pwc-archive/papers-with-abstracts huggingface dataset:
SEEDS: Superpixels Extracted via Energy-Driven Sampling
Authors: Michael Van den Bergh, Xavier Boix, Gemma Roig, Luc van Gool
Date: 2013-09-16 00:00:00
Tasks: CPU, Superpixels

Superpixel algorithms aim to over-segment the image by grouping pixels that
belong to the same object. Many state-of-the-art superpixel algorithms rely on
minimizing objective functions to enforce color ho- mogeneity. The optimization
is accomplished by sophis- ticated methods that progressively build the
superpix- els, typically by adding cuts or growing superpixels. As a result,
they are computationally too expensive for real-time applications. We introduce
a new approach based on a simple hill-climbing optimization. Starting from an
initial superpixel partitioning, it continuously refines the superpixels by
modifying the boundaries. We define a robust and fast to evaluate energy
function, based on enforcing color similarity between the bound- aries and the
superpixel color histogram. In a series of experiments, we show that we achieve
an excellent com- promise between accuracy and efficiency. We are able to
achieve a performance comparable to the state-of- the-art, but in real-time on
a single Intel i7 CPU at 2.8GHz.
Estimation of intrinsic volumes from digital grey-scale images
Authors: Anne Marie Svane
Date: 2013-09-16 00:00:00

Local algorithms are common tools for estimating intrinsic volumes from
black-and-white digital images. However, these algorithms are typically biased
in the design based setting, even when the resolution tends to infinity.
Moreover, images recorded in practice are most often blurred grey-scale images
rather than black-and-white. In this paper, an extended definition of local
algorithms, applying directly to grey-scale images without thresholding, is
suggested. We investigate the asymptotics of these new algorithms when the
resolution tends to infinity and apply this to construct estimators for surface
area and integrated mean curvature that are asymptotically unbiased in certain
natural settings.
An iterative algorithm for computed tomography image reconstruction from limited-angle projections
Authors: Yuli Sun, Jinxu Tao, Conggui Liu
Date: 2013-09-16 00:00:00
Tasks: Image Reconstruction

In application of tomography imaging, limited-angle problem is a quite
practical and important issue. In this paper, an iterative
reprojection-reconstruction (IRR) algorithm using a modified Papoulis-Gerchberg
(PG) iterative scheme is developed for reconstruction from limited-angle
projections which contain noise. The proposed algorithm has two iterative
update processes, one is the extrapolation of unknown data, and the other is
the modification of the known noisy observation data. And the algorithm
introduces scaling factors to control the two processes, respectively. The
convergence of the algorithm is guaranteed, and the method of choosing the
scaling factors is given with energy constraints. The simulation result
demonstrates our conclusions and indicates that the algorithm proposed in this
paper can obviously improve the reconstruction quality.
Fast and accurate sentiment classification using an enhanced Naive Bayes model
Authors: Vivek Narayanan, Ishan Arora, Arjun Bhatia
Date: 2013-05-27 00:00:00
Tasks: feature selection, General Classification, Negation, Sentiment Analysis, Sentiment Classification

We have explored different methods of improving the accuracy of a Naive Bayes
classifier for sentiment analysis. We observed that a combination of methods
like negation handling, word n-grams and feature selection by mutual
information results in a significant improvement in accuracy. This implies that
a highly accurate and fast sentiment classifier can be built using a simple
Naive Bayes model that has linear training and testing time complexities. We
achieved an accuracy of 88.80% on the popular IMDB movie reviews dataset.
Multiplicative Approximations, Optimal Hypervolume Distributions, and the Choice of the Reference Point
Authors: Tobias Friedrich, Frank Neumann, Christian Thyssen
Date: 2013-09-16 00:00:00
Tasks: Evolutionary Algorithms

