pwc-archive/papers-with-abstracts huggingface dataset:
Duality in Graphical Models
Authors: Dhafer Malouche, Bala Rajaratnam, Benjamin T. Rolfs
Date: 2013-10-09 00:00:00

Graphical models have proven to be powerful tools for representing
high-dimensional systems of random variables. One example of such a model is
the undirected graph, in which lack of an edge represents conditional
independence between two random variables given the rest. Another example is
the bidirected graph, in which absence of edges encodes pairwise marginal
independence. Both of these classes of graphical models have been extensively
studied, and while they are considered to be dual to one another, except in a
few instances this duality has not been thoroughly investigated. In this paper,
we demonstrate how duality between undirected and bidirected models can be used
to transport results for one class of graphical models to the dual model in a
transparent manner. We proceed to apply this technique to extend previously
existing results as well as to prove new ones, in three important domains.
First, we discuss the pairwise and global Markov properties for undirected and
bidirected models, using the pseudographoid and reverse-pseudographoid rules
which are weaker conditions than the typically used intersection and
composition rules. Second, we investigate these pseudographoid and reverse
pseudographoid rules in the context of probability distributions, using the
concept of duality in the process. Duality allows us to quickly relate them to
the more familiar intersection and composition properties. Third and finally,
we apply the dualization method to understand the implications of faithfulness,
which in turn leads to a more general form of an existing result.
Confidence-constrained joint sparsity recovery under the Poisson noise model
Authors: E. Chunikhina, R. Raich, T. Nguyen
Date: 2013-09-04 00:00:00

Our work is focused on the joint sparsity recovery problem where the common
sparsity pattern is corrupted by Poisson noise. We formulate the
confidence-constrained optimization problem in both least squares (LS) and
maximum likelihood (ML) frameworks and study the conditions for perfect
reconstruction of the original row sparsity and row sparsity pattern. However,
the confidence-constrained optimization problem is non-convex. Using convex
relaxation, an alternative convex reformulation of the problem is proposed. We
evaluate the performance of the proposed approach using simulation results on
synthetic data and show the effectiveness of proposed row sparsity and row
sparsity pattern recovery framework.
Understanding Boltzmann Machine and Deep Learning via A Confident Information First Principle
Authors: Xiaozhao Zhao, Yuexian Hou, Qian Yu, Dawei Song, Wenjie Li
Date: 2013-02-16 00:00:00
Tasks: Density Estimation, Dimensionality Reduction

Typical dimensionality reduction methods focus on directly reducing the
number of random variables while retaining maximal variations in the data. In
this paper, we consider the dimensionality reduction in parameter spaces of
binary multivariate distributions. We propose a general
Confident-Information-First (CIF) principle to maximally preserve parameters
with confident estimates and rule out unreliable or noisy parameters. Formally,
the confidence of a parameter can be assessed by its Fisher information, which
establishes a connection with the inverse variance of any unbiased estimate for
the parameter via the Cram\'{e}r-Rao bound. We then revisit Boltzmann machines
(BM) and theoretically show that both single-layer BM without hidden units
(SBM) and restricted BM (RBM) can be solidly derived using the CIF principle.
This can not only help us uncover and formalize the essential parts of the
target density that SBM and RBM capture, but also suggest that the deep neural
network consisting of several layers of RBM can be seen as the layer-wise
application of CIF. Guided by the theoretical analysis, we develop a
sample-specific CIF-based contrastive divergence (CD-CIF) algorithm for SBM and
a CIF-based iterative projection procedure (IP) for RBM. Both CD-CIF and IP are
studied in a series of density estimation experiments.
Towards common-sense reasoning via conditional simulation: legacies of Turing in Artificial Intelligence
Authors: Cameron E. Freer, Daniel M. Roy, Joshua B. Tenenbaum
Date: 2012-12-19 00:00:00
Tasks: Common Sense Reasoning, Philosophy

