pwc-archive/papers-with-abstracts huggingface dataset:
Reasoning about Independence in Probabilistic Models of Relational Data
Authors: Marc Maier, Katerina Marazopoulou, David Jensen
Date: 2013-02-18 00:00:00

We extend the theory of d-separation to cases in which data instances are not
independent and identically distributed. We show that applying the rules of
d-separation directly to the structure of probabilistic models of relational
data inaccurately infers conditional independence. We introduce relational
d-separation, a theory for deriving conditional independence facts from
relational models. We provide a new representation, the abstract ground graph,
that enables a sound, complete, and computationally efficient method for
answering d-separation queries about relational models, and we present
empirical results that demonstrate effectiveness.
Effective Slot Filling Based on Shallow Distant Supervision Methods
Authors: Benjamin Roth, Tassilo Barth, Michael Wiegand, Mittul Singh, Dietrich Klakow
Date: 2014-01-06 00:00:00
Tasks: Relation, Relation Extraction, Relation Prediction, Retrieval, slot-filling, Slot Filling, Translation

Spoken Language Systems at Saarland University (LSV) participated this year
with 5 runs at the TAC KBP English slot filling track. Effective algorithms for
all parts of the pipeline, from document retrieval to relation prediction and
response post-processing, are bundled in a modular end-to-end relation
extraction system called RelationFactory. The main run solely focuses on
shallow techniques and achieved significant improvements over LSV's last year's
system, while using the same training data and patterns. Improvements mainly
have been obtained by a feature representation focusing on surface skip n-grams
and improved scoring for extracted distant supervision patterns. Important
factors for effective extraction are the training and tuning scheme for distant
supervision classifiers, and the query expansion by a translation model based
on Wikipedia links. In the TAC KBP 2013 English Slotfilling evaluation, the
submitted main run of the LSV RelationFactory system achieved the top-ranked
F1-score of 37.3%.
Exploration vs Exploitation vs Safety: Risk-averse Multi-Armed Bandits
Authors: Nicolas Galichet, Michèle Sebag, Olivier Teytaud
Date: 2014-01-06 00:00:00
Tasks: energy management, Management, Multi-Armed Bandits

Motivated by applications in energy management, this paper presents the
Multi-Armed Risk-Aware Bandit (MARAB) algorithm. With the goal of limiting the
exploration of risky arms, MARAB takes as arm quality its conditional value at
risk. When the user-supplied risk level goes to 0, the arm quality tends toward
the essential infimum of the arm distribution density, and MARAB tends toward
the MIN multi-armed bandit algorithm, aimed at the arm with maximal minimal
value. As a first contribution, this paper presents a theoretical analysis of
the MIN algorithm under mild assumptions, establishing its robustness
comparatively to UCB. The analysis is supported by extensive experimental
validation of MIN and MARAB compared to UCB and state-of-art risk-aware MAB
algorithms on artificial and real-world problems.
Speeding up SOR Solvers for Constraint-based GUIs with a Warm-Start Strategy
Authors: Noreen Jamil, Johannes Müller, Christof Lutteroth, Gerald Weber
Date: 2014-01-06 00:00:00

Many computer programs have graphical user interfaces (GUIs), which need good
layout to make efficient use of the available screen real estate. Most GUIs do
not have a fixed layout, but are resizable and able to adapt themselves.
Constraints are a powerful tool for specifying adaptable GUI layouts: they are
used to specify a layout in a general form, and a constraint solver is used to
find a satisfying concrete layout, e.g.\ for a specific GUI size. The
constraint solver has to calculate a new layout every time a GUI is resized or
changed, so it needs to be efficient to ensure a good user experience. One
approach for constraint solvers is based on the Gauss-Seidel algorithm and
successive over-relaxation (SOR).
  Our observation is that a solution after resizing or changing is similar in
structure to a previous solution. Thus, our hypothesis is that we can increase
the computational performance of an SOR-based constraint solver if we reuse the
solution of a previous layout to warm-start the solving of a new layout. In
this paper we report on experiments to test this hypothesis experimentally for
three common use cases: big-step resizing, small-step resizing and constraint
change. In our experiments, we measured the solving time for randomly generated
GUI layout specifications of various sizes. For all three cases we found that
the performance is improved if an existing solution is used as a starting
solution for a new layout.
Constraint Solvers for User Interface Layout
Authors: Noreen Jamil
Date: 2014-01-06 00:00:00

