pwc-archive/papers-with-abstracts huggingface dataset:
A Combined Approach for Constraints over Finite Domains and Arrays
Authors: Sébastien Bardin, Arnaud Gotlieb
Date: 2013-12-01 00:00:00

Arrays are ubiquitous in the context of software verification. However,
effective reasoning over arrays is still rare in CP, as local reasoning is
dramatically ill-conditioned for constraints over arrays. In this paper, we
propose an approach combining both global symbolic reasoning and local
consistency filtering in order to solve constraint systems involving arrays
(with accesses, updates and size constraints) and finite-domain constraints
over their elements and indexes. Our approach, named FDCC, is based on a
combination of a congruence closure algorithm for the standard theory of arrays
and a CP solver over finite domains. The tricky part of the work lies in the
bi-directional communication mechanism between both solvers. We identify the
significant information to share, and design ways to master the communication
overhead. Experiments on random instances show that FDCC solves more formulas
than any portfolio combination of the two solvers taken in isolation, while
overhead is kept reasonable.
No Free Lunch Theorem and Bayesian probability theory: two sides of the same coin. Some implications for black-box optimization and metaheuristics
Authors: Loris Serafino
Date: 2013-11-23 00:00:00
Tasks: Bayesian Optimization

Challenging optimization problems, which elude acceptable solution via
conventional calculus methods, arise commonly in different areas of industrial
design and practice. Hard optimization problems are those who manifest the
following behavior: a) high number of independent input variables; b) very
complex or irregular multi-modal fitness; c) computational expensive fitness
evaluation. This paper will focus on some theoretical issues that have strong
implications for practice. I will stress how an interpretation of the No Free
Lunch theorem leads naturally to a general Bayesian optimization framework. The
choice of a prior over the space of functions is a critical and inevitable step
in every black-box optimization.
A Typology of Collaboration Platform Users
Authors: Anastasia Bezzubtseva, Dmitry I. Ignatov
Date: 2013-11-30 00:00:00

In this paper we present a review of the existing typologies of Internet
service users. We zoom in on social networking services including blogs and
crowdsourcing websites. Based on the results of the analysis of the considered
typologies obtained by means of FCA we developed a new user typology of a
certain class of Internet services, namely a collaboration innovation platform.
Cluster analysis of data extracted from the collaboration platform Witology was
used to divide more than 500 participants into six groups based on three
activity indicators: idea generation, commenting, and evaluation (assigning
marks) The obtained groups and their percentages appear to follow the "90 - 9 -
1" rule.
Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization
Authors: Po-Yu Chen, Ivan W. Selesnick
Date: 2013-08-23 00:00:00
Tasks: Denoising, Speech Enhancement

Convex optimization with sparsity-promoting convex regularization is a
standard approach for estimating sparse signals in noise. In order to promote
sparsity more strongly than convex regularization, it is also standard practice
to employ non-convex optimization. In this paper, we take a third approach. We
utilize a non-convex regularization term chosen such that the total cost
function (consisting of data consistency and regularization terms) is convex.
Therefore, sparsity is more strongly promoted than in the standard convex
formulation, but without sacrificing the attractive aspects of convex
optimization (unique minimum, robust algorithms, etc.). We use this idea to
improve the recently developed 'overlapping group shrinkage' (OGS) algorithm
for the denoising of group-sparse signals. The algorithm is applied to the
problem of speech enhancement with favorable results in terms of both SNR and
perceptual quality.
Characterizing and Extending Answer Set Semantics using Possibility Theory
Authors: Kim Bauters, Steven Schockaert, Martine De Cock, Dirk Vermeir
Date: 2013-11-30 00:00:00

