pwc-archive/papers-with-abstracts huggingface dataset:
Penetration Testing == POMDP Solving?
Authors: Carlos Sarraute, Olivier Buffet, Joerg Hoffmann
Date: 2013-06-19 00:00:00

Penetration Testing is a methodology for assessing network security, by
generating and executing possible attacks. Doing so automatically allows for
regular and systematic testing without a prohibitive amount of human labor. A
key question then is how to generate the attacks. This is naturally formulated
as a planning problem. Previous work (Lucangeli et al. 2010) used classical
planning and hence ignores all the incomplete knowledge that characterizes
hacking. More recent work (Sarraute et al. 2011) makes strong independence
assumptions for the sake of scaling, and lacks a clear formal concept of what
the attack planning problem actually is. Herein, we model that problem in terms
of partially observable Markov decision processes (POMDP). This grounds
penetration testing in a well-researched formalism, highlighting important
aspects of this problem's nature. POMDPs allow to model information gathering
as an integral part of the problem, thus providing for the first time a means
to intelligently mix scanning actions with actual exploits.
Using Arabic Wordnet for semantic indexation in information retrieval system
Authors: Mohammed Alaeddine Abderrahim, Mohammed El Amine Abderrahim, Mohammed Amine Chikh
Date: 2013-06-11 00:00:00
Tasks: Information Retrieval, Retrieval

In the context of arabic Information Retrieval Systems (IRS) guided by arabic
ontology and to enable those systems to better respond to user requirements,
this paper aims to representing documents and queries by the best concepts
extracted from Arabic Wordnet. Identified concepts belonging to Arabic WordNet
synsets are extracted from documents and queries, and those having a single
sense are expanded. The expanded query is then used by the IRS to retrieve the
relevant documents searched. Our experiments are based primarily on a medium
size corpus of arabic text. The results obtained shown us that there are a
global improvement in the performance of the arabic IRS.
Towards Multistage Design of Modular Systems
Authors: Mark Sh. Levin
Date: 2013-06-19 00:00:00
Tasks: Multiple-choice

The paper describes multistage design of composite (modular) systems (i.e.,
design of a system trajectory). This design process consists of the following:
(i) definition of a set of time/logical points; (ii) modular design of the
system for each time/logical point (e.g., on the basis of combinatorial
synthesis as hierarchical morphological design or multiple choice problem) to
obtain several system solutions; (iii) selection of the system solution for
each time/logical point while taking into account their quality and the quality
of compatibility between neighbor selected system solutions (here,
combinatorial synthesis is used as well). Mainly, the examined time/logical
points are based on a time chain. In addition, two complicated cases are
considered: (a) the examined logical points are based on a tree-like structure,
(b) the examined logical points are based on a digraph. Numerical examples
illustrate the approach.
Non-Correlated Character Recognition using Artificial Neural Network
Authors: Tirtharaj Dash, Tanistha Nayak
Date: 2013-06-19 00:00:00

This paper investigates a method of Handwritten English Character Recognition
using Artificial Neural Network (ANN). This work has been done in offline
Environment for non correlated characters, which do not possess any linear
relationships among them. We test that whether the particular tested character
belongs to a cluster or not. The implementation is carried out in Matlab
environment and successfully tested. Fifty-two sets of English alphabets are
used to train the ANN and test the network. The algorithms are tested with 26
capital letters and 26 small letters. The testing result showed that the
proposed ANN based algorithm showed a maximum recognition rate of 85%.
Solution to Quadratic Equation Using Genetic Algorithm
Authors: Tanistha Nayak, Tirtharaj Dash
Date: 2013-06-19 00:00:00

Solving Quadratic equation is one of the intrinsic interests as it is the
simplest nonlinear equations. A novel approach for solving Quadratic Equation
based on Genetic Algorithms (GAs) is presented. Genetic Algorithms (GAs) are a
technique to solve problems which need optimization. Generation of trial
solutions have been formed by this method. Many examples have been worked out,
and in most cases we find out the exact solution. We have discussed the effect
of different parameters on the performance of the developed algorithm. The
results are concluded after rigorous testing on different equations.
English Character Recognition using Artificial Neural Network
Authors: Tirtharaj Dash, Tanistha Nayak
Date: 2013-06-19 00:00:00

This work focuses on development of a Offline Hand Written English Character
Recognition algorithm based on Artificial Neural Network (ANN). The ANN
implemented in this work has single output neuron which shows whether the
tested character belongs to a particular cluster or not. The implementation is
carried out completely in 'C' language. Ten sets of English alphabets
(small-26, capital-26) were used to train the ANN and 5 sets of English
alphabets were used to test the network. The characters were collected from
different persons over duration of about 25 days. The algorithm was tested with
5 capital letters and 5 small letter sets. However, the result showed that the
algorithm recognized English alphabet patterns with maximum accuracy of 92.59%
and False Rejection Rate (FRR) of 0%.
Time Efficient Approach To Offline Hand Written Character Recognition Using Associative Memory Net
Authors: Tirtharaj Dash
Date: 2013-06-19 00:00:00