Constraints have played an important role in the construction of GUIs, where
they are mainly used to define the layout of the widgets. Resizing behavior is
very important in GUIs because areas have domain specific parameters such as
form the resizing of windows. If linear objective function is used and window
is resized then error is not distributed equally. To distribute the error
equally, a quadratic objective function is introduced. Different algorithms are
widely used for solving linear constraints and quadratic problems in a variety
of different scientific areas. The linear relxation, Kaczmarz, direct and
linear programming methods are common methods for solving linear constraints
for GUI layout. The interior point and active set methods are most commonly
used techniques to solve quadratic programming problems. Current constraint
solvers designed for GUI layout do not use interior point methods for solving a
quadratic objective function subject to linear equality and inequality
constraints. In this paper, performance aspects and the convergence speed of
interior point and active set methods are compared along with one most commonly
used linear programming method when they are implemented for graphical user
interface layout. The performance and convergence of the proposed algorithms
are evaluated empirically using randomly generated UI layout specifications of
various sizes. The results show that the interior point algorithms perform
significantly better than the Simplex method and QOCA-solver, which uses the
active set method implementation for solving quadratic optimization.
Solver Scheduling via Answer Set Programming
Authors: Holger Hoos, Roland Kaminski, Marius Lindauer, Torsten Schaub
Date: 2014-01-06 00:00:00
Tasks: Benchmarking, Scheduling

Although Boolean Constraint Technology has made tremendous progress over the
last decade, the efficacy of state-of-the-art solvers is known to vary
considerably across different types of problem instances and is known to depend
strongly on algorithm parameters. This problem was addressed by means of a
simple, yet effective approach using handmade, uniform and unordered schedules
of multiple solvers in ppfolio, which showed very impressive performance in the
2011 SAT Competition. Inspired by this, we take advantage of the modeling and
solving capacities of Answer Set Programming (ASP) to automatically determine
more refined, that is, non-uniform and ordered solver schedules from existing
benchmarking data. We begin by formulating the determination of such schedules
as multi-criteria optimization problems and provide corresponding ASP
encodings. The resulting encodings are easily customizable for different
settings and the computation of optimum schedules can mostly be done in the
blink of an eye, even when dealing with large runtime data sets stemming from
many solvers on hundreds to thousands of instances. Also, the fact that our
approach can be customized easily enabled us to swiftly adapt it to generate
parallel schedules for multi-processor machines.
Structure estimation for discrete graphical models: Generalized covariance matrices and their inverses
Authors: Po-Ling Loh, Martin J. Wainwright
Date: 2012-12-03 00:00:00
Proceeding: NeurIPS 2012
Tasks: Open-Ended Question Answering

We investigate the relationship between the structure of a discrete graphical
model and the support of the inverse of a generalized covariance matrix. We
show that for certain graph structures, the support of the inverse covariance
matrix of indicator variables on the vertices of a graph reflects the
conditional independence structure of the graph. Our work extends results that
have previously been established only in the context of multivariate Gaussian
graphical models, thereby addressing an open question about the significance of
the inverse covariance matrix of a non-Gaussian distribution. The proof
exploits a combination of ideas from the geometry of exponential families,
junction tree theory and convex analysis. These population-level results have
various consequences for graph selection methods, both known and novel,
including a novel method for structure estimation for missing or corrupted
observations. We provide nonasymptotic guarantees for such methods and
illustrate the sharpness of these predictions via simulations.
Differentially Private Data Releasing for Smooth Queries with Synthetic Database Output
Authors: Chi Jin, Ziteng Wang, Junliang Huang, Yiqiao Zhong, Li-Wei Wang
Date: 2014-01-06 00:00:00