Many optimization problems arising in applications have to consider several
objective functions at the same time. Evolutionary algorithms seem to be a very
natural choice for dealing with multi-objective problems as the population of
such an algorithm can be used to represent the trade-offs with respect to the
given objective functions. In this paper, we contribute to the theoretical
understanding of evolutionary algorithms for multi-objective problems. We
consider indicator-based algorithms whose goal is to maximize the hypervolume
for a given problem by distributing {\mu} points on the Pareto front. To gain
new theoretical insights into the behavior of hypervolume-based algorithms we
compare their optimization goal to the goal of achieving an optimal
multiplicative approximation ratio. Our studies are carried out for different
Pareto front shapes of bi-objective problems. For the class of linear fronts
and a class of convex fronts, we prove that maximizing the hypervolume gives
the best possible approximation ratio when assuming that the extreme points
have to be included in both distributions of the points on the Pareto front.
Furthermore, we investigate the choice of the reference point on the
approximation behavior of hypervolume-based approaches and examine Pareto
fronts of different shapes by numerical calculations.
Visual-Semantic Scene Understanding by Sharing Labels in a Context Network
Authors: Ishani Chakraborty, Ahmed Elgammal
Date: 2013-09-16 00:00:00
Tasks: Data Augmentation, Object, Scene Understanding

We consider the problem of naming objects in complex, natural scenes
containing widely varying object appearance and subtly different names.
Informed by cognitive research, we propose an approach based on sharing context
based object hypotheses between visual and lexical spaces. To this end, we
present the Visual Semantic Integration Model (VSIM) that represents object
labels as entities shared between semantic and visual contexts and infers a new
image by updating labels through context switching. At the core of VSIM is a
semantic Pachinko Allocation Model and a visual nearest neighbor Latent
Dirichlet Allocation Model. For inference, we derive an iterative Data
Augmentation algorithm that pools the label probabilities and maximizes the
joint label posterior of an image. Our model surpasses the performance of
state-of-art methods in several visual tasks on the challenging SUN09 dataset.
Local Support Vector Machines:Formulation and Analysis
Authors: Ravi Ganti, Alexander Gray
Date: 2013-09-14 00:00:00

We provide a formulation for Local Support Vector Machines (LSVMs) that
generalizes previous formulations, and brings out the explicit connections to
local polynomial learning used in nonparametric estimation literature. We
investigate the simplest type of LSVMs called Local Linear Support Vector
Machines (LLSVMs). For the first time we establish conditions under which
LLSVMs make Bayes consistent predictions at each test point $x_0$. We also
establish rates at which the local risk of LLSVMs converges to the minimum
value of expected local risk at each point $x_0$. Using stability arguments we
establish generalization error bounds for LLSVMs.
Group Learning and Opinion Diffusion in a Broadcast Network
Authors: Yang Liu, Mingyan Liu
Date: 2013-09-14 00:00:00

We analyze the following group learning problem in the context of opinion
diffusion: Consider a network with $M$ users, each facing $N$ options. In a
discrete time setting, at each time step, each user chooses $K$ out of the $N$
options, and receive randomly generated rewards, whose statistics depend on the
options chosen as well as the user itself, and are unknown to the users. Each
user aims to maximize their expected total rewards over a certain time horizon
through an online learning process, i.e., a sequence of exploration (sampling
the return of each option) and exploitation (selecting empirically good
options) steps.
  Within this context we consider two group learning scenarios, (1) users with
uniform preferences and (2) users with diverse preferences, and examine how a
user should construct its learning process to best extract information from
other's decisions and experiences so as to maximize its own reward. Performance
is measured in {\em weak regret}, the difference between the user's total
reward and the reward from a user-specific best single-action policy (i.e.,
always selecting the set of options generating the highest mean rewards for
this user). Within each scenario we also consider two cases: (i) when users
exchange full information, meaning they share the actual rewards they obtained
from their choices, and (ii) when users exchange limited information, e.g.,
only their choices but not rewards obtained from these choices.
Optimized projections for compressed sensing via rank-constrained nearest correlation matrix
Authors: Nicolae Cleju
Date: 2013-09-14 00:00:00
Tasks: compressed sensing