In this paper, an efficient Offline Hand Written Character Recognition
algorithm is proposed based on Associative Memory Net (AMN). The AMN used in
this work is basically auto associative. The implementation is carried out
completely in 'C' language. To make the system perform to its best with minimal
computation time, a Parallel algorithm is also developed using an API package
OpenMP. Characters are mainly English alphabets (Small (26), Capital (26))
collected from system (52) and from different persons (52). The characters
collected from system are used to train the AMN and characters collected from
different persons are used for testing the recognition ability of the net. The
detailed analysis showed that the network recognizes the hand written
characters with recognition rate of 72.20% in average case. However, in best
case, it recognizes the collected hand written characters with 88.5%. The
developed network consumes 3.57 sec (average) in Serial implementation and 1.16
sec (average) in Parallel implementation using OpenMP.
A lasso for hierarchical interactions
Authors: Jacob Bien, Jonathan Taylor, Robert Tibshirani
Date: 2012-05-22 00:00:00

We add a set of convex constraints to the lasso to produce sparse interaction
models that honor the hierarchy restriction that an interaction only be
included in a model if one or both variables are marginally important. We give
a precise characterization of the effect of this hierarchy constraint, prove
that hierarchy holds with probability one and derive an unbiased estimate for
the degrees of freedom of our estimator. A bound on this estimate reveals the
amount of fitting "saved" by the hierarchy constraint. We distinguish between
parameter sparsity - the number of nonzero coefficients - and practical
sparsity - the number of raw variables one must measure to make a new
prediction. Hierarchy focuses on the latter, which is more closely tied to
important data collection concerns such as cost, time and effort. We develop an
algorithm, available in the R package hierNet, and perform an empirical study
of our method.
Implementing a Wall-In Building Placement in StarCraft with Declarative Programming
Authors: Michal Certicky
Date: 2013-06-19 00:00:00
Tasks: Real-Time Strategy Games, Starcraft

In real-time strategy games like StarCraft, skilled players often block the
entrance to their base with buildings to prevent the opponent's units from
getting inside. This technique, called "walling-in", is a vital part of
player's skill set, allowing him to survive early aggression. However, current
artificial players (bots) do not possess this skill, due to numerous
inconveniences surfacing during its implementation in imperative languages like
C++ or Java. In this text, written as a guide for bot programmers, we address
the problem of finding an appropriate building placement that would block the
entrance to player's base, and present a ready to use declarative solution
employing the paradigm of answer set programming (ASP). We also encourage the
readers to experiment with different declarative approaches to this problem.
Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers
Authors: Giuseppe Ateniese, Giovanni Felici, Luigi V. Mancini, Angelo Spognardi, Antonio Villani, Domenico Vitali
Date: 2013-06-19 00:00:00
Tasks: BIG-bench Machine Learning

Machine Learning (ML) algorithms are used to train computers to perform a
variety of complex tasks and improve with experience. Computers learn how to
recognize patterns, make unintended decisions, or react to a dynamic
environment. Certain trained machines may be more effective than others because
they are based on more suitable ML algorithms or because they were trained
through superior training sets. Although ML algorithms are known and publicly
released, training sets may not be reasonably ascertainable and, indeed, may be
guarded as trade secrets. While much research has been performed about the
privacy of the elements of training sets, in this paper we focus our attention
on ML classifiers and on the statistical information that can be unconsciously
or maliciously revealed from them. We show that it is possible to infer
unexpected but useful information from ML classifiers. In particular, we build
a novel meta-classifier and train it to hack other classifiers, obtaining
meaningful information about their training sets. This kind of information
leakage can be exploited, for example, by a vendor to build more effective
classifiers or to simply acquire trade secrets from a competitor's apparatus,
potentially violating its intellectual property rights.
Structure Based Extended Resolution for Constraint Programming
Authors: Geoffrey Chu, Peter J. Stuckey
Date: 2013-06-19 00:00:00

