pwc-archive/papers-with-abstracts huggingface dataset:
Mining Malware Specifications through Static Reachability Analysis
Authors: Hugo Daniel Macedo, Tayssir Touili
Date: 2013-12-17 00:00:00

The number of malicious software (malware) is growing out of control.
Syntactic signature based detection cannot cope with such growth and manual
construction of malware signature databases needs to be replaced by computer
learning based approaches. Currently, a single modern signature capturing the
semantics of a malicious behavior can be used to replace an arbitrarily large
number of old-fashioned syntactical signatures. However teaching computers to
learn such behaviors is a challenge. Existing work relies on dynamic analysis
to extract malicious behaviors, but such technique does not guarantee the
coverage of all behaviors. To sidestep this limitation we show how to learn
malware signatures using static reachability analysis. The idea is to model
binary programs using pushdown systems (that can be used to model the stack
operations occurring during the binary code execution), use reachability
analysis to extract behaviors in the form of trees, and use subtrees that are
common among the trees extracted from a training set of malware files as
signatures. To detect malware we propose to use a tree automaton to compactly
store malicious behavior trees and check if any of the subtrees extracted from
the file under analysis is malicious. Experimental data shows that our approach
can be used to learn signatures from a training set of malware files and use
them to detect a test set of malware that is 5 times the size of the training
set.
Performance Engineering for a Medical Imaging Application on the Intel Xeon Phi Accelerator
Authors: Johannes Hofmann, Jan Treibig, Georg Hager, Gerhard Wellein
Date: 2013-12-17 00:00:00
Tasks: Image Reconstruction

We examine the Xeon Phi, which is based on Intel's Many Integrated Cores
architecture, for its suitability to run the FDK algorithm--the most commonly
used algorithm to perform the 3D image reconstruction in cone-beam computed
tomography. We study the challenges of efficiently parallelizing the
application and means to enable sensible data sharing between threads despite
the lack of a shared last level cache. Apart from parallelization, SIMD
vectorization is critical for good performance on the Xeon Phi; we perform
various micro-benchmarks to investigate the platform's new set of vector
instructions and put a special emphasis on the newly introduced vector gather
capability. We refine a previous performance model for the application and
adapt it for the Xeon Phi to validate the performance of our optimized
hand-written assembly implementation, as well as the performance of several
different auto-vectorization approaches.
Semantic Annotation: The Mainstay of Semantic Web
Authors: Thabet Slimani
Date: 2013-12-17 00:00:00

Given that semantic Web realization is based on the critical mass of metadata
accessibility and the representation of data with formal knowledge, it needs to
generate metadata that is specific, easy to understand and well-defined.
However, semantic annotation of the web documents is the successful way to make
the Semantic Web vision a reality. This paper introduces the Semantic Web and
its vision (stack layers) with regard to some concept definitions that helps
the understanding of semantic annotation. Additionally, this paper introduces
the semantic annotation categories, tools, domains and models.
BW - Eye Ophthalmologic decision support system based on clinical workflow and data mining techniques-image registration algorithm
Authors: Ricardo Martins
Date: 2013-12-17 00:00:00
Tasks: Image Registration

Blueworks - Medical Expert Diagnosis is developing an application, BWEye, to
be used as an ophthalmology consultation decision support system. The
implementation of this application involves several different tasks and one of
them is the implementation of an ophthalmology images registration algorithm.
The work reported in this document is related with the implementation of an
algorithm to register images of angiography, colour retinography and redfree
retinography. The implementations described were developed in the software
MATLAB.
  The implemented algorithm is based in the detection of the bifurcation points
(y-features) of the vascular structures of the retina that usually are visible
in the referred type of images. There are proposed two approaches to establish
an initial set of features correspondences. The first approach is based in the
maximization of the mutual information of the bifurcation regions of the
features of images. The second approach is based in the characterization of
each bifurcation point and in the minimization of the Euclidean distance
between the descriptions of the features of the images in the descriptors
space. The final set of the matching features for a pair of images is defined
through the application of the RANSAC algorithm.
  Although, it was not achieved the implementation of a full functional
algorithm, there were made several analysis that can be important to future
improvement of the current implementation.
Co-Sparse Textural Similarity for Image Segmentation
Authors: Claudia Nieuwenhuis, Daniel Cremers, Simon Hawe, Martin Kleinsteuber
Date: 2013-12-17 00:00:00
Tasks: Image Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