We consider accurately answering smooth queries while preserving differential
privacy. A query is said to be $K$-smooth if it is specified by a function
defined on $[-1,1]^d$ whose partial derivatives up to order $K$ are all
bounded. We develop an $\epsilon$-differentially private mechanism for the
class of $K$-smooth queries. The major advantage of the algorithm is that it
outputs a synthetic database. In real applications, a synthetic database output
is appealing. Our mechanism achieves an accuracy of $O
(n^{-\frac{K}{2d+K}}/\epsilon )$, and runs in polynomial time. We also
generalize the mechanism to preserve $(\epsilon, \delta)$-differential privacy
with slightly improved accuracy. Extensive experiments on benchmark datasets
demonstrate that the mechanisms have good accuracy and are efficient.
LB2CO: A Semantic Ontology Framework for B2C eCommerce Transaction on the Internet
Authors: Adeyinka K. Akanbi
Date: 2014-01-05 00:00:00

Business ontology can enhance the successful development of complex
enterprise system; this is being achieved through knowledge sharing and the
ease of communication between every entity in the domain. Through human
semantic interaction with the web resources, machines to interpret the data
published in a machine interpretable form under web. However, the theoretical
practice of business ontology in eCommerce domain is quite a few especially in
the section of electronic transaction, and the various techniques used to
obtain efficient communication across spheres are error prone and are not
always guaranteed to be efficient in obtaining desired result due to poor
semantic integration between entities. To overcome the poor semantic
integration this research focuses on proposed ontology called LB2CO, which
combines the framework of IDEF5 & SNAP as an analysis tool, for automated
recommendation of product and services and create effective ontological
framework for B2C transaction & communication across different business domains
that facilitates the interoperability & integration of B2C transactions over
the web.
Feature Selection Using Classifier in High Dimensional Data
Authors: Vijendra Singh, Shivani Pathak
Date: 2014-01-05 00:00:00
Tasks: feature selection, General Classification, Vocal Bursts Intensity Prediction

Feature selection is frequently used as a pre-processing step to machine
learning. It is a process of choosing a subset of original features so that the
feature space is optimally reduced according to a certain evaluation criterion.
The central objective of this paper is to reduce the dimension of the data by
finding a small set of important features which can give good classification
performance. We have applied filter and wrapper approach with different
classifiers QDA and LDA respectively. A widely-used filter method is used for
bioinformatics data i.e. a univariate criterion separately on each feature,
assuming that there is no interaction between features and then applied
Sequential Feature Selection method. Experimental results show that filter
approach gives better performance in respect of Misclassification Error Rate.
Learning parametric dictionaries for graph signals
Authors: Dorina Thanou, David I Shuman, Pascal Frossard
Date: 2014-01-05 00:00:00
Tasks: Denoising, Dictionary Learning

In sparse signal representation, the choice of a dictionary often involves a
tradeoff between two desirable properties -- the ability to adapt to specific
signal data and a fast implementation of the dictionary. To sparsely represent
signals residing on weighted graphs, an additional design challenge is to
incorporate the intrinsic geometric structure of the irregular data domain into
the atoms of the dictionary. In this work, we propose a parametric dictionary
learning algorithm to design data-adapted, structured dictionaries that
sparsely represent graph signals. In particular, we model graph signals as
combinations of overlapping local patterns. We impose the constraint that each
dictionary is a concatenation of subdictionaries, with each subdictionary being
a polynomial of the graph Laplacian matrix, representing a single pattern
translated to different areas of the graph. The learning algorithm adapts the
patterns to a training set of graph signals. Experimental results on both
synthetic and real datasets demonstrate that the dictionaries learned by the
proposed algorithm are competitive with and often better than unstructured
dictionaries learned by state-of-the-art numerical learning algorithms in terms
of sparse approximation of graph signals. In contrast to the unstructured
dictionaries, however, the dictionaries learned by the proposed algorithm
feature localized atoms and can be implemented in a computationally efficient
manner in signal processing tasks such as compression, denoising, and
classification.
Spectrum Hole Prediction Based On Historical Data: A Neural Network Approach
Authors: Barau Gafai Najashi, Feng Wenjiang, Mohammed Dikko Almustapha
Date: 2014-01-05 00:00:00
Tasks: Management

