pwc-archive/papers-with-abstracts huggingface dataset:
Collaborative ontology sharing and editing
Authors: Philippe A. Martin
Date: 2013-05-30 00:00:00
Tasks: General Knowledge

This article first lists reasons why - in the long term or when creating a
new knowledge base (KB) for general knowledge sharing purposes -
collaboratively building a well-organized KB does/can provide more
possibilities, with on the whole no more costs, than the mainstream approach
where knowledge creation and re-use involves searching, merging and creating
(semi-)independent (relatively small) ontologies or semi-formal documents. The
article lists elements required to achieve this and describes the main one: a
KB editing protocol that keeps the KB free of automatically/manually detected
inconsistencies while not forcing them to discuss or agree on terminology and
beliefs nor requiring a selection committee.
Lensless Imaging by Compressive Sensing
Authors: Gang Huang, Hong Jiang, Kim Matthews, Paul Wilford
Date: 2013-05-30 00:00:00
Tasks: Compressive Sensing

In this paper, we propose a lensless compressive imaging architecture. The
architecture consists of two components, an aperture assembly and a sensor. No
lens is used. The aperture assembly consists of a two dimensional array of
aperture elements. The transmittance of each aperture element is independently
controllable. The sensor is a single detection element. A compressive sensing
matrix is implemented by adjusting the transmittance of the individual aperture
elements according to the values of the sensing matrix. The proposed
architecture is simple and reliable because no lens is used. The architecture
can be used for capturing images of visible and other spectra such as infrared,
or millimeter waves, in surveillance applications for detecting anomalies or
extracting features such as speed of moving objects. Multiple sensors may be
used with a single aperture assembly to capture multi-view images
simultaneously. A prototype was built by using a LCD panel and a photoelectric
sensor for capturing images of visible spectrum.
Structural and Functional Discovery in Dynamic Networks with Non-negative Matrix Factorization
Authors: Shawn Mankad, George Michailidis
Date: 2013-05-30 00:00:00
Tasks: Community Detection, Time Series, Time Series Analysis

Time series of graphs are increasingly prevalent in modern data and pose
unique challenges to visual exploration and pattern extraction. This paper
describes the development and application of matrix factorizations for
exploration and time-varying community detection in time-evolving graph
sequences. The matrix factorization model allows the user to home in on and
display interesting, underlying structure and its evolution over time. The
methods are scalable to weighted networks with a large number of time points or
nodes, and can accommodate sudden changes to graph topology. Our techniques are
demonstrated with several dynamic graph series from both synthetic and real
world data, including citation and trade networks. These examples illustrate
how users can steer the techniques and combine them with existing methods to
discover and display meaningful patterns in sizable graphs over many time
points.
Modelling and Analysing Cargo Screening Processes: A Project Outline
Authors: Peer-Olaf Siebers, Uwe Aickelin, David Menachof, Galina Sherman, Peter Zimmerman
Date: 2013-05-30 00:00:00
Tasks: Decision Making

The efficiency of current cargo screening processes at sea and air ports is
unknown as no benchmarks exists against which they could be measured. Some
manufacturer benchmarks exist for individual sensors but we have not found any
benchmarks that take a holistic view of the screening procedures assessing a
combination of sensors and also taking operator variability into account. Just
adding up resources and manpower used is not an effective way for assessing
systems where human decision-making and operator compliance to rules play a
vital role. For such systems more advanced assessment methods need to be used,
taking into account that the cargo screening process is of a dynamic and
stochastic nature. Our project aim is to develop a decision support tool
(cargo-screening system simulator) that will map the right technology and
manpower to the right commodity-threat combination in order to maximize
detection rates. In this paper we present a project outline and highlight the
research challenges we have identified so far. In addition we introduce our
first case study, where we investigate the cargo screening process at the ferry
port in Calais.
Immune System Approaches to Intrusion Detection - A Review (ICARIS)
Authors: Uwe Aickelin, Julie Greensmith, Jamie Twycross
Date: 2013-05-30 00:00:00
Tasks: Computer Security, Intrusion Detection