Nogood learning is a powerful approach to reducing search in Constraint
Programming (CP) solvers. The current state of the art, called Lazy Clause
Generation (LCG), uses resolution to derive nogoods expressing the reasons for
each search failure. Such nogoods can prune other parts of the search tree,
producing exponential speedups on a wide variety of problems. Nogood learning
solvers can be seen as resolution proof systems. The stronger the proof system,
the faster it can solve a CP problem. It has recently been shown that the proof
system used in LCG is at least as strong as general resolution. However,
stronger proof systems such as \emph{extended resolution} exist. Extended
resolution allows for literals expressing arbitrary logical concepts over
existing variables to be introduced and can allow exponentially smaller proofs
than general resolution. The primary problem in using extended resolution is to
figure out exactly which literals are useful to introduce. In this paper, we
show that we can use the structural information contained in a CP model in
order to introduce useful literals, and that this can translate into
significant speedups on a range of problems.
Machine Learning with Operational Costs
Authors: Theja Tulabandhula, Cynthia Rudin
Date: 2011-12-03 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

This work proposes a way to align statistical modeling with decision making.
We provide a method that propagates the uncertainty in predictive modeling to
the uncertainty in operational cost, where operational cost is the amount spent
by the practitioner in solving the problem. The method allows us to explore the
range of operational costs associated with the set of reasonable statistical
models, so as to provide a useful way for practitioners to understand
uncertainty. To do this, the operational cost is cast as a regularization term
in a learning algorithm's objective function, allowing either an optimistic or
pessimistic view of possible costs, depending on the regularization parameter.
From another perspective, if we have prior knowledge about the operational
cost, for instance that it should be low, this knowledge can help to restrict
the hypothesis space, and can help with generalization. We provide a
theoretical generalization bound for this scenario. We also show that learning
with operational costs is related to robust optimization.
Joint estimation of sparse multivariate regression and conditional graphical models
Authors: Junhui Wang
Date: 2013-06-19 00:00:00
Tasks: regression

Multivariate regression model is a natural generalization of the classical
univari- ate regression model for fitting multiple responses. In this paper, we
propose a high- dimensional multivariate conditional regression model for
constructing sparse estimates of the multivariate regression coefficient matrix
that accounts for the dependency struc- ture among the multiple responses. The
proposed method decomposes the multivariate regression problem into a series of
penalized conditional log-likelihood of each response conditioned on the
covariates and other responses. It allows simultaneous estimation of the sparse
regression coefficient matrix and the sparse inverse covariance matrix. The
asymptotic selection consistency and normality are established for the
diverging dimension of the covariates and number of responses. The
effectiveness of the pro- posed method is also demonstrated in a variety of
simulated examples as well as an application to the Glioblastoma multiforme
cancer data.
An Overview of the Research on Texture Based Plant Leaf Classification
Authors: Vishakha Metre, Jayshree Ghorpade
Date: 2013-06-18 00:00:00
Tasks: Classification, Diversity, General Classification

Plant classification has a broad application prospective in agriculture and
medicine, and is especially significant to the biology diversity research. As
plants are vitally important for environmental protection, it is more important
to identify and classify them accurately. Plant leaf classification is a
technique where leaf is classified based on its different morphological
features. The goal of this paper is to provide an overview of different aspects
of texture based plant leaf classification and related things. At last we will
be concluding about the efficient method i.e. the method that gives better
performance compared to the other methods.
The evolutionary origins of modularity
Authors: Jeff Clune, Jean-Baptiste Mouret, Hod Lipson
Date: 2012-07-11 00:00:00

A central biological question is how natural organisms are so evolvable
(capable of quickly adapting to new environments). A key driver of evolvability
is the widespread modularity of biological networks--their organization as
functional, sparsely connected subunits--but there is no consensus regarding
why modularity itself evolved. While most hypotheses assume indirect selection
for evolvability, here we demonstrate that the ubiquitous, direct selection
pressure to reduce the cost of connections between network nodes causes the
emergence of modular networks. Experiments with selection pressures to maximize
network performance and minimize connection costs yield networks that are
significantly more modular and more evolvable than control experiments that
only select for performance. These results will catalyze research in numerous
disciplines, including neuroscience, genetics and harnessing evolution for
engineering purposes.
Multiarmed Bandit Problems with Delayed Feedback
Authors: Sudipto Guha, Kamesh Munagala, Martin Pal
Date: 2010-11-04 00:00:00

In this paper we initiate the study of optimization of bandit type problems
in scenarios where the feedback of a play is not immediately known. This arises
naturally in allocation problems which have been studied extensively in the
literature, albeit in the absence of delays in the feedback. We study this
problem in the Bayesian setting. In presence of delays, no solution with
provable guarantees is known to exist with sub-exponential running time.
  We show that bandit problems with delayed feedback that arise in allocation
settings can be forced to have significant structure, with a slight loss in
optimality. This structure gives us the ability to reason about the
relationship of single arm policies to the entangled optimum policy, and
eventually leads to a O(1) approximation for a significantly general class of
priors. The structural insights we develop are of key interest and carry over
to the setting where the feedback of an action is available instantaneously,
and we improve all previous results in this setting as well.
A class of random fields on complete graphs with tractable partition function
Authors: Boris Flach
Date: 2012-12-10 00:00:00