Optimizing the acquisition matrix is useful for compressed sensing of signals
that are sparse in overcomplete dictionaries, because the acquisition matrix
can be adapted to the particular correlations of the dictionary atoms. In this
paper a novel formulation of the optimization problem is proposed, in the form
of a rank-constrained nearest correlation matrix problem. Furthermore,
improvements for three existing optimization algorithms are introduced, which
are shown to be particular instances of the proposed formulation. Simulation
results show notable improvements and superior robustness in sparse signal
recovery.
Incentives for Privacy Tradeoff in Community Sensing
Authors: Adish Singla, Andreas Krause
Date: 2013-08-19 00:00:00

Community sensing, fusing information from populations of privately-held
sensors, presents a great opportunity to create efficient and cost-effective
sensing applications. Yet, reasonable privacy concerns often limit the access
to such data streams. How should systems valuate and negotiate access to
private information, for example in return for monetary incentives? How should
they optimally choose the participants from a large population of strategic
users with privacy concerns, and compensate them for information shared? In
this paper, we address these questions and present a novel mechanism,
SeqTGreedy, for budgeted recruitment of participants in community sensing. We
first show that privacy tradeoffs in community sensing can be cast as an
adaptive submodular optimization problem. We then design a budget feasible,
incentive compatible (truthful) mechanism for adaptive submodular maximization,
which achieves near-optimal utility for a large class of sensing applications.
This mechanism is general, and of independent interest. We demonstrate the
effectiveness of our approach in a case study of air quality monitoring, using
data collected from the Mechanical Turk platform. Compared to the state of the
art, our approach achieves up to 30% reduction in cost in order to achieve a
desired level of utility.
Ultrametric Component Analysis with Application to Analysis of Text and of Emotion
Authors: Fionn Murtagh
Date: 2013-09-14 00:00:00

We review the theory and practice of determining what parts of a data set are
ultrametric. It is assumed that the data set, to begin with, is endowed with a
metric, and we include discussion of how this can be brought about if a
dissimilarity, only, holds. The basis for part of the metric-endowed data set
being ultrametric is to consider triplets of the observables (vectors). We
develop a novel consensus of hierarchical clusterings. We do this in order to
have a framework (including visualization and supporting interpretation) for
the parts of the data that are determined to be ultrametric. Furthermore a
major objective is to determine locally ultrametric relationships as opposed to
non-local ultrametric relationships. As part of this work, we also study a
particular property of our ultrametricity coefficient, namely, it being a
function of the difference of angles of the base angles of the isosceles
triangle. This work is completed by a review of related work, on consensus
hierarchies, and of a major new application, namely quantifying and
interpreting the emotional content of narrative.
Mixed Membership Models for Time Series
Authors: Emily B. Fox, Michael. I. Jordan
Date: 2013-09-13 00:00:00
Tasks: Time Series, Time Series Analysis

In this article we discuss some of the consequences of the mixed membership
perspective on time series analysis. In its most abstract form, a mixed
membership model aims to associate an individual entity with some set of
attributes based on a collection of observed data. Although much of the
literature on mixed membership models considers the setting in which
exchangeable collections of data are associated with each member of a set of
entities, it is equally natural to consider problems in which an entire time
series is viewed as an entity and the goal is to characterize the time series
in terms of a set of underlying dynamic attributes or "dynamic regimes".
Indeed, this perspective is already present in the classical hidden Markov
model, where the dynamic regimes are referred to as "states", and the
collection of states realized in a sample path of the underlying process can be
viewed as a mixed membership characterization of the observed time series. Our
goal here is to review some of the richer modeling possibilities for time
series that are provided by recent developments in the mixed membership
framework.
A two-layer Conditional Random Field for the classification of partially occluded objects
Authors: Sergey Kosov, Pushmeet Kohli, Franz Rottensteiner, Christian Heipke
Date: 2013-07-11 00:00:00
Tasks: General Classification

Conditional Random Fields (CRF) are among the most popular techniques for
image labelling because of their flexibility in modelling dependencies between
the labels and the image features. This paper proposes a novel CRF-framework
for image labeling problems which is capable to classify partially occluded
objects. Our approach is evaluated on aerial near-vertical images as well as on
urban street-view images and compared with another methods.
Algebraic Properties of Qualitative Spatio-Temporal Calculi
Authors: Frank Dylla, Till Mossakowski, Thomas Schneider, Diedrich Wolter
Date: 2013-05-31 00:00:00
Tasks: General Classification