We propose an algorithm for segmenting natural images based on texture and
color information, which leverages the co-sparse analysis model for image
segmentation within a convex multilabel optimization framework. As a key
ingredient of this method, we introduce a novel textural similarity measure,
which builds upon the co-sparse representation of image patches. We propose a
Bayesian approach to merge textural similarity with information about color and
location. Combined with recently developed convex multilabel optimization
methods this leads to an efficient algorithm for both supervised and
unsupervised segmentation, which is easily parallelized on graphics hardware.
The approach provides competitive results in unsupervised segmentation and
outperforms state-of-the-art interactive segmentation methods on the Graz
Benchmark.
The Bernstein Function: A Unifying Framework of Nonconvex Penalization in Sparse Estimation
Authors: Zhihua Zhang
Date: 2013-12-17 00:00:00
Tasks: regression

In this paper we study nonconvex penalization using Bernstein functions.
Since the Bernstein function is concave and nonsmooth at the origin, it can
induce a class of nonconvex functions for high-dimensional sparse estimation
problems. We derive a threshold function based on the Bernstein penalty and
give its mathematical properties in sparsity modeling. We show that a
coordinate descent algorithm is especially appropriate for penalized regression
problems with the Bernstein penalty. Additionally, we prove that the Bernstein
function can be defined as the concave conjugate of a $\varphi$-divergence and
develop a conjugate maximization algorithm for finding the sparse solution.
Finally, we particularly exemplify a family of Bernstein nonconvex penalties
based on a generalized Gamma measure and conduct empirical analysis for this
family.
The Matrix Ridge Approximation: Algorithms and Applications
Authors: Zhihua Zhang
Date: 2013-12-17 00:00:00
Tasks: Clustering

We are concerned with an approximation problem for a symmetric positive
semidefinite matrix due to motivation from a class of nonlinear machine
learning methods. We discuss an approximation approach that we call {matrix
ridge approximation}. In particular, we define the matrix ridge approximation
as an incomplete matrix factorization plus a ridge term. Moreover, we present
probabilistic interpretations using a normal latent variable model and a
Wishart model for this approximation approach. The idea behind the latent
variable model in turn leads us to an efficient EM iterative method for
handling the matrix ridge approximation problem. Finally, we illustrate the
applications of the approximation approach in multivariate data analysis.
Empirical studies in spectral clustering and Gaussian process regression show
that the matrix ridge approximation with the EM iteration is potentially
useful.
Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem
Authors: Masrour Zoghi, Shimon Whiteson, Remi Munos, Maarten de Rijke
Date: 2013-12-12 00:00:00
Tasks: Information Retrieval, Retrieval

This paper proposes a new method for the K-armed dueling bandit problem, a
variation on the regular K-armed bandit problem that offers only relative
feedback about pairs of arms. Our approach extends the Upper Confidence Bound
algorithm to the relative setting by using estimates of the pairwise
probabilities to select a promising arm and applying Upper Confidence Bound
with the winner as a benchmark. We prove a finite-time regret bound of order
O(log t). In addition, our empirical results using real data from an
information retrieval application show that it greatly outperforms the state of
the art.
Markov Network Structure Learning via Ensemble-of-Forests Models
Authors: Eirini Arvaniti, Manfred Claassen
Date: 2013-12-17 00:00:00
Tasks: Model Selection