The concept of cognitive radio pioneered by Mitola promises to change the
future of wireless communication especially in the area of spectrum management.
Currently, the command and control strategy employed in spectrum assignment is
too rigid and needs to be reviewed. Recent studies have shown that assigned
spectrum is underutilized spectrally and temporally. Cognitive radio provides a
viable solution whereby licensed users can share the spectrum with unlicensed
users opportunistically without causing interference. Unlicensed users must be
able to sense weather the channel is busy or idle, failure to do so will lead
to interference to the licensed user. In this paper, a neural network based
prediction model for predicting the channel status using historical data
obtained during a spectrum occupancy measurement is presented. Genetic
algorithm is combined with LM BP for increasing the probability of obtaining
the best weights thus optimizing the network. The results obtained indicate
high prediction accuracy over all bands considered
Stylistic Clusters and the Syrian/South Syrian Tradition of First-Millennium BCE Levantine Ivory Carving: A Machine Learning Approach
Authors: Amy Rebecca Gansell, Jan-Willem van de Meent, Sakellarios Zairis, Chris H. Wiggins
Date: 2014-01-05 00:00:00
Tasks: BIG-bench Machine Learning, Descriptive, Diagnostic, General Classification

Thousands of first-millennium BCE ivory carvings have been excavated from
Neo-Assyrian sites in Mesopotamia (primarily Nimrud, Khorsabad, and Arslan
Tash) hundreds of miles from their Levantine production contexts. At present,
their specific manufacture dates and workshop localities are unknown. Relying
on subjective, visual methods, scholars have grappled with their classification
and regional attribution for over a century. This study combines visual
approaches with machine-learning techniques to offer data-driven perspectives
on the classification and attribution of this early Iron Age corpus. The study
sample consisted of 162 sculptures of female figures. We have developed an
algorithm that clusters the ivories based on a combination of descriptive and
anthropometric data. The resulting categories, which are based on purely
statistical criteria, show good agreement with conventional art historical
classifications, while revealing new perspectives, especially with regard to
the contested Syrian/South Syrian/Intermediate tradition. Specifically, we have
identified that objects of the Syrian/South Syrian/Intermediate tradition may
be more closely related to Phoenician objects than to North Syrian objects; we
offer a reconsideration of a subset of Phoenician objects, and we confirm
Syrian/South Syrian/Intermediate stylistic subgroups that might distinguish
networks of acquisition among the sites of Nimrud, Khorsabad, Arslan Tash and
the Levant. We have also identified which features are most significant in our
cluster assignments and might thereby be most diagnostic of regional carving
traditions. In short, our study both corroborates traditional visual
classification methods and demonstrates how machine-learning techniques may be
employed to reveal complementary information not accessible through the
exclusively visual analysis of an archaeological corpus.
Pectoral Muscles Suppression in Digital Mammograms using Hybridization of Soft Computing Methods
Authors: I. Laurence Aroquiaraj, K. Thangavel
Date: 2014-01-05 00:00:00
Tasks: Segmentation