The problem of replicating the flexibility of human common-sense reasoning
has captured the imagination of computer scientists since the early days of
Alan Turing's foundational work on computation and the philosophy of artificial
intelligence. In the intervening years, the idea of cognition as computation
has emerged as a fundamental tenet of Artificial Intelligence (AI) and
cognitive science. But what kind of computation is cognition?
  We describe a computational formalism centered around a probabilistic Turing
machine called QUERY, which captures the operation of probabilistic
conditioning via conditional simulation. Through several examples and analyses,
we demonstrate how the QUERY abstraction can be used to cast common-sense
reasoning as probabilistic inference in a statistical model of our observations
and the uncertain structure of the world that generated that experience. This
formulation is a recent synthesis of several research programs in AI and
cognitive science, but it also represents a surprising convergence of several
of Turing's pioneering insights in AI, the foundations of computation, and
statistics.
Combining Ontologies with Correspondences and Link Relations: The E-SHIQ Representation Framework
Authors: George A. Vouros, Georgios Santipantakis
Date: 2013-10-09 00:00:00

Combining knowledge and beliefs of autonomous peers in distributed settings,
is a ma- jor challenge. In this paper we consider peers that combine ontologies
and reason jointly with their coupled knowledge. Ontologies are within the SHIQ
fragment of Description Logics. Although there are several representation
frameworks for modular Description Log- ics, each one makes crucial assumptions
concerning the subjectivity of peers' knowledge, the relation between the
domains over which ontologies are interpreted, the expressivity of the
constructors used for combining knowledge, and the way peers share their
knowledge. However in settings where autonomous peers can evolve and extend
their knowledge and beliefs independently from others, these assumptions may
not hold. In this article, we moti- vate the need for a representation
framework that allows peers to combine their knowledge in various ways,
maintaining the subjectivity of their own knowledge and beliefs, and that
reason collaboratively, constructing a tableau that is distributed among them,
jointly. The paper presents the proposed E-SHIQ representation framework, the
implementation of the E-SHIQ distributed tableau reasoner, and discusses the
efficiency of this reasoner.
Development of Marathi Part of Speech Tagger Using Statistical Approach
Authors: Jyoti Singh, Nisheeth Joshi, Iti Mathur
Date: 2013-10-02 00:00:00
Tasks: Information Retrieval, Part-Of-Speech Tagging, POS, POS Tagging, Retrieval, Sentence, Speech Synthesis, TAG, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Part-of-speech (POS) tagging is a process of assigning the words in a text
corresponding to a particular part of speech. A fundamental version of POS
tagging is the identification of words as nouns, verbs, adjectives etc. For
processing natural languages, Part of Speech tagging is a prominent tool. It is
one of the simplest as well as most constant and statistical model for many NLP
applications. POS Tagging is an initial stage of linguistics, text analysis
like information retrieval, machine translator, text to speech synthesis,
information extraction etc. In POS Tagging we assign a Part of Speech tag to
each word in a sentence and literature. Various approaches have been proposed
to implement POS taggers. In this paper we present a Marathi part of speech
tagger. It is morphologically rich language. Marathi is spoken by the native
people of Maharashtra. The general approach used for development of tagger is
statistical using Unigram, Bigram, Trigram and HMM Methods. It presents a clear
idea about all the algorithms with suitable examples. It also introduces a tag
set for Marathi which can be used for tagging Marathi text. In this paper we
have shown the development of the tagger as well as compared to check the
accuracy of taggers output. The three Marathi POS taggers viz. Unigram, Bigram,
Trigram and HMM gives the accuracy of 77.38%, 90.30%, 91.46% and 93.82%
respectively.
Learning-Based Procedural Content Generation
Authors: Jonathan Roberts, Ke Chen
Date: 2013-08-29 00:00:00