Real world systems typically feature a variety of different dependency types
and topologies that complicate model selection for probabilistic graphical
models. We introduce the ensemble-of-forests model, a generalization of the
ensemble-of-trees model. Our model enables structure learning of Markov random
fields (MRF) with multiple connected components and arbitrary potentials. We
present two approximate inference techniques for this model and demonstrate
their performance on synthetic data. Our results suggest that the
ensemble-of-forests approach can accurately recover sparse, possibly
disconnected MRF topologies, even in presence of non-Gaussian dependencies
and/or low sample size. We applied the ensemble-of-forests model to learn the
structure of perturbed signaling networks of immune cells and found that these
frequently exhibit non-Gaussian dependencies with disconnected MRF topologies.
In summary, we expect that the ensemble-of-forests model will enable MRF
structure learning in other high dimensional real world settings that are
governed by non-trivial dependencies.
Designing Spontaneous Speech Search Interface for Historical Archives
Authors: Donna Vakharia, Rachel Gibbs
Date: 2013-12-17 00:00:00

Spontaneous speech in the form of conversations, meetings, voice-mail,
interviews, oral history, etc. is one of the most ubiquitous forms of human
communication. Search engines providing access to such speech collections have
the potential to better inform intelligence and make relevant data over vast
audio/video archives available to users. This project presents a search user
interface design supporting search tasks over a speech collection consisting of
an historical archive with nearly 52,000 audiovisual testimonies of survivors
and witnesses of the Holocaust and other genocides. The design incorporates
faceted search, along with other UI elements like highlighted search items,
tags, snippets, etc., to promote discovery and exploratory search. Two
different designs have been created to support both manual and automated
transcripts. Evaluation was performed using human subjects to measure accuracy
in retrieving results, understanding user-perspective on the design elements,
and ease of parsing information.
RDF Translator: A RESTful Multi-Format Data Converter for the Semantic Web
Authors: Alex Stolz, Bene Rodriguez-Castro, Martin Hepp
Date: 2013-12-17 00:00:00

The interdisciplinary nature of the Semantic Web and the many projects put
forward by the community led to a large number of widely accepted serialization
formats for RDF. Most of these RDF syntaxes have been developed out of a
necessity to serve specific purposes better than existing ones, e.g. RDFa was
proposed as an extension to HTML for embedding non-intrusive RDF statements in
human-readable documents. Nonetheless, the RDF serialization formats are
generally transducible among themselves given that they are commonly based on
the RDF model. In this paper, we present (1) a RESTful Web service based on the
HTTP protocol that translates between different serializations. In addition to
its core functionality, our proposed solution provides (2) features to
accommodate frequent needs of Semantic Web developers, namely a straightforward
user interface with copy-to-clipboard functionality, syntax highlighting,
persistent URI links for easy sharing, cool URI patterns, and content
negotiation using respective HTTP headers. We demonstrate the benefit of our
converter by presenting two use cases.
Stable mixed graphs
Authors: Kayvan Sadeghi
Date: 2011-10-19 00:00:00

In this paper, we study classes of graphs with three types of edges that
capture the modified independence structure of a directed acyclic graph (DAG)
after marginalisation over unobserved variables and conditioning on selection
variables using the $m$-separation criterion. These include MC, summary, and
ancestral graphs. As a modification of MC graphs, we define the class of
ribbonless graphs (RGs) that permits the use of the $m$-separation criterion.
RGs contain summary and ancestral graphs as subclasses, and each RG can be
generated by a DAG after marginalisation and conditioning. We derive simple
algorithms to generate RGs, from given DAGs or RGs, and also to generate
summary and ancestral graphs in a simple way by further extension of the
RG-generating algorithm. This enables us to develop a parallel theory on these
three classes and to study the relationships between them as well as the use of
each class.
A Review of Temporal Aspects of Hand Gesture Analysis Applied to Discourse Analysis and Natural Conversation
Authors: Renata Cristina Barros Madeo, Priscilla Koch Wagner, Sarajane Marques Peres
Date: 2013-12-17 00:00:00
Tasks: multimodal interaction, Systematic Literature Review