Qualitative spatial and temporal reasoning is based on so-called qualitative
calculi. Algebraic properties of these calculi have several implications on
reasoning algorithms. But what exactly is a qualitative calculus? And to which
extent do the qualitative calculi proposed meet these demands? The literature
provides various answers to the first question but only few facts about the
second. In this paper we identify the minimal requirements to binary
spatio-temporal calculi and we discuss the relevance of the according axioms
for representation and reasoning. We also analyze existing qualitative calculi
and provide a classification involving different notions of a relation algebra.
A method for nose-tip based 3D face registration using maximum intensity algorithm
Authors: Parama Bagchi, Debotosh Bhattacharjee, Mita Nasipuri, Dipak kr. Basu
Date: 2013-09-13 00:00:00
Tasks: Translation

In this paper we present a novel technique of registering 3D images across
pose. In this context, we have taken into account the images which are aligned
across X, Y and Z axes. We have first determined the angle across which the
image is rotated with respect to X, Y and Z axes and then translation is
performed on the images. After testing the proposed method on 472 images from
the FRAV3D database, the method correctly registers 358 images thus giving a
performance rate of 75.84%.
A Novel Approach in detecting pose orientation of a 3D face required for face
Authors: Parama Bagchi, Debotosh Bhattacharjee, Mita Nasipuri, Dipak Kumar Basu
Date: 2013-09-13 00:00:00

In this paper we present a novel approach that takes as input a 3D image and gives as output its pose i.e. it tells whether the face is oriented with respect the X, Y or Z axes with angles of rotation up to 40 degree. All the experiments have been performed on the FRAV3D Database. After applying the proposed algorithm to the 3D facial surface we have obtained i.e. on 848 3D face images our method detected the pose correctly for 566 face images,thus giving an approximately 67 % of correct pose detection.
A tabu search algorithm with efficient diversification strategy for high school timetabling problem
Authors: Salman Hooshmand, Mehdi Behshameh, Omid Hamidi
Date: 2013-09-12 00:00:00
Tasks: Scheduling

The school timetabling problem can be described as scheduling a set of
lessons (combination of classes, teachers, subjects and rooms) in a weekly
timetable. This paper presents a novel way to generate timetables for high
schools. The algorithm has three phases. Pre-scheduling, initial phase and
optimization through tabu search. In the first phase, a graph based algorithm
used to create groups of lessons to be scheduled simultaneously; then an
initial solution is built by a sequential greedy heuristic. Finally, the
solution is optimized using tabu search algorithm based on frequency based
diversification. The algorithm has been tested on a set of real problems
gathered from Iranian high schools. Experiments show that the proposed
algorithm can effectively build acceptable timetables.
Using memristor crossbar structure to implement a novel adaptive real time fuzzy modeling algorithm
Authors: Iman Esmaili Paeen Afrakoti, Saeed Bagheri Shouraki, Farnood Merrikhbayat
Date: 2013-09-12 00:00:00
Tasks: Active Learning

Although fuzzy techniques promise fast meanwhile accurate modeling and
control abilities for complicated systems, different difficulties have been
re-vealed in real situation implementations. Usually there is no escape of
it-erative optimization based on crisp domain algorithms. Recently memristor
structures appeared promising to implement neural network structures and fuzzy
algorithms. In this paper a novel adaptive real-time fuzzy modeling algorithm
is proposed which uses active learning method concept to mimic recent
understandings of right brain processing techniques. The developed method is
based on processing fuzzy numbers to provide the ability of being sensitive to
each training data point to expand the knowledge tree leading to plasticity
while used defuzzification technique guaranties enough stability. An
outstanding characteristic of the proposed algorithm is its consistency to
memristor crossbar hardware processing concepts. An analog implemen-tation of
the proposed algorithm on memristor crossbars structure is also introduced in
this paper. The effectiveness of the proposed algorithm in modeling and pattern
recognition tasks is verified by means of computer simulations
Efficient Orthogonal Tensor Decomposition, with an Application to Latent Variable Model Learning
Authors: Franz J. Király
Date: 2013-09-12 00:00:00
Tasks: Tensor Decomposition