Breast region segmentation is an essential prerequisite in computerized
analysis of mammograms. It aims at separating the breast tissue from the
background of the mammogram and it includes two independent segmentations. The
first segments the background region which usually contains annotations, labels
and frames from the whole breast region, while the second removes the pectoral
muscle portion (present in Medio Lateral Oblique (MLO) views) from the rest of
the breast tissue. In this paper we propose hybridization of Connected
Component Labeling (CCL), Fuzzy, and Straight line methods. Our proposed
methods worked good for separating pectoral region. After removal pectoral
muscle from the mammogram, further processing is confined to the breast region
alone. To demonstrate the validity of our segmentation algorithm, it is
extensively tested using over 322 mammographic images from the Mammographic
Image Analysis Society (MIAS) database. The segmentation results were evaluated
using a Mean Absolute Error (MAE), Hausdroff Distance (HD), Probabilistic Rand
Index (PRI), Local Consistency Error (LCE) and Tanimoto Coefficient (TC). The
hybridization of fuzzy with straight line method is given more than 96% of the
curve segmentations to be adequate or better. In addition a comparison with
similar approaches from the state of the art has been given, obtaining slightly
improved results. Experimental results demonstrate the effectiveness of the
proposed approach.
An Empirical Evaluation of Portfolios Approaches for solving CSPs
Authors: Roberto Amadini, Maurizio Gabbrielli, Jacopo Mauro
Date: 2012-12-04 00:00:00
Tasks: General Classification

Recent research in areas such as SAT solving and Integer Linear Programming
has shown that the performances of a single arbitrarily efficient solver can be
significantly outperformed by a portfolio of possibly slower on-average
solvers. We report an empirical evaluation and comparison of portfolio
approaches applied to Constraint Satisfaction Problems (CSPs). We compared
models developed on top of off-the-shelf machine learning algorithms with
respect to approaches used in the SAT field and adapted for CSPs, considering
different portfolio sizes and using as evaluation metrics the number of solved
problems and the time taken to solve them. Results indicate that the best SAT
approaches have top performances also in the CSP field and are slightly more
competitive than simple models built on top of classification algorithms.
Multimodal Optimization by Sparkling Squid Populations
Authors: Videh Seksaria
Date: 2014-01-05 00:00:00
Tasks: Image Registration

The swarm intelligence of animals is a natural paradigm to apply to
optimization problems. Ant colony, bee colony, firefly and bat algorithms are
amongst those that have been demonstrated to efficiently to optimize complex
constraints. This paper proposes the new Sparkling Squid Algorithm (SSA) for
multimodal optimization, inspired by the intelligent swarm behavior of its
namesake. After an introduction, formulation and discussion of its
implementation, it will be compared to other popular metaheuristics. Finally,
applications to well - known problems such as image registration and the
traveling salesperson problem will be discussed.
Least Squares Policy Iteration with Instrumental Variables vs. Direct Policy Search: Comparison Against Optimal Benchmarks Using Energy Storage
Authors: Warren R. Scott, Warren B. Powell, Somayeh Moazehi
Date: 2014-01-04 00:00:00

This paper studies approximate policy iteration (API) methods which use
least-squares Bellman error minimization for policy evaluation. We address
several of its enhancements, namely, Bellman error minimization using
instrumental variables, least-squares projected Bellman error minimization, and
projected Bellman error minimization using instrumental variables. We prove
that for a general discrete-time stochastic control problem, Bellman error
minimization using instrumental variables is equivalent to both variants of
projected Bellman error minimization. An alternative to these API methods is
direct policy search based on knowledge gradient. The practical performance of
these three approximate dynamic programming methods are then investigated in
the context of an application in energy storage, integrated with an
intermittent wind energy supply to fully serve a stochastic time-varying
electricity demand. We create a library of test problems using real-world data
and apply value iteration to find their optimal policies. These benchmarks are
then used to compare the developed policies. Our analysis indicates that API
with instrumental variables Bellman error minimization prominently outperforms
API with least-squares Bellman error minimization. However, these approaches
underperform our direct policy search implementation.
A stochastic model for Case-Based Reasoning
Authors: Michael Gr. Voskoglou
Date: 2014-01-04 00:00:00
Tasks: model

Case-Bsed Reasoning (CBR) is a recent theory for problem-solving and learning
in computers and people.Broadly construed it is the process of solving new
problems based on the solution of similar past problems. In the present paper
we introduce an absorbing Markov chain on the main steps of the CBR process.In
this way we succeed in obtaining the probabilities for the above process to be
in a certain step at a certain phase of the solution of the corresponding
problem, and a measure for the efficiency of a CBR system. Examples are given
to illustrate our results.