Lately, there has been an increasing interest in hand gesture analysis
systems. Recent works have employed pattern recognition techniques and have
focused on the development of systems with more natural user interfaces. These
systems may use gestures to control interfaces or recognize sign language
gestures, which can provide systems with multimodal interaction; or consist in
multimodal tools to help psycholinguists to understand new aspects of discourse
analysis and to automate laborious tasks. Gestures are characterized by several
aspects, mainly by movements and sequence of postures. Since data referring to
movements or sequences carry temporal information, this paper presents a
literature review about temporal aspects of hand gesture analysis, focusing on
applications related to natural conversation and psycholinguistic analysis,
using Systematic Literature Review methodology. In our results, we organized
works according to type of analysis, methods, highlighting the use of Machine
Learning techniques, and applications.
Compact Random Feature Maps
Authors: Raffay Hamid, Ying Xiao, Alex Gittens, Dennis Decoste
Date: 2013-12-17 00:00:00

Kernel approximation using randomized feature maps has recently gained a lot
of interest. In this work, we identify that previous approaches for polynomial
kernel approximation create maps that are rank deficient, and therefore do not
utilize the capacity of the projected feature space effectively. To address
this challenge, we propose compact random feature maps (CRAFTMaps) to
approximate polynomial kernels more concisely and accurately. We prove the
error bounds of CRAFTMaps demonstrating their superior kernel reconstruction
performance compared to the previous approximation schemes. We show how
structured random matrices can be used to efficiently generate CRAFTMaps, and
present a single-pass algorithm using CRAFTMaps to learn non-linear multi-class
classifiers. We present experiments on multiple standard data-sets with
performance competitive with state-of-the-art results.
Evolution and Computational Learning Theory: A survey on Valiant's paper
Authors: Arka Bhattacharya
Date: 2013-12-17 00:00:00
Tasks: Learning Theory, Survey

Darwin's theory of evolution is considered to be one of the greatest
scientific gems in modern science. It not only gives us a description of how
living things evolve, but also shows how a population evolves through time and
also, why only the fittest individuals continue the generation forward. The
paper basically gives a high level analysis of the works of Valiant[1]. Though,
we know the mechanisms of evolution, but it seems that there does not exist any
strong quantitative and mathematical theory of the evolution of certain
mechanisms. What is defined exactly as the fitness of an individual, why is
that only certain individuals in a population tend to mutate, how computation
is done in finite time when we have exponentially many examples: there seems to
be a lot of questions which need to be answered. [1] basically treats Darwinian
theory as a form of computational learning theory, which calculates the net
fitness of the hypotheses and thus distinguishes functions and their classes
which could be evolvable using polynomial amount of resources. Evolution is
considered as a function of the environment and the previous evolutionary
stages that chooses the best hypothesis using learning techniques that makes
mutation possible and hence, gives a quantitative idea that why only the
fittest individuals tend to survive and have the power to mutate.
Comparative Analysis of Viterbi Training and Maximum Likelihood Estimation for HMMs
Authors: Armen E. Allahverdyan, Aram Galstyan
Date: 2013-12-16 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: parameter estimation

We present an asymptotic analysis of Viterbi Training (VT) and contrast it
with a more conventional Maximum Likelihood (ML) approach to parameter
estimation in Hidden Markov Models. While ML estimator works by (locally)
maximizing the likelihood of the observed data, VT seeks to maximize the
probability of the most likely hidden state sequence. We develop an analytical
framework based on a generating function formalism and illustrate it on an
exactly solvable model of HMM with one unambiguous symbol. For this particular
model the ML objective function is continuously degenerate. VT objective, in
contrast, is shown to have only finite degeneracy. Furthermore, VT converges
faster and results in sparser (simpler) models, thus realizing an automatic
Occam's razor for HMM learning. For more general scenario VT can be worse
compared to ML but still capable of correctly recovering most of the
parameters.
Parametric Modelling of Multivariate Count Data Using Probabilistic Graphical Models
Authors: Pierre Fernique, Jean-Baptiste Durand, Yann Guédon
Date: 2013-12-16 00:00:00
Tasks: Econometrics, Sociology

