pwc-archive/papers-with-abstracts huggingface dataset:
Parallel architectures for fuzzy triadic similarity learning
Authors: Sonia Alouane-Ksouri, Minyar Sassi-Hidri, Kamel Barkaoui
Date: 2013-12-21 00:00:00
Tasks: Clustering

In a context of document co-clustering, we define a new similarity measure
which iteratively computes similarity while combining fuzzy sets in a
three-partite graph. The fuzzy triadic similarity (FT-Sim) model can deal with
uncertainty offers by the fuzzy sets. Moreover, with the development of the Web
and the high availability of storage spaces, more and more documents become
accessible. Documents can be provided from multiple sites and make similarity
computation an expensive processing. This problem motivated us to use parallel
computing. In this paper, we introduce parallel architectures which are able to
treat large and multi-source data sets by a sequential, a merging or a
splitting-based process. Then, we proceed to a local and a central (or global)
computing using the basic FT-Sim measure. The idea behind these architectures
is to reduce both time and space complexities thanks to parallel computation.
Representing, Simulating and Analysing Ho Chi Minh City Tsunami Plan by Means of Process Models
Authors: Le Nguyen Tuan Thanh, Chihab Hanachi, Serge Stinckwich, Ho Tuong Vinh
Date: 2013-12-17 00:00:00
Tasks: Management

This paper considers the textual plan (guidelines) proposed by People's
Committee of Ho Chi Minh City (Vietnam) to manage earthquake and tsunami, and
try to represent it in a more formal way, in order to provide means to
simulate, analyse and adapt it. We first present a state of the art about
coordination models for disaster management with a focus on process oriented
approaches. We give an overview of the different dimensions of the textual
tsunami plan of Ho Chi Minh City and then the graphical representation of its
process with BPMN (Business Process Model and Notation). We finally show how to
exploit this process with workflow tools to simulate (YAWL tool) and analyse it
(ProM tool).
G-invariant Persistent Homology
Authors: Patrizio Frosini
Date: 2012-12-04 00:00:00

Classical persistent homology is a powerful mathematical tool for shape
comparison. Unfortunately, it is not tailored to study the action of
transformation groups that are different from the group Homeo(X) of all
self-homeomorphisms of a topological space X. This fact restricts its use in
applications. In order to obtain better lower bounds for the natural
pseudo-distance d_G associated with a subgroup G of Homeo(X), we need to adapt
persistent homology and consider G-invariant persistent homology. Roughly
speaking, the main idea consists in defining persistent homology by means of a
set of chains that is invariant under the action of G. In this paper we
formalize this idea, and prove the stability of the persistent Betti number
functions in G-invariant persistent homology with respect to the natural
pseudo-distance d_G. We also show how G-invariant persistent homology could be
used in applications concerning shape comparison, when the invariance group is
a proper subgroup of the group of all self-homeomorphisms of a topological
space. In this paper we will assume that the space X is triangulable, in order
to guarantee that the persistent Betti number functions are finite without
using any tameness assumption.
Extracting Region of Interest for Palm Print Authentication
Authors: Kasturika B. Ray
Date: 2013-12-21 00:00:00

Biometrics authentication is an effective method for automatically
recognizing individuals. The authentication consists of an enrollment phase and
an identification or verification phase. In the stages of enrollment known
(training) samples after the pre-processing stage are used for suitable feature
extraction to generate the template database. In the verification stage, the
test sample is similarly pre processed and subjected to feature extraction
modules, and then it is matched with the training feature templates to decide
whether it is a genuine or not. This paper presents use of a region of interest
(ROI) for palm print technology. First some of the existing methods for palm
print identification have been introduced. Then focus has been given on
extraction of a suitable smaller region from the acquired palm print to improve
the identification method accuracy. Several existing work in the topic of
region extraction have been examined. Subsequently, a simple and original
method has then proposed for locating the ROI that can be effectively used for
palm print analysis. The ROI extracted using this new technique is suitable for
different types of processing as it creates a rectangular or square area around
the center of activity represented by the lines, wrinkles and ridges of the
palm print. The effectiveness of the ROI approach has been tested by
integrating it with a texture based identification / authentication system
proposed earlier. The improvement has been shown by comparing the
identification accuracy rate before and after the ROI pre-processing.
Total variation with overlapping group sparsity for image deblurring under impulse noise
Authors: Gang Liu, Ting-Zhu Huang, Jun Liu, Xiao-Guang Lv
Date: 2013-12-21 00:00:00
Tasks: Deblurring, Image Deblurring