Procedural content generation (PCG) has recently become one of the hottest
topics in computational intelligence and AI game researches. Among a variety of
PCG techniques, search-based approaches overwhelmingly dominate PCG development
at present. While SBPCG leads to promising results and successful applications,
it poses a number of challenges ranging from representation to evaluation of
the content being generated. In this paper, we present an alternative yet
generic PCG framework, named learning-based procedure content generation
(LBPCG), to provide potential solutions to several challenging problems in
existing PCG techniques. By exploring and exploiting information gained in game
development and public beta test via data-driven learning, our framework can
generate robust content adaptable to end-user or target players on-line with
minimal interruption to their experience. Furthermore, we develop enabling
techniques to implement the various models required in our framework. For a
proof of concept, we have developed a prototype based on the classic open
source first-person shooter game, Quake. Simulation results suggest that our
framework is promising in generating quality content.
Discriminative Relational Topic Models
Authors: Ning Chen, Jun Zhu, Fei Xia, Bo Zhang
Date: 2013-10-09 00:00:00
Tasks: Bayesian Inference, Data Augmentation, Topic Models

Many scientific and engineering fields involve analyzing network data. For
document networks, relational topic models (RTMs) provide a probabilistic
generative process to describe both the link structure and document contents,
and they have shown promise on predicting network structures and discovering
latent topic representations. However, existing RTMs have limitations in both
the restricted model expressiveness and incapability of dealing with imbalanced
network data. To expand the scope and improve the inference accuracy of RTMs,
this paper presents three extensions: 1) unlike the common link likelihood with
a diagonal weight matrix that allows the-same-topic interactions only, we
generalize it to use a full weight matrix that captures all pairwise topic
interactions and is applicable to asymmetric networks; 2) instead of doing
standard Bayesian inference, we perform regularized Bayesian inference
(RegBayes) with a regularization parameter to deal with the imbalanced link
structure issue in common real networks and improve the discriminative ability
of learned latent representations; and 3) instead of doing variational
approximation with strict mean-field assumptions, we present collapsed Gibbs
sampling algorithms for the generalized relational topic models by exploring
data augmentation without making restricting assumptions. Under the generic
RegBayes framework, we carefully investigate two popular discriminative loss
functions, namely, the logistic log-loss and the max-margin hinge loss.
Experimental results on several real network datasets demonstrate the
significance of these extensions on improving the prediction performance, and
the time efficiency can be dramatically improved with a simple fast
approximation method.
Improved Bayesian Logistic Supervised Topic Models with Data Augmentation
Authors: Jun Zhu, Xun Zheng, Bo Zhang
Date: 2013-10-09 00:00:00
Proceeding: ACL 2013 8
Tasks: Bayesian Inference, Data Augmentation, Topic Models, Variational Inference

Supervised topic models with a logistic likelihood have two issues that
potentially limit their practical use: 1) response variables are usually
over-weighted by document word counts; and 2) existing variational inference
methods make strict mean-field assumptions. We address these issues by: 1)
introducing a regularization constant to better balance the two parts based on
an optimization formulation of Bayesian inference; and 2) developing a simple
Gibbs sampling algorithm by introducing auxiliary Polya-Gamma variables and
collapsing out Dirichlet variables. Our augment-and-collapse sampling algorithm
has analytical forms of each conditional distribution without making any
restricting assumptions and can be easily parallelized. Empirical results
demonstrate significant improvements on prediction performance and time
efficiency.
A Fuzzy AHP Approach for Supplier Selection Problem: A Case Study in a Gear Motor Company
Authors: Mustafa Batuhan Ayhan
Date: 2013-10-09 00:00:00
Tasks: Decision Making

Suuplier selection is one of the most important functions of a purchasing
department. Since by deciding the best supplier, companies can save material
costs and increase competitive advantage.However this decision becomes
compilcated in case of multiple suppliers, multiple conflicting criteria, and
imprecise parameters. In addition the uncertainty and vagueness of the experts'
opinion is the prominent characteristic of the problem. therefore an
extensively used multi criteria decision making tool Fuzzy AHP can be utilized
as an approach for supplier selection problem. This paper reveals the
application of Fuzzy AHP in a gear motor company determining the best supplier
with respect to selected criteria. the contribution of this study is not only
the application of the Fuzzy AHP methodology for supplier selection problem,
but also releasing a comprehensive literature review of multi criteria decision
making problems. In addition by stating the steps of Fuzzy AHP clearly and
numerically, this study can be a guide of the methodology to be implemented to
other multiple criteria decision making problems.
A necessary and sufficient condition for two relations to induce the same definable set family
Authors: Hua Yao, William Zhu
Date: 2013-10-09 00:00:00