Answer Set Programming (ASP) is a popular framework for modeling
combinatorial problems. However, ASP cannot easily be used for reasoning about
uncertain information. Possibilistic ASP (PASP) is an extension of ASP that
combines possibilistic logic and ASP. In PASP a weight is associated with each
rule, where this weight is interpreted as the certainty with which the
conclusion can be established when the body is known to hold. As such, it
allows us to model and reason about uncertain information in an intuitive way.
In this paper we present new semantics for PASP, in which rules are interpreted
as constraints on possibility distributions. Special models of these
constraints are then identified as possibilistic answer sets. In addition,
since ASP is a special case of PASP in which all the rules are entirely
certain, we obtain a new characterization of ASP in terms of constraints on
possibility distributions. This allows us to uncover a new form of disjunction,
called weak disjunction, that has not been previously considered in the
literature. In addition to introducing and motivating the semantics of weak
disjunction, we also pinpoint its computational complexity. In particular,
while the complexity of most reasoning tasks coincides with standard
disjunctive ASP, we find that brave reasoning for programs with weak
disjunctions is easier.
Improving Texture Categorization with Biologically Inspired Filtering
Authors: Ngoc-Son Vu, Thanh Phuong Nguyen, Christophe Garcia
Date: 2013-11-30 00:00:00
Tasks: Classification, General Classification, Texture Classification

Within the domain of texture classification, a lot of effort has been spent
on local descriptors, leading to many powerful algorithms. However,
preprocessing techniques have received much less attention despite their
important potential for improving the overall classification performance. We
address this question by proposing a novel, simple, yet very powerful
biologically-inspired filtering (BF) which simulates the performance of human
retina. In the proposed approach, given a texture image, after applying a DoG
filter to detect the "edges", we first split the filtered image into two "maps"
alongside the sides of its edges. The feature extraction step is then carried
out on the two "maps" instead of the input image. Our algorithm has several
advantages such as simplicity, robustness to illumination and noise, and
discriminative power. Experimental results on three large texture databases
show that with an extremely low computational cost, the proposed method
improves significantly the performance of many texture classification systems,
notably in noisy environments. The source codes of the proposed algorithm can
be downloaded from https://sites.google.com/site/nsonvu/code.
One-Class Classification: Taxonomy of Study and Review of Techniques
Authors: Shehroz S. Khan, Michael G. Madden
Date: 2013-11-30 00:00:00
Tasks: Classification, General Classification, Novelty Detection, One-Class Classification

One-class classification (OCC) algorithms aim to build classification models
when the negative class is either absent, poorly sampled or not well defined.
This unique situation constrains the learning of efficient classifiers by
defining class boundary just with the knowledge of positive class. The OCC
problem has been considered and applied under many research themes, such as
outlier/novelty detection and concept learning. In this paper we present a
unified view of the general problem of OCC by presenting a taxonomy of study
for OCC problems, which is based on the availability of training data,
algorithms used and the application domains applied. We further delve into each
of the categories of the proposed taxonomy and present a comprehensive
literature review of the OCC algorithms, techniques and methodologies with a
focus on their significance, limitations and applications. We conclude our
paper by discussing some open research problems in the field of OCC and present
our vision for future research.
Stochastic Optimization of Smooth Loss
Authors: Rong Jin
Date: 2013-11-30 00:00:00
Tasks: Stochastic Optimization

In this paper, we first prove a high probability bound rather than an
expectation bound for stochastic optimization with smooth loss. Furthermore,
the existing analysis requires the knowledge of optimal classifier for tuning
the step size in order to achieve the desired bound. However, this information
is usually not accessible in advanced. We also propose a strategy to address
the limitation.
Top-k Query Answering in Datalog+/- Ontologies under Subjective Reports (Technical Report)
Authors: Thomas Lukasiewicz, Maria Vanina Martinez, Cristian Molinaro, Livia Predoiu, Gerardo I. Simari
Date: 2013-11-29 00:00:00