Decomposing tensors into orthogonal factors is a well-known task in
statistics, machine learning, and signal processing. We study orthogonal outer
product decompositions where the factors in the summands in the decomposition
are required to be orthogonal across summands, by relating this orthogonal
decomposition to the singular value decompositions of the flattenings. We show
that it is a non-trivial assumption for a tensor to have such an orthogonal
decomposition, and we show that it is unique (up to natural symmetries) in case
it exists, in which case we also demonstrate how it can be efficiently and
reliably obtained by a sequence of singular value decompositions. We
demonstrate how the factoring algorithm can be applied for parameter
identification in latent variable and mixture models.
Modeling Based on Elman Wavelet Neural Network for Class-D Power Amplifiers
Authors: Li Wang, Jie Shao, Yaqin Zhong, Weisong Zhao, Reza Malekian
Date: 2013-09-12 00:00:00

In Class-D Power Amplifiers (CDPAs), the power supply noise can intermodulate
with the input signal, manifesting into power-supply induced intermodulation
distortion (PS-IMD) and due to the memory effects of the system, there exist
asymmetries in the PS-IMDs. In this paper, a new behavioral modeling based on
the Elman Wavelet Neural Network (EWNN) is proposed to study the nonlinear
distortion of the CDPAs. In EWNN model, the Morlet wavelet functions are
employed as the activation function and there is a normalized operation in the
hidden layer, the modification of the scale factor and translation factor in
the wavelet functions are ignored to avoid the fluctuations of the error
curves. When there are 30 neurons in the hidden layer, to achieve the same
square sum error (SSE) $\epsilon_{min}=10^{-3}$, EWNN needs 31 iteration steps,
while the basic Elman neural network (BENN) model needs 86 steps. The
Volterra-Laguerre model has 605 parameters to be estimated but still can't
achieve the same magnitude accuracy of EWNN. Simulation results show that the
proposed approach of EWNN model has fewer parameters and higher accuracy than
the Volterra-Laguerre model and its convergence rate is much faster than the
BENN model.
A new framework for optimal classifier design
Authors: Matías Di Martino, Guzman Hernández, Marcelo Fiori, Alicia Fernández
Date: 2013-05-07 00:00:00

The use of alternative measures to evaluate classifier performance is gaining
attention, specially for imbalanced problems. However, the use of these
measures in the classifier design process is still unsolved. In this work we
propose a classifier designed specifically to optimize one of these alternative
measures, namely, the so-called F-measure. Nevertheless, the technique is
general, and it can be used to optimize other evaluation measures. An algorithm
to train the novel classifier is proposed, and the numerical scheme is tested
with several databases, showing the optimality and robustness of the presented
classifier.
Multi-view in Lensless Compressive Imaging
Authors: Hong Jiang, Gang Huang, Paul Wilford
Date: 2013-06-17 00:00:00
Tasks: Compressive Sensing

Multi-view images are acquired by a lensless compressive imaging
architecture, which consists of an aperture assembly and multiple sensors. The
aperture assembly consists of a two dimensional array of aperture elements
whose transmittance can be individually controlled to implement a compressive
sensing matrix. For each transmittance pattern of the aperture assembly, each
of the sensors takes a measurement. The measurement vectors from the multiple
sensors represent multi-view images of the same scene. We present theoretical
framework for multi-view reconstruction and experimental results for enhancing
quality of image using multi-view.
Cache Performance Study of Portfolio-Based Parallel CDCL SAT Solvers
Authors: Roberto Asín, Juan Olate, Leo Ferres
Date: 2013-09-12 00:00:00

Parallel SAT solvers are becoming mainstream. Their performance has made them
win the past two SAT competitions consecutively and are in the limelight of
research and industry. The problem is that it is not known exactly what is
needed to make them perform even better; that is, how to make them solve more
problems in less time. Also, it is also not know how well they scale in massive
multi-core environments which, predictably, is the scenario of comming new
hardware. In this paper we show that cache contention is a main culprit of a
slowing down in scalability, and provide empirical results that for some type
of searches, physically sharing the clause Database between threads is
beneficial.