In Pawlak rough sets, the structure of the definable set families is simple
and clear, but in generalizing rough sets, the structure of the definable set
families is a bit more complex. There has been much research work focusing on
this topic. However, as a fundamental issue in relation based rough sets, under
what condition two relations induce the same definable set family has not been
discussed. In this paper, based on the concept of the closure of relations, we
present a necessary and sufficient condition for two relations to induce the
same definable set family.
Neural perceptual model to global-local vision for recognition of the logical structure of administrative documents
Authors: Boulbaba Ben Ammar
Date: 2013-10-09 00:00:00

This paper gives the definition of Transparent Neural Network "TNN" for the
simulation of the globallocal vision and its application to the segmentation of
administrative document image. We have developed and have adapted a recognition
method which models the contextual effects reported from studies in
experimental psychology. Then, we evaluated and tested the TNN and the
multi-layer perceptron "MLP", which showed its effectiveness in the field of
the recognition, in order to show that the TNN is clearer for the user and more
powerful on the level of the recognition. Indeed, the TNN is the only system
which makes it possible to recognize the document and its structure.
The Generalized Traveling Salesman Problem solved with Ant Algorithms
Authors: Camelia-M. Pintea, Petrica C. Pop, Camelia Chira
Date: 2013-10-09 00:00:00
Tasks: Traveling Salesman Problem

A well known N P-hard problem called the Generalized Traveling Salesman
Problem (GTSP) is considered. In GTSP the nodes of a complete undirected graph
are partitioned into clusters. The objective is to find a minimum cost tour
passing through exactly one node from each cluster. An exact exponential time
algorithm and an effective meta-heuristic algorithm for the problem are
presented. The meta-heuristic proposed is a modified Ant Colony System (ACS)
algorithm called Reinforcing Ant Colony System (RACS) which introduces new
correction rules in the ACS algorithm. Computational results are reported for
many standard test problems. The proposed algorithm is competitive with the
other already proposed heuristics for the GTSP in both solution quality and
computational time.
Simplifying Energy Optimization using Partial Enumeration
Authors: Carl Olsson, Johannes Ulen, Yuri Boykov, Vladimir Kolmogorov
Date: 2013-03-07 00:00:00

Energies with high-order non-submodular interactions have been shown to be
very useful in vision due to their high modeling power. Optimization of such
energies, however, is generally NP-hard. A naive approach that works for small
problem instances is exhaustive search, that is, enumeration of all possible
labelings of the underlying graph. We propose a general minimization approach
for large graphs based on enumeration of labelings of certain small patches.
This partial enumeration technique reduces complex high-order energy
formulations to pairwise Constraint Satisfaction Problems with unary costs
(uCSP), which can be efficiently solved using standard methods like TRW-S. Our
approach outperforms a number of existing state-of-the-art algorithms on well
known difficult problems (e.g. curvature regularization, stereo,
deconvolution); it gives near global minimum and better speed.
  Our main application of interest is curvature regularization. In the context
of segmentation, our partial enumeration technique allows to evaluate curvature
directly on small patches using a novel integral geometry approach.
Treating clitics with minimalist grammars
Authors: Maxime Amblard
Date: 2013-10-08 00:00:00

We propose an extension of Stabler's version of clitics treatment for a wider
coverage of the French language. For this, we present the lexical entries
needed in the lexicon. Then, we show the recognition of complex syntactic
phenomena as (left and right) dislo- cation, clitic climbing over modal and
extraction from determiner phrase. The aim of this presentation is the
syntax-semantic interface for clitics analyses in which we will stress on
clitic climbing over verb and raising verb.
Optimization Of Cross Domain Sentiment Analysis Using Sentiwordnet
Authors: K Paramesha, K C Ravishankar
Date: 2013-10-08 00:00:00
Tasks: General Classification, Sentence, Sentiment Analysis