The total variation (TV) regularization method is an effective method for
image deblurring in preserving edges. However, the TV based solutions usually
have some staircase effects. In this paper, in order to alleviate the staircase
effect, we propose a new model for restoring blurred images with impulse noise.
The model consists of an $\ell_1$-fidelity term and a TV with overlapping group
sparsity (OGS) regularization term. Moreover, we impose a box constraint to the
proposed model for getting more accurate solutions. An efficient and effective
algorithm is proposed to solve the model under the framework of the alternating
direction method of multipliers (ADMM). We use an inner loop which is nested
inside the majorization minimization (MM) iteration for the subproblem of the
proposed method. Compared with other methods, numerical results illustrate that
the proposed method, can significantly improve the restoration quality, both in
avoiding staircase effects and in terms of peak signal-to-noise ratio (PSNR)
and relative error (ReE).
GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network Training
Authors: Thomas Paine, Hailin Jin, Jianchao Yang, Zhe Lin, Thomas Huang
Date: 2013-12-21 00:00:00
Tasks: GPU

The ability to train large-scale neural networks has resulted in
state-of-the-art performance in many areas of computer vision. These results
have largely come from computational break throughs of two forms: model
parallelism, e.g. GPU accelerated training, which has seen quick adoption in
computer vision circles, and data parallelism, e.g. A-SGD, whose large scale
has been used mostly in industry. We report early experiments with a system
that makes use of both model parallelism and data parallelism, we call GPU
A-SGD. We show using GPU A-SGD it is possible to speed up training of large
convolutional neural networks useful for computer vision. We believe GPU A-SGD
will make it possible to train larger networks on larger training sets in a
reasonable amount of time.
Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data
Authors: Yalong Bai, Kuiyuan Yang, Wei Yu, Wei-Ying Ma, Tiejun Zhao
Date: 2013-12-17 00:00:00
Tasks: Image Retrieval, Retrieval

Image retrieval refers to finding relevant images from an image database for
a query, which is considered difficult for the gap between low-level
representation of images and high-level representation of queries. Recently
further developed Deep Neural Network sheds light on automatically learning
high-level image representation from raw pixels. In this paper, we proposed a
multi-task DNN learned for image retrieval, which contains two parts, i.e.,
query-sharing layers for image representation computation and query-specific
layers for relevance estimation. The weights of multi-task DNN are learned on
clickthrough data by Ring Training. Experimental results on both simulated and
real dataset show the effectiveness of the proposed method.
Large-Scale Paralleled Sparse Principal Component Analysis
Authors: W. Liu, H. Zhang, D. Tao, Y. Wang, K. Lu
Date: 2013-12-21 00:00:00
Tasks: CPU, GPU

Principal component analysis (PCA) is a statistical technique commonly used
in multivariate data analysis. However, PCA can be difficult to interpret and
explain since the principal components (PCs) are linear combinations of the
original variables. Sparse PCA (SPCA) aims to balance statistical fidelity and
interpretability by approximating sparse PCs whose projections capture the
maximal variance of original data. In this paper we present an efficient and
paralleled method of SPCA using graphics processing units (GPUs), which can
process large blocks of data in parallel. Specifically, we construct parallel
implementations of the four optimization formulations of the generalized power
method of SPCA (GP-SPCA), one of the most efficient and effective SPCA
approaches, on a GPU. The parallel GPU implementation of GP-SPCA (using CUBLAS)
is up to eleven times faster than the corresponding CPU implementation (using
CBLAS), and up to 107 times faster than a MatLab implementation. Extensive
comparative experiments in several real-world datasets confirm that SPCA offers
a practical advantage.
Manifold regularized kernel logistic regression for web image annotation
Authors: W. Liu, H. Liu, D. Tao, Y. Wang, K. Lu
Date: 2013-12-21 00:00:00
Tasks: Binary Classification, regression