The use of artificial immune systems in intrusion detection is an appealing
concept for two reasons. Firstly, the human immune system provides the human
body with a high level of protection from invading pathogens, in a robust,
self-organised and distributed manner. Secondly, current techniques used in
computer security are not able to cope with the dynamic and increasingly
complex nature of computer systems and their security. It is hoped that
biologically inspired approaches in this area, including the use of
immune-based systems will be able to meet this challenge. Here we collate the
algorithms used, the development of the systems and the outcome of their
implementation. It provides an introduction and review of the key developments
within this field, in addition to making suggestions for future research.
Memory Implementations - Current Alternatives
Authors: William Wilson, Uwe Aickelin
Date: 2013-05-30 00:00:00

Memory can be defined as the ability to retain and recall information in a
diverse range of forms. It is a vital component of the way in which we as human
beings operate on a day to day basis. Given a particular situation, decisions
are made and actions undertaken in response to that situation based on our
memory of related prior events and experiences. By utilising our memory we can
anticipate the outcome of our chosen actions to avoid unexpected or unwanted
events. In addition, as we subtly alter our actions and recognise altered
outcomes we learn and create new memories, enabling us to improve the
efficiency of our actions over time. However, as this process occurs so
naturally in the subconscious its importance is often overlooked.
Test cost and misclassification cost trade-off using reframing
Authors: Celestine Periale Maguedong-Djoumessi, José Hernández-Orallo
Date: 2013-05-30 00:00:00
Tasks: Missing Values

Many solutions to cost-sensitive classification (and regression) rely on some
or all of the following assumptions: we have complete knowledge about the cost
context at training time, we can easily re-train whenever the cost context
changes, and we have technique-specific methods (such as cost-sensitive
decision trees) that can take advantage of that information. In this paper we
address the problem of selecting models and minimising joint cost (integrating
both misclassification cost and test costs) without any of the above
assumptions. We introduce methods and plots (such as the so-called JROC plots)
that can work with any off-the-shelf predictive technique, including ensembles,
such that we reframe the model to use the appropriate subset of attributes (the
feature configuration) during deployment time. In other words, models are
trained with the available attributes (once and for all) and then deployed by
setting missing values on the attributes that are deemed ineffective for
reducing the joint cost. As the number of feature configuration combinations
grows exponentially with the number of features we introduce quadratic methods
that are able to approximate the optimal configuration and model choices, as
shown by the experimental results.
Towards an Ontology based integrated Framework for Semantic Web
Authors: Nora Y. Ibrahim, Sahar A. Mokhtar, Hany M. Harb
Date: 2013-05-30 00:00:00

This Ontologies are widely used as a means for solving the information
heterogeneity problems on the web because of their capability to provide
explicit meaning to the information. They become an efficient tool for
knowledge representation in a structured manner. There is always more than one
ontology for the same domain. Furthermore, there is no standard method for
building ontologies, and there are many ontology building tools using different
ontology languages. Because of these reasons, interoperability between the
ontologies is very low. Current ontology tools mostly use functions to build,
edit and inference the ontology. Methods for merging heterogeneous domain
ontologies are not included in most tools. This paper presents ontology merging
methodology for building a single global ontology from heterogeneous eXtensible
Markup Language (XML) data sources to capture and maintain all the knowledge
which XML data sources can contain
Predicting the Severity of Breast Masses with Data Mining Methods
Authors: Sahar A. Mokhtar, Alaa. M. Elsayad
Date: 2013-05-30 00:00:00
Tasks: Classification, General Classification, Specificity