The task of sentiment analysis of reviews is carried out using manually built
/ automatically generated lexicon resources of their own with which terms are
matched with lexicon to compute the term count for positive and negative
polarity. On the other hand the Sentiwordnet, which is quite different from
other lexicon resources that gives scores (weights) of the positive and
negative polarity for each word. The polarity of a word namely positive,
negative and neutral have the score ranging between 0 to 1 indicates the
strength/weight of the word with that sentiment orientation. In this paper, we
show that using the Sentiwordnet, how we could enhance the performance of the
classification at both sentence and document level.
Graphical law beneath each written natural language
Authors: Anindya Kumar Biswas
Date: 2013-07-18 00:00:00

We study twenty four written natural languages. We draw in the log scale, number of words starting with a letter vs rank of the letter, both normalised. We find that all the graphs are of the similar type. The graphs are tantalisingly closer to the curves of reduced magnetisation vs reduced temperature for magnetic materials. We make a weak conjecture that a curve of magnetisation underlies a written natural language.
A short note on the axiomatic requirements of uncertainty measure
Authors: Xinyang Deng, Yong Deng
Date: 2013-10-08 00:00:00

In this note, we argue that the axiomatic requirement of range to the measure
of aggregated total uncertainty (ATU) in Dempster-Shafer theory is not
reasonable.
Double four-bar crank-slider mechanism dynamic balancing by meta-heuristic algorithms
Authors: Habib Emdadi, Mahsa Yazdanian, Mir Mohammad Ettefagh, Mohammad-Reza Feizi-Derakhshi
Date: 2013-10-08 00:00:00

In this paper, a new method for dynamic balancing of double four-bar crank
slider mechanism by meta- heuristic-based optimization algorithms is proposed.
For this purpose, a proper objective function which is necessary for balancing
of this mechanism and corresponding constraints has been obtained by dynamic
modeling of the mechanism. Then PSO, ABC, BGA and HGAPSO algorithms have been
applied for minimizing the defined cost function in optimization step. The
optimization results have been studied completely by extracting the cost
function, fitness, convergence speed and runtime values of applied algorithms.
It has been shown that PSO and ABC are more efficient than BGA and HGAPSO in
terms of convergence speed and result quality. Also, a laboratory scale
experimental doublefour-bar crank-slider mechanism was provided for validating
the proposed balancing method practically.
A Robust Variational Model for Positive Image Deconvolution
Authors: Martin Welk
Date: 2013-10-08 00:00:00
Tasks: Computational Efficiency, Image Deconvolution, Image Restoration

In this paper, an iterative method for robust deconvolution with positivity
constraints is discussed. It is based on the known variational interpretation
of the Richardson-Lucy iterative deconvolution as fixed-point iteration for the
minimisation of an information divergence functional under a multiplicative
perturbation model. The asymmetric penaliser function involved in this
functional is then modified into a robust penaliser, and complemented with a
regulariser. The resulting functional gives rise to a fixed point iteration
that we call robust and regularised Richardson-Lucy deconvolution. It achieves
an image restoration quality comparable to state-of-the-art robust variational
deconvolution with a computational efficiency similar to that of the original
Richardson-Lucy method. Experiments on synthetic and real-world image data
demonstrate the performance of the proposed method.
Predicting Students' Performance Using ID3 And C4.5 Classification Algorithms
Authors: Kalpesh Adhatrao, Aditya Gaykar, Amiraj Dhawan, Rohit Jha, Vipul Honrao
Date: 2013-10-08 00:00:00
Tasks: Classification, General Classification

An educational institution needs to have an approximate prior knowledge of
enrolled students to predict their performance in future academics. This helps
them to identify promising students and also provides them an opportunity to
pay attention to and improve those who would probably get lower grades. As a
solution, we have developed a system which can predict the performance of
students from their previous performances using concepts of data mining
techniques under Classification. We have analyzed the data set containing
information about students, such as gender, marks scored in the board
examinations of classes X and XII, marks and rank in entrance examinations and
results in first year of the previous batch of students. By applying the ID3
(Iterative Dichotomiser 3) and C4.5 classification algorithms on this data, we
have predicted the general and individual performance of freshly admitted
students in future examinations.