The use of preferences in query answering, both in traditional databases and
in ontology-based data access, has recently received much attention, due to its
many real-world applications. In this paper, we tackle the problem of top-k
query answering in Datalog+/- ontologies subject to the querying user's
preferences and a collection of (subjective) reports of other users. Here, each
report consists of scores for a list of features, its author's preferences
among the features, as well as other information. Theses pieces of information
of every report are then combined, along with the querying user's preferences
and his/her trust into each report, to rank the query results. We present two
alternative such rankings, along with algorithms for top-k (atomic) query
answering under these rankings. We also show that, under suitable assumptions,
these algorithms run in polynomial time in the data complexity. We finally
present more general reports, which are associated with sets of atoms rather
than single atoms.
Combination of Diverse Ranking Models for Personalized Expedia Hotel Searches
Authors: Xudong Liu, Bing Xu, Yuyu Zhang, Qiang Yan, Liang Pang, Qiang Li, Hanxiao Sun, Bin Wang
Date: 2013-11-29 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering

The ICDM Challenge 2013 is to apply machine learning to the problem of hotel
ranking, aiming to maximize purchases according to given hotel characteristics,
location attractiveness of hotels, user's aggregated purchase history and
competitive online travel agency information for each potential hotel choice.
This paper describes the solution of team "binghsu & MLRush & BrickMover". We
conduct simple feature engineering work and train different models by each
individual team member. Afterwards, we use listwise ensemble method to combine
each model's output. Besides describing effective model and features, we will
discuss about the lessons we learned while using deep learning in this
competition.
The Power of Asymmetry in Binary Hashing
Authors: Behnam Neyshabur, Payman Yadollahpour, Yury Makarychev, Ruslan Salakhutdinov, Nathan Srebro
Date: 2013-11-29 00:00:00
Proceeding: NeurIPS 2013 12

When approximating binary similarity using the hamming distance between short
binary hashes, we show that even if the similarity is symmetric, we can have
shorter and more accurate hashes by using two distinct code maps. I.e. by
approximating the similarity between $x$ and $x'$ as the hamming distance
between $f(x)$ and $g(x')$, for two distinct binary codes $f,g$, rather than as
the hamming distance between $f(x)$ and $f(x')$.
Statistical estimation for optimization problems on graphs
Authors: Mikhail Langovoy, Suvrit Sra
Date: 2013-11-29 00:00:00
Tasks: Combinatorial Optimization, Position

Large graphs abound in machine learning, data mining, and several related
areas. A useful step towards analyzing such graphs is that of obtaining certain
summary statistics - e.g., or the expected length of a shortest path between
two nodes, or the expected weight of a minimum spanning tree of the graph, etc.
These statistics provide insight into the structure of a graph, and they can
help predict global properties of a graph. Motivated thus, we propose to study
statistical properties of structured subgraphs (of a given graph), in
particular, to estimate the expected objective function value of a
combinatorial optimization problem over these subgraphs. The general task is
very difficult, if not unsolvable; so for concreteness we describe a more
specific statistical estimation problem based on spanning trees. We hope that
our position paper encourages others to also study other types of graphical
structures for which one can prove nontrivial statistical estimates.
Adaptive nonparametric detection in cryo-electron microscopy
Authors: Mikhail Langovoy, Michael Habeck, Bernhard Schoelkopf
Date: 2013-11-29 00:00:00

Cryo-electron microscopy (cryo-EM) is an emerging experimental method to
characterize the structure of large biomolecular assemblies. Single particle
cryo-EM records 2D images (so-called micrographs) of projections of the
three-dimensional particle, which need to be processed to obtain the
three-dimensional reconstruction. A crucial step in the reconstruction process
is particle picking which involves detection of particles in noisy 2D
micrographs with low signal-to-noise ratios of typically 1:10 or even lower.
Typically, each picture contains a large number of particles, and particles
have unknown irregular and nonconvex shapes.
Viterbi training in PRISM
Authors: Taisuke Sato, Keiichi Kubota
Date: 2013-03-22 00:00:00