With the rapid advance of Internet technology and smart devices, users often
need to manage large amounts of multimedia information using smart devices,
such as personal image and video accessing and browsing. These requirements
heavily rely on the success of image (video) annotation, and thus large scale
image annotation through innovative machine learning methods has attracted
intensive attention in recent years. One representative work is support vector
machine (SVM). Although it works well in binary classification, SVM has a
non-smooth loss function and can not naturally cover multi-class case. In this
paper, we propose manifold regularized kernel logistic regression (KLR) for web
image annotation. Compared to SVM, KLR has the following advantages: (1) the
KLR has a smooth loss function; (2) the KLR produces an explicit estimate of
the probability instead of class label; and (3) the KLR can naturally be
generalized to the multi-class case. We carefully conduct experiments on MIR
FLICKR dataset and demonstrate the effectiveness of manifold regularized kernel
logistic regression for image annotation.
Learned versus Hand-Designed Feature Representations for 3d Agglomeration
Authors: John A. Bogovic, Gary B. Huang, Viren Jain
Date: 2013-12-20 00:00:00
Tasks: Data Augmentation, Retrieval

For image recognition and labeling tasks, recent results suggest that machine
learning methods that rely on manually specified feature representations may be
outperformed by methods that automatically derive feature representations based
on the data. Yet for problems that involve analysis of 3d objects, such as mesh
segmentation, shape retrieval, or neuron fragment agglomeration, there remains
a strong reliance on hand-designed feature descriptors. In this paper, we
evaluate a large set of hand-designed 3d feature descriptors alongside features
learned from the raw data using both end-to-end and unsupervised learning
techniques, in the context of agglomeration of 3d neuron fragments. By
combining unsupervised learning techniques with a novel dynamic pooling scheme,
we show how pure learning-based methods are for the first time competitive with
hand-designed 3d shape descriptors. We investigate data augmentation strategies
for dramatically increasing the size of the training set, and show how
combining both learned and hand-designed features leads to the highest
accuracy.
Negation in the Head of CP-logic Rules
Authors: Joost Vennekens
Date: 2013-12-20 00:00:00
Tasks: Negation

CP-logic is a probabilistic extension of the logic FO(ID). Unlike ASP, both
of these logics adhere to a Tarskian informal semantics, in which
interpretations represent objective states-of-affairs. In other words, these
logics lack the epistemic component of ASP, in which interpretations represent
the beliefs or knowledge of a rational agent. Consequently, neither CP-logic
nor FO(ID) have the need for two kinds of negations: there is only one
negation, and its meaning is that of objective falsehood. Nevertheless, the
formal semantics of this objective negation is mathematically more similar to
ASP's negation-as-failure than to its classical negation. The reason is that
both CP-logic and FO(ID) have a constructive semantics in which all atoms start
out as false, and may only become true as the result of a rule application.
This paper investigates the possibility of adding the well-known ASP feature of
allowing negation in the head of rules to CP-logic. Because CP-logic only has
one kind of negation, it is of necessity this ''negation-as-failure like''
negation that will be allowed in the head. We investigate the intuitive meaning
of such a construct and the benefits that arise from it.
Abstract Modular Systems and Solvers
Authors: Yuliya Lierler, Miroslaw Truszczynski
Date: 2013-12-20 00:00:00

Integrating diverse formalisms into modular knowledge representation systems
offers increased expressivity, modeling convenience and computational benefits.
We introduce concepts of abstract modules and abstract modular systems to study
general principles behind the design and analysis of model-finding programs, or
solvers, for integrated heterogeneous multi-logic systems. We show how abstract
modules and abstract modular systems give rise to transition systems, which are
a natural and convenient representation of solvers pioneered by the SAT
community. We illustrate our approach by showing how it applies to answer set
programming and propositional logic, and to multi-logic systems based on these
two formalisms.
On the Semantics of Gringo
Authors: Amelia Harrison, Vladimir Lifschitz, Fangkai Yang
Date: 2013-12-20 00:00:00