Mammography is the most effective and available tool for breast cancer
screening. However, the low positive predictive value of breast biopsy
resulting from mammogram interpretation leads to approximately 70% unnecessary
biopsies with benign outcomes. Data mining algorithms could be used to help
physicians in their decisions to perform a breast biopsy on a suspicious lesion
seen in a mammogram image or to perform a short term follow-up examination
instead. In this research paper data mining classification algorithms; Decision
Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM)
are analyzed on mammographic masses data set. The purpose of this study is to
increase the ability of physicians to determine the severity (benign or
malignant) of a mammographic mass lesion from BI-RADS attributes and the
patient,s age. The whole data set is divided for training the models and test
them by the ratio of 70:30% respectively and the performances of classification
algorithms are compared through three statistical measures; sensitivity,
specificity, and classification accuracy. Accuracy of DT, ANN and SVM are
78.12%, 80.56% and 81.25% of test samples respectively. Our analysis shows that
out of these three classification models SVM predicts severity of breast cancer
with least error rate and highest accuracy.
Dienstplanerstellung in Krankenhaeusern mittels genetischer Algorithmen
Authors: Uwe Aickelin
Date: 2013-05-30 00:00:00
Tasks: Multiple-choice

This thesis investigates the use of problem-specific knowledge to enhance a
genetic algorithm approach to multiple-choice optimisation problems. It shows
that such information can significantly enhance performance, but that the
choice of information and the way it is included are important factors for
success.
A Local Active Contour Model for Image Segmentation with Intensity Inhomogeneity
Authors: Kaihua Zhang, Lei Zhang, Kin-Man Lam, David Zhang
Date: 2013-05-30 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

A novel locally statistical active contour model (ACM) for image segmentation
in the presence of intensity inhomogeneity is presented in this paper. The
inhomogeneous objects are modeled as Gaussian distributions of different means
and variances, and a moving window is used to map the original image into
another domain, where the intensity distributions of inhomogeneous objects are
still Gaussian but are better separated. The means of the Gaussian
distributions in the transformed domain can be adaptively estimated by
multiplying a bias field with the original signal within the window. A
statistical energy functional is then defined for each local region, which
combines the bias field, the level set function, and the constant approximating
the true signal of the corresponding object. Experiments on both synthetic and
real images demonstrate the superiority of our proposed algorithm to
state-of-the-art and representative methods.
Tweets Miner for Stock Market Analysis
Authors: Bohdan Pavlyshenko
Date: 2013-05-30 00:00:00

In this paper, we present a software package for the data mining of Twitter
microblogs for the purpose of using them for the stock market analysis. The
package is written in R langauge using apropriate R packages. The model of
tweets has been considered. We have also compared stock market charts with
frequent sets of keywords in Twitter microblogs messages.
The Expressive Power of Word Embeddings
Authors: Yanqing Chen, Bryan Perozzi, Rami Al-Rfou, Steven Skiena
Date: 2013-01-15 00:00:00
Tasks: Benchmarking, Sentence, Word Embeddings

We seek to better understand the difference in quality of the several
publicly released embeddings. We propose several tasks that help to distinguish
the characteristics of different embeddings. Our evaluation of sentiment
polarity and synonym/antonym relations shows that embeddings are able to
capture surprisingly nuanced semantics even in the absence of sentence
structure. Moreover, benchmarking the embeddings shows great variance in
quality and characteristics of the semantics captured by the tested embeddings.
Finally, we show the impact of varying the number of dimensions and the
resolution of each dimension on the effective useful features captured by the
embedding space. Our contributions highlight the importance of embeddings for
NLP tasks and the effect of their quality on the final results.
Video Human Segmentation using Fuzzy Object Models and its Application to Body Pose Estimation of Toddlers for Behavior Studies
Authors: Thiago V. Spina, Mariano Tepper, Amy Esler, Vassilios Morellas, Nikolaos Papanikolopoulos, Alexandre X. Falcão, Guillermo Sapiro
Date: 2013-05-29 00:00:00
Tasks: Object, Pose Estimation, Semantic Segmentation, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation

Video object segmentation is a challenging problem due to the presence of
deformable, connected, and articulated objects, intra- and inter-object
occlusions, object motion, and poor lighting. Some of these challenges call for
object models that can locate a desired object and separate it from its
surrounding background, even when both share similar colors and textures. In
this work, we extend a fuzzy object model, named cloud system model (CSM), to
handle video segmentation, and evaluate it for body pose estimation of toddlers
at risk of autism. CSM has been successfully used to model the parts of the
brain (cerebrum, left and right brain hemispheres, and cerebellum) in order to
automatically locate and separate them from each other, the connected brain
stem, and the background in 3D MR-images. In our case, the objects are
articulated parts (2D projections) of the human body, which can deform, cause
self-occlusions, and move along the video. The proposed CSM extension handles
articulation by connecting the individual clouds, body parts, of the system
using a 2D stickman model. The stickman representation naturally allows us to
extract 2D body pose measures of arm asymmetry patterns during unsupported gait
of toddlers, a possible behavioral marker of autism. The results show that our
method can provide insightful knowledge to assist the specialist's observations
during real in-clinic assessments.
An estimation of distribution algorithm with adaptive Gibbs sampling for unconstrained global optimization
Authors: Jonás Velasco, Mario A. Saucedo-Espinosa, Hugo Jair Escalante, Karlo Mendoza, César Emilio Villarreal-Rodríguez, Óscar L. Chacón-Mondragón, Adrián Rodríguez, Arturo Berrones
Date: 2011-07-11 00:00:00
Tasks: global-optimization

In this paper is proposed a new heuristic approach belonging to the field of
evolutionary Estimation of Distribution Algorithms (EDAs). EDAs builds a
probability model and a set of solutions is sampled from the model which
characterizes the distribution of such solutions. The main framework of the
proposed method is an estimation of distribution algorithm, in which an
adaptive Gibbs sampling is used to generate new promising solutions and, in
combination with a local search strategy, it improves the individual solutions
produced in each iteration. The Estimation of Distribution Algorithm with
Adaptive Gibbs Sampling we are proposing in this paper is called AGEDA. We
experimentally evaluate and compare this algorithm against two deterministic
procedures and several stochastic methods in three well known test problems for
unconstrained global optimization. It is empirically shown that our heuristic
is robust in problems that involve three central aspects that mainly determine
the difficulty of global optimization problems, namely high-dimensionality,
multi-modality and non-smoothness.
Rotation invariants of two dimensional curves based on iterated integrals
Authors: Joscha Diehl
Date: 2013-05-29 00:00:00
Tasks: BIG-bench Machine Learning, Vocal Bursts Valence Prediction

We introduce a novel class of rotation invariants of two dimensional curves
based on iterated integrals. The invariants we present are in some sense
complete and we describe an algorithm to calculate them, giving explicit
computations up to order six. We present an application to online
(stroke-trajectory based) character recognition. This seems to be the first
time in the literature that the use of iterated integrals of a curve is
proposed for (invariant) feature extraction in machine learning applications.
Multi-Label Classifier Chains for Bird Sound
Authors: Forrest Briggs, Xiaoli Z. Fern, Jed Irvine
Date: 2013-04-22 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Multi-Label Learning

Bird sound data collected with unattended microphones for automatic surveys,
or mobile devices for citizen science, typically contain multiple
simultaneously vocalizing birds of different species. However, few works have
considered the multi-label structure in birdsong. We propose to use an ensemble
of classifier chains combined with a histogram-of-segments representation for
multi-label classification of birdsong. The proposed method is compared with
binary relevance and three multi-instance multi-label learning (MIML)
algorithms from prior work (which focus more on structure in the sound, and
less on structure in the label sets). Experiments are conducted on two
real-world birdsong datasets, and show that the proposed method usually
outperforms binary relevance (using the same features and base-classifier), and
is better in some cases and worse in others compared to the MIML algorithms.
Unsupervised ensemble of experts (EoE) framework for automatic binarization of document images
Authors: Reza Farrahi Moghaddam, Fereydoun Farrahi Moghaddam, Mohamed Cheriet
Date: 2013-05-13 00:00:00
Tasks: Binarization

