-05-19 00:00:00
Tasks: Classification, General Classification, Handwriting Recognition, image-classification, Image Classification, text-classification, Text Classification

A large number of experimental data shows that Support Vector Machine (SVM)
algorithm has obvious advantages in text classification, handwriting
recognition, image classification, bioinformatics, and some other fields. To
some degree, the optimization of SVM depends on its kernel function and Slack
variable, the determinant of which is its parameters $\delta$ and c in the
classification function. That is to say,to optimize the SVM algorithm, the
optimization of the two parameters play a huge role. Ant Colony Optimization
(ACO) is optimization algorithm which simulate ants to find the optimal path.In
the available literature, we mix the ACO algorithm and Parallel algorithm
together to find a well parameters.
Learning to Exploit Different Translation Resources for Cross Language Information Retrieval
Authors: Hosein Azarbonyad, Azadeh Shakery, Heshaam Faili
Date: 2014-05-20 00:00:00
Tasks: Information Retrieval, Learning-To-Rank, Retrieval, Translation

One of the important factors that affects the performance of Cross Language
Information Retrieval(CLIR)is the quality of translations being employed in
CLIR. In order to improve the quality of translations, it is important to
exploit available resources efficiently. Employing different translation
resources with different characteristics has many challenges. In this paper, we
propose a method for exploiting available translation resources simultaneously.
This method employs Learning to Rank(LTR) for exploiting different translation
resources. To apply LTR methods for query translation, we define different
translation relation based features in addition to context based features. We
use the contextual information contained in translation resources for
extracting context based features.The proposed method uses LTR to construct a
translation ranking model based on defined features. The constructed model is
used for ranking translation candidates of query words. To evaluate the
proposed method we do English-Persian CLIR, in which we employ the translation
ranking model to find translations of English queries and employ the
translations to retrieve Persian documents. Experimental results show that our
approach significantly outperforms single resource based CLIR methods.
Auto-adaptative Laplacian Pyramids for High-dimensional Data Analysis
Authors: Ángela Fernández, Neta Rabin, Dalia Fishelov, José R. Dorronsoro
Date: 2013-11-26 00:00:00
Tasks: Clustering, Dimensionality Reduction, Vocal Bursts Intensity Prediction

Non-linear dimensionality reduction techniques such as manifold learning
algorithms have become a common way for processing and analyzing
high-dimensional patterns that often have attached a target that corresponds to
the value of an unknown function. Their application to new points consists in
two steps: first, embedding the new data point into the low dimensional space
and then, estimating the function value on the test point from its neighbors in
the embedded space.
  However, finding the low dimension representation of a test point, while easy
for simple but often not powerful enough procedures such as PCA, can be much
more complicated for methods that rely on some kind of eigenanalysis, such as
Spectral Clustering (SC) or Diffusion Maps (DM). Similarly, when a target
function is to be evaluated, averaging methods like nearest neighbors may give
unstable results if the function is noisy. Thus, the smoothing of the target
function with respect to the intrinsic, low-dimensional representation that
describes the geometric structure of the examined data is a challenging task.
  In this paper we propose Auto-adaptive Laplacian Pyramids (ALP), an extension
of the standard Laplacian Pyramids model that incorporates a modified LOOCV
procedure that avoids the large cost of the standard one and offers the
following advantages: (i) it selects automatically the optimal function
resolution (stopping time) adapted to the data and its noise, (ii) it is easy
to apply as it does not require parameterization, (iii) it does not overfit the
training set and (iv) it adds no extra cost compared to other classical
interpolation methods. We illustrate numerically ALP's behavior on a synthetic
problem and apply it to the computation of the DM projection of new patterns
and to the extension to them of target function values on a radiation
forecasting problem over very high dimensional patterns.
Secure Friend Discovery via Privacy-Preserving and Decentralized Community Detection
Authors: Pili Hu, Sherman S. M. Chow, Wing Cheong Lau
Date: 2014-05-20 00:00:00
Tasks: Community Detection, Privacy Preserving

The problem of secure friend discovery on a social network has long been
proposed and studied. The requirement is that a pair of nodes can make
befriending decisions with minimum information exposed to the other party. In
this paper, we propose to use community detection to tackle the problem of
secure friend discovery. We formulate the first privacy-preserving and
decentralized community detection problem as a multi-objective optimization. We
design the first protocol to solve this problem, which transforms community
detection to a series of Private Set Intersection (PSI) instances using
Truncated Random Walk (TRW). Preliminary theoretical results show that our
protocol can uncover communities with overwhelming probability and preserve
privacy. We also discuss future works, potential extensions and variations.
Minimal Dirichlet energy partitions for graphs
Authors: Braxton Osting, Chris D. White, Edouard Oudet
Date: 2013-08-22 00:00:00
Tasks: Clustering, graph partitioning

Motivated by a geometric problem, we introduce a new non-convex graph
partitioning objective where the optimality criterion is given by the sum of
the Dirichlet eigenvalues of the partition components. A relaxed formulation is
identified and a novel rearrangement algorithm is proposed, which we show is
strictly decreasing and converges in a finite number of iterations to a local
minimum of the relaxed objective function. Our method is applied to several
clustering problems on graphs constructed from synthetic data, MNIST
handwritten digits, and manifold discretizations. The model has a
semi-supervised extension and provides a natural representative for the
clusters as well.