Input languages of answer set solvers are based on the mathematically simple
concept of a stable model. But many useful constructs available in these
languages, including local variables, conditional literals, and aggregates,
cannot be easily explained in terms of stable models in the sense of the
original definition of this concept and its straightforward generalizations.
Manuals written by designers of answer set solvers usually explain such
constructs using examples and informal comments that appeal to the user's
intuition, without references to any precise semantics. We propose to approach
the problem of defining the semantics of gringo programs by translating them
into the language of infinitary propositional formulas. This semantics allows
us to study equivalent transformations of gringo programs using natural
deduction in infinitary propositional logic.
Generating Shortest Synchronizing Sequences using Answer Set Programming
Authors: Canan Güniçen, Esra Erdem, Hüsnü Yenigün
Date: 2013-12-20 00:00:00

For a finite state automaton, a synchronizing sequence is an input sequence
that takes all the states to the same state. Checking the existence of a
synchronizing sequence and finding a synchronizing sequence, if one exists, can
be performed in polynomial time. However, the problem of finding a shortest
synchronizing sequence is known to be NP-hard. In this work, the usefulness of
Answer Set Programming to solve this optimization problem is investigated, in
comparison with brute-force algorithms and SAT-based approaches.
  Keywords: finite automata, shortest synchronizing sequence, ASP
A System for Interactive Query Answering with Answer Set Programming
Authors: Martin Gebser, Philipp Obermeier, Torsten Schaub
Date: 2013-12-20 00:00:00

Reactive answer set programming has paved the way for incorporating online
information into operative solving processes. Although this technology was
originally devised for dealing with data streams in dynamic environments, like
assisted living and cognitive robotics, it can likewise be used to incorporate
facts, rules, or queries provided by a user. As a result, we present the design
and implementation of a system for interactive query answering with reactive
answer set programming. Our system quontroller is based on the reactive solver
oclingo and implemented as a dedicated front-end. We describe its functionality
and implementation, and we illustrate its features by some selected use cases.
The DIAMOND System for Argumentation: Preliminary Report
Authors: Stefan Ellmauthaler, Hannes Strass
Date: 2013-12-20 00:00:00
Tasks: Abstract Argumentation

Abstract dialectical frameworks (ADFs) are a powerful generalisation of
Dung's abstract argumentation frameworks. In this paper we present an answer
set programming based software system, called DIAMOND (DIAlectical MOdels
eNcoDing). It translates ADFs into answer set programs whose stable models
correspond to models of the ADF with respect to several semantics (i.e.
admissible, complete, stable, grounded).
Query Answering in Object Oriented Knowledge Bases in Logic Programming: Description and Challenge for ASP
Authors: Vinay K. Chaudhri, Stijn Heymans, Michael Wessel, Tran Cao Son
Date: 2013-12-20 00:00:00

Research on developing efficient and scalable ASP solvers can substantially
benefit by the availability of data sets to experiment with. KB_Bio_101
contains knowledge from a biology textbook, has been developed as part of
Project Halo, and has recently become available for research use. KB_Bio_101 is
one of the largest KBs available in ASP and the reasoning with it is
undecidable in general. We give a description of this KB and ASP programs for a
suite of queries that have been of practical interest. We explain why these
queries pose significant practical challenges for the current ASP solvers.
An Algebra of Causal Chains
Authors: Pedro Cabalar, Jorge Fandinno
Date: 2013-12-20 00:00:00

In this work we propose a multi-valued extension of logic programs under the
stable models semantics where each true atom in a model is associated with a
set of justifications, in a similar spirit than a set of proof trees. The main
contribution of this paper is that we capture justifications into an algebra of
truth values with three internal operations: an addition '+' representing
alternative justifications for a formula, a commutative product '*'
representing joint interaction of causes and a non-commutative product '.'
acting as a concatenation or proof constructor. Using this multi-valued
semantics, we obtain a one-to-one correspondence between the syntactic proof
tree of a standard (non-causal) logic program and the interpretation of each
true atom in a model. Furthermore, thanks to this algebraic characterization we
can detect semantic properties like redundancy and relevance of the obtained
justifications. We also identify a lattice-based characterization of this
algebra, defining a direct consequences operator, proving its continuity and
that its least fix point can be computed after a finite number of iterations.
Finally, we define the concept of causal stable model by introducing an
analogous transformation to Gelfond and Lifschitz's program reduct.
A Functional View of Strong Negation in Answer Set Programming
Authors: Michael Bartholomew, Joohyung Lee
Date: 2013-12-20 00:00:00
Tasks: Negation