VT (Viterbi training), or hard EM, is an efficient way of parameter learning
for probabilistic models with hidden variables. Given an observation $y$, it
searches for a state of hidden variables $x$ that maximizes $p(x,y \mid
\theta)$ by coordinate ascent on parameters $\theta$ and $x$. In this paper we
introduce VT to PRISM, a logic-based probabilistic modeling system for
generative models. VT improves PRISM in three ways. First VT in PRISM converges
faster than EM in PRISM due to the VT's termination condition. Second,
parameters learned by VT often show good prediction performance compared to
those learned by EM. We conducted two parsing experiments with probabilistic
grammars while learning parameters by a variety of inference methods, i.e.\ VT,
EM, MAP and VB. The result is that VT achieved the best parsing accuracy among
them in both experiments. Also we conducted a similar experiment for
classification tasks where a hidden variable is not a prediction target unlike
probabilistic grammars. We found that in such a case VT does not necessarily
yield superior performance. Third since VT always deals with a single
probability of a single explanation, Viterbi explanation, the exclusiveness
condition that is imposed on PRISM programs is no more required if we learn
parameters by VT.
  Last but not least we can say that as VT in PRISM is general and applicable
to any PRISM program, it largely reduces the need for the user to develop a
specific VT algorithm for a specific model. Furthermore since VT in PRISM can
be used just by setting a PRISM flag appropriately, it makes VT easily
accessible to (probabilistic) logic programmers. To appear in Theory and
Practice of Logic Programming (TPLP).
Visualizing and Understanding Convolutional Networks
Authors: Matthew D. Zeiler, Rob Fergus
Date: 2013-11-12 00:00:00
Tasks: General Classification, Image Classification, Video Quality Assessment

Large Convolutional Network models have recently demonstrated impressive
classification performance on the ImageNet benchmark. However there is no clear
understanding of why they perform so well, or how they might be improved. In
this paper we address both issues. We introduce a novel visualization technique
that gives insight into the function of intermediate feature layers and the
operation of the classifier. We also perform an ablation study to discover the
performance contribution from different model layers. This enables us to find
model architectures that outperform Krizhevsky \etal on the ImageNet
classification benchmark. We show our ImageNet model generalizes well to other
datasets: when the softmax classifier is retrained, it convincingly beats the
current state-of-the-art results on Caltech-101 and Caltech-256 datasets.
Shape from Texture using Locally Scaled Point Processes
Authors: Eva-Maria Didden, Thordis Linda Thorarinsdottir, Alex Lenkoski, Christoph Schnörr
Date: 2013-11-28 00:00:00
Tasks: Point Processes, Shape from Texture

Shape from texture refers to the extraction of 3D information from 2D images
with irregular texture. This paper introduces a statistical framework to learn
shape from texture where convex texture elements in a 2D image are represented
through a point process. In a first step, the 2D image is preprocessed to
generate a probability map corresponding to an estimate of the unnormalized
intensity of the latent point process underlying the texture elements. The
latent point process is subsequently inferred from the probability map in a
non-parametric, model free manner. Finally, the 3D information is extracted
from the point pattern by applying a locally scaled point process model where
the local scaling function represents the deformation caused by the projection
of a 3D surface onto a 2D image.
Gaussian Probabilities and Expectation Propagation
Authors: John P. Cunningham, Philipp Hennig, Simon Lacoste-Julien
Date: 2011-11-29 00:00:00

While Gaussian probability densities are omnipresent in applied mathematics,
Gaussian cumulative probabilities are hard to calculate in any but the
univariate case. We study the utility of Expectation Propagation (EP) as an
approximate integration method for this problem. For rectangular integration
regions, the approximation is highly accurate. We also extend the derivations
to the more general case of polyhedral integration regions. However, we find
that in this polyhedral case, EP's answer, though often accurate, can be almost
arbitrarily wrong. We consider these unexpected results empirically and
theoretically, both for the problem of Gaussian probabilities and for EP more
generally. These results elucidate an interesting and non-obvious feature of EP
not yet studied in detail.
Unobtrusive Low Cost Pupil Size Measurements using Web cameras
Authors: Sergios Petridis, Theodoros Giannakopoulos, Costantine D. Spyropoulos
Date: 2013-11-28 00:00:00