The aim of this short note is to draw attention to a method by which the
partition function and marginal probabilities for a certain class of random
fields on complete graphs can be computed in polynomial time. This class
includes Ising models with homogeneous pairwise potentials but arbitrary
(inhomogeneous) unary potentials. Similarly, the partition function and
marginal probabilities can be computed in polynomial time for random fields on
complete bipartite graphs, provided they have homogeneous pairwise potentials.
We expect that these tractable classes of large scale random fields can be very
useful for the evaluation of approximation algorithms by providing exact error
estimates.
Generalized Beta Divergence
Authors: Y. Kenan Yilmaz
Date: 2013-06-14 00:00:00
Tasks: Form, Translation

This paper generalizes beta divergence beyond its classical form associated
with power variance functions of Tweedie models. Generalized form is
represented by a compact definite integral as a function of variance function
of the exponential dispersion model. This compact integral form simplifies
derivations of many properties such as scaling, translation and expectation of
the beta divergence. Further, we show that beta divergence and (half of) the
statistical deviance are equivalent measures.
Bioclimating Modelling: A Machine Learning Perspective
Authors: Maumita Bhattacharya
Date: 2013-06-18 00:00:00
Tasks: BIG-bench Machine Learning

Many machine learning (ML) approaches are widely used to generate bioclimatic
models for prediction of geographic range of organism as a function of climate.
Applications such as prediction of range shift in organism, range of invasive
species influenced by climate change are important parameters in understanding
the impact of climate change. However, success of machine learning-based
approaches depends on a number of factors. While it can be safely said that no
particular ML technique can be effective in all applications and success of a
technique is predominantly dependent on the application or the type of the
problem, it is useful to understand their behaviour to ensure informed choice
of techniques. This paper presents a comprehensive review of machine
learning-based bioclimatic model generation and analyses the factors
influencing success of such models. Considering the wide use of statistical
techniques, in our discussion we also include conventional statistical
techniques used in bioclimatic modelling.
Punjabi Language Interface to Database: a brief review
Authors: Preeti Verma, Suket Arora, Kamaljit Batra
Date: 2013-06-18 00:00:00
Tasks: Management

Unlike most user-computer interfaces, a natural language interface allows
users to communicate fluently with a computer system with very little
preparation. Databases are often hard to use in cooperating with the users
because of their rigid interface. A good NLIDB allows a user to enter commands
and ask questions in native language and then after interpreting respond to the
user in native language. For a large number of applications requiring
interaction between humans and the computer systems, it would be convenient to
provide the end-user friendly interface. Punjabi language interface to database
would proof fruitful to native people of Punjab, as it provides ease to them to
use various e-governance applications like Punjab Sewa, Suwidha, Online Public
Utility Forms, Online Grievance Cell, Land Records Management System,legacy
matters, e-District, agriculture, etc. Punjabi is the mother tongue of more
than 110 million people all around the world. According to available
information, Punjabi ranks 10th from top out of a total of 6,900 languages
recognized internationally by the United Nations. This paper covers a brief
overview of the Natural language interface to database, its different
components, its advantages, disadvantages, approaches and techniques used. The
paper ends with the work done on Punjabi language interface to database and
future enhancements that can be done.
Dialogue System: A Brief Review
Authors: Suket Arora, Kamaljeet Batra, Sarabjit Singh
Date: 2013-06-18 00:00:00

A Dialogue System is a system which interacts with human in natural language.
At present many universities are developing the dialogue system in their
regional language. This paper will discuss about dialogue system, its
components, challenges and its evaluation. This paper helps the researchers for
getting info regarding dialogues system.
Group Symmetry and non-Gaussian Covariance Estimation
Authors: Ilya Soloveychik, Ami Wiesel
Date: 2013-06-18 00:00:00

We consider robust covariance estimation with group symmetry constraints.
Non-Gaussian covariance estimation, e.g., Tyler scatter estimator and
Multivariate Generalized Gaussian distribution methods, usually involve
non-convex minimization problems. Recently, it was shown that the underlying
principle behind their success is an extended form of convexity over the
geodesics in the manifold of positive definite matrices. A modern approach to
improve estimation accuracy is to exploit prior knowledge via additional
constraints, e.g., restricting the attention to specific classes of covariances
which adhere to prior symmetry structures. In this paper, we prove that such
group symmetry constraints are also geodesically convex and can therefore be
incorporated into various non-Gaussian covariance estimators. Practical
examples of such sets include: circulant, persymmetric and complex/quaternion
proper structures. We provide a simple numerical technique for finding maximum
likelihood estimates under such constraints, and demonstrate their performance
advantage using synthetic experiments.