The distinction between strong negation and default negation has been useful
in answer set programming. We present an alternative account of strong
negation, which lets us view strong negation in terms of the functional stable
model semantics by Bartholomew and Lee. More specifically, we show that, under
complete interpretations, minimizing both positive and negative literals in the
traditional answer set semantics is essentially the same as ensuring the
uniqueness of Boolean function values under the functional stable model
semantics. The same account lets us view Lifschitz's two-valued logic programs
as a special case of the functional stable model semantics. In addition, we
show how non-Boolean intensional functions can be eliminated in favor of
Boolean intensional functions, and furthermore can be represented using strong
negation, which provides a way to compute the functional stable model semantics
using existing ASP solvers. We also note that similar results hold with the
functional stable model semantics by Cabalar.
Aspartame: Solving Constraint Satisfaction Problems with Answer Set Programming
Authors: Mutsunori Banbara, Martin Gebser, Katsumi Inoue, Torsten Schaub, Takehide Soh, Naoyuki Tamura, Matthias Weise
Date: 2013-12-20 00:00:00

Encoding finite linear CSPs as Boolean formulas and solving them by using
modern SAT solvers has proven to be highly effective, as exemplified by the
award-winning sugar system. We here develop an alternative approach based on
ASP. This allows us to use first-order encodings providing us with a high
degree of flexibility for easy experimentation with different implementations.
The resulting system aspartame re-uses parts of sugar for parsing and
normalizing CSPs. The obtained set of facts is then combined with an ASP
encoding that can be grounded and solved by off-the-shelf ASP systems. We
establish the competitiveness of our approach by empirically contrasting
aspartame and sugar.
Hybrid Automated Reasoning Tools: from Black-box to Clear-box Integration
Authors: Marcello Balduccini, Yulia Lierler
Date: 2013-12-20 00:00:00
Tasks: Scheduling

Recently, researchers in answer set programming and constraint programming
spent significant efforts in the development of hybrid languages and solving
algorithms combining the strengths of these traditionally separate fields.
These efforts resulted in a new research area: constraint answer set
programming (CASP). CASP languages and systems proved to be largely successful
at providing efficient solutions to problems involving hybrid reasoning tasks,
such as scheduling problems with elements of planning. Yet, the development of
CASP systems is difficult, requiring non-trivial expertise in multiple areas.
This suggests a need for a study identifying general development principles of
hybrid systems. Once these principles and their implications are well
understood, the development of hybrid languages and systems may become a
well-established and well-understood routine process. As a step in this
direction, in this paper we conduct a case study aimed at evaluating various
integration schemas of CASP methods.
Properties of Answer Set Programming with Convex Generalized Atoms
Authors: Mario Alviano, Wolfgang Faber
Date: 2013-12-20 00:00:00

In recent years, Answer Set Programming (ASP), logic programming under the
stable model or answer set semantics, has seen several extensions by
generalizing the notion of an atom in these programs: be it aggregate atoms,
HEX atoms, generalized quantifiers, or abstract constraints, the idea is to
have more complicated satisfaction patterns in the lattice of Herbrand
interpretations than traditional, simple atoms. In this paper we refer to any
of these constructs as generalized atoms. Several semantics with differing
characteristics have been proposed for these extensions, rendering the big
picture somewhat blurry. In this paper, we analyze the class of programs that
have convex generalized atoms (originally proposed by Liu and Truszczynski in
[10]) in rule bodies and show that for this class many of the proposed
semantics coincide. This is an interesting result, since recently it has been
shown that this class is the precise complexity boundary for the FLP semantics.
We investigate whether similar results also hold for other semantics, and
discuss the implications of our findings.