Unobtrusive every day health monitoring can be of important use for the
elderly population. In particular, pupil size may be a valuable source of
information, since, apart from pathological cases, it can reveal the emotional
state, the fatigue and the ageing. To allow for unobtrusive monitoring to gain
acceptance, one should seek for efficient methods of monitoring using com- mon
low-cost hardware. This paper describes a method for monitoring pupil sizes
using a common web camera in real time. Our method works by first detecting the
face and the eyes area. Subsequently, optimal iris and sclera location and
radius, modelled as ellipses, are found using efficient filtering. Finally, the
pupil center and radius is estimated by optimal filtering within the area of
the iris. Experimental result show both the efficiency and the effectiveness of
our approach.
Glasgow's Stereo Image Database of Garments
Authors: Gerardo Aragon-Camarasa, Susanne B. Oehler, Yu-An Liu, Sun Li, Paul Cockshott, J. Paul Siebert
Date: 2013-11-28 00:00:00

To provide insight into cloth perception and manipulation with an active
binocular robotic vision system, we compiled a database of 80 stereo-pair
colour images with corresponding horizontal and vertical disparity maps and
mask annotations, for 3D garment point cloud rendering has been created and
released. The stereo-image garment database is part of research conducted under
the EU-FP7 Clothes Perception and Manipulation (CloPeMa) project and belongs to
a wider database collection released through CloPeMa (www.clopema.eu). This
database is based on 16 different off-the-shelve garments. Each garment has
been imaged in five different pose configurations on the project's binocular
robot head. A full copy of the database is made available for scientific
research only at https://sites.google.com/site/ugstereodatabase/.
Spatially-Adaptive Reconstruction in Computed Tomography using Neural Networks
Authors: Joseph Shtok, Michael Zibulevsky, Michael Elad
Date: 2013-11-28 00:00:00
Tasks: BIG-bench Machine Learning, Image Reconstruction

We propose a supervised machine learning approach for boosting existing
signal and image recovery methods and demonstrate its efficacy on example of
image reconstruction in computed tomography. Our technique is based on a local
nonlinear fusion of several image estimates, all obtained by applying a chosen
reconstruction algorithm with different values of its control parameters.
Usually such output images have different bias/variance trade-off. The fusion
of the images is performed by feed-forward neural network trained on a set of
known examples. Numerical experiments show an improvement in reconstruction
quality relatively to existing direct and iterative reconstruction methods.
Solving Minimum Vertex Cover Problem Using Learning Automata
Authors: Aylin Mousavian, Alireza Rezvanian, Mohammad Reza Meybodi
Date: 2013-11-28 00:00:00

Minimum vertex cover problem is an NP-Hard problem with the aim of finding
minimum number of vertices to cover graph. In this paper, a learning automaton
based algorithm is proposed to find minimum vertex cover in graph. In the
proposed algorithm, each vertex of graph is equipped with a learning automaton
that has two actions in the candidate or non-candidate of the corresponding
vertex cover set. Due to characteristics of learning automata, this algorithm
significantly reduces the number of covering vertices of graph. The proposed
algorithm based on learning automata iteratively minimize the candidate vertex
cover through the update its action probability. As the proposed algorithm
proceeds, a candidate solution nears to optimal solution of the minimum vertex
cover problem. In order to evaluate the proposed algorithm, several experiments
conducted on DIMACS dataset which compared to conventional methods.
Experimental results show the major superiority of the proposed algorithm over
the other methods.
Finding a Maximum Clique using Ant Colony Optimization and Particle Swarm Optimization in Social Networks
Authors: Mohammad Soleimani-Pouri, Alireza Rezvanian, Mohammad Reza Meybodi
Date: 2013-11-28 00:00:00

Interaction between users in online social networks plays a key role in
social network analysis. One on important types of social group is full
connected relation between some users, which known as clique structure.
Therefore finding a maximum clique is essential for some analysis. In this
paper, we proposed a new method using ant colony optimization algorithm and
particle swarm optimization algorithm. In the proposed method, in order to
attain better results, it is improved process of pheromone update by particle
swarm optimization. Simulation results on popular standard social network
benchmarks in comparison standard ant colony optimization algorithm are shown a
relative enhancement of proposed algorithm.