Multivariate count data are defined as the number of items of different
categories issued from sampling within a population, which individuals are
grouped into categories. The analysis of multivariate count data is a recurrent
and crucial issue in numerous modelling problems, particularly in the fields of
biology and ecology (where the data can represent, for example, children counts
associated with multitype branching processes), sociology and econometrics. We
focus on I) Identifying categories that appear simultaneously, or on the
contrary that are mutually exclusive. This is achieved by identifying
conditional independence relationships between the variables; II)Building
parsimonious parametric models consistent with these relationships; III)
Characterising and testing the effects of covariates on the joint distribution
of the counts. To achieve these goals, we propose an approach based on
graphical probabilistic models, and more specifically partially directed
acyclic graphs.
A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain
Authors: Sudipta Roy, Sanjay Nag, Indra Kanta Maitra, Samir Kumar Bandyopadhyay
Date: 2013-12-16 00:00:00
Tasks: Anatomy, Diversity, Segmentation, Tumor Segmentation

Tumor segmentation from magnetic resonance imaging (MRI) data is an important
but time consuming manual task performed by medical experts. Automating this
process is a challenging task because of the high diversity in the appearance
of tumor tissues among different patients and in many cases similarity with the
normal tissues. MRI is an advanced medical imaging technique providing rich
information about the human soft-tissue anatomy. There are different brain
tumor detection and segmentation methods to detect and segment a brain tumor
from MRI images. These detection and segmentation approaches are reviewed with
an importance placed on enlightening the advantages and drawbacks of these
methods for brain tumor detection and segmentation. The use of MRI image
detection and segmentation in different procedures are also described. Here a
brief review of different segmentation for detection of brain tumor from MRI of
brain has been discussed.
Optimization for Compressed Sensing: the Simplex Method and Kronecker Sparsification
Authors: Robert Vanderbei, Han Liu, Lie Wang, Kevin Lin
Date: 2013-12-16 00:00:00
Tasks: compressed sensing

In this paper we present two new approaches to efficiently solve large-scale
compressed sensing problems. These two ideas are independent of each other and
can therefore be used either separately or together. We consider all
possibilities.
  For the first approach, we note that the zero vector can be taken as the
initial basic (infeasible) solution for the linear programming problem and
therefore, if the true signal is very sparse, some variants of the simplex
method can be expected to take only a small number of pivots to arrive at a
solution. We implemented one such variant and demonstrate a dramatic
improvement in computation time on very sparse signals.
  The second approach requires a redesigned sensing mechanism in which the
vector signal is stacked into a matrix. This allows us to exploit the Kronecker
compressed sensing (KCS) mechanism. We show that the Kronecker sensing requires
stronger conditions for perfect recovery compared to the original vector
problem. However, the Kronecker sensing, modeled correctly, is a much sparser
linear optimization problem. Hence, algorithms that benefit from sparse problem
representation, such as interior-point methods, can solve the Kronecker sensing
problems much faster than the corresponding vector problem. In our numerical
studies, we demonstrate a ten-fold improvement in the computation time.
Unsupervised learning of depth and motion
Authors: Kishore Konda, Roland Memisevic
Date: 2013-12-12 00:00:00

We present a model for the joint estimation of disparity and motion. The
model is based on learning about the interrelations between images from
multiple cameras, multiple frames in a video, or the combination of both. We
show that learning depth and motion cues, as well as their combinations, from
data is possible within a single type of architecture and a single type of
learning algorithm, by using biologically inspired "complex cell" like units,
which encode correlations between the pixels across image pairs. Our
experimental results show that the learning of depth and motion makes it
possible to achieve state-of-the-art performance in 3-D activity analysis, and
to outperform existing hand-engineered 3-D motion features by a very large
margin.