In recent years, a large number of binarization methods have been developed,
with varying performance generalization and strength against different
benchmarks. In this work, to leverage on these methods, an ensemble of experts
(EoE) framework is introduced, to efficiently combine the outputs of various
methods. The proposed framework offers a new selection process of the
binarization methods, which are actually the experts in the ensemble, by
introducing three concepts: confidentness, endorsement and schools of experts.
The framework, which is highly objective, is built based on two general
principles: (i) consolidation of saturated opinions and (ii) identification of
schools of experts. After building the endorsement graph of the ensemble for an
input document image based on the confidentness of the experts, the saturated
opinions are consolidated, and then the schools of experts are identified by
thresholding the consolidated endorsement graph. A variation of the framework,
in which no selection is made, is also introduced that combines the outputs of
all experts using endorsement-dependent weights. The EoE framework is evaluated
on the set of participating methods in the H-DIBCO'12 contest and also on an
ensemble generated from various instances of grid-based Sauvola method with
promising performance.
Active Sensing as Bayes-Optimal Sequential Decision Making
Authors: Sheeraz Ahmad, Angela J. Yu
Date: 2013-05-28 00:00:00
Tasks: Decision Making, Sensitivity, Sequential Decision Making

Sensory inference under conditions of uncertainty is a major problem in both
machine learning and computational neuroscience. An important but poorly
understood aspect of sensory processing is the role of active sensing. Here, we
present a Bayes-optimal inference and control framework for active sensing,
C-DAC (Context-Dependent Active Controller). Unlike previously proposed
algorithms that optimize abstract statistical objectives such as information
maximization (Infomax) [Butko & Movellan, 2010] or one-step look-ahead accuracy
[Najemnik & Geisler, 2005], our active sensing model directly minimizes a
combination of behavioral costs, such as temporal delay, response error, and
effort. We simulate these algorithms on a simple visual search task to
illustrate scenarios in which context-sensitivity is particularly beneficial
and optimization with respect to generic statistical objectives particularly
inadequate. Motivated by the geometric properties of the C-DAC policy, we
present both parametric and non-parametric approximations, which retain
context-sensitivity while significantly reducing computational complexity.
These approximations enable us to investigate the more complex problem
involving peripheral vision, and we notice that the difference between C-DAC
and statistical policies becomes even more evident in this scenario.
Normalized Online Learning
Authors: Stephane Ross, Paul Mineiro, John Langford
Date: 2013-05-28 00:00:00

We introduce online learning algorithms which are independent of feature
scales, proving regret bounds dependent on the ratio of scales existent in the
data rather than the absolute scale. This has several useful effects: there is
no need to pre-normalize data, the test-time and test-space complexity are
reduced, and the algorithms are more robust.
Reinforcement Learning for the Soccer Dribbling Task
Authors: Arthur Carvalho, Renato Oliveira
Date: 2013-05-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a reinforcement learning solution to the \emph{soccer dribbling
task}, a scenario in which a soccer agent has to go from the beginning to the
end of a region keeping possession of the ball, as an adversary attempts to
gain possession. While the adversary uses a stationary policy, the dribbler
learns the best action to take at each decision point. After defining
meaningful variables to represent the state space, and high-level macro-actions
to incorporate domain knowledge, we describe our application of the
reinforcement learning algorithm \emph{Sarsa} with CMAC for function
approximation. Our experiments show that, after the training period, the
dribbler is able to accomplish its task against a strong adversary around 58%
of the time.
A Cooperative Coevolutionary Genetic Algorithm for Learning Bayesian Network Structures
Authors: Arthur Carvalho
Date: 2013-05-28 00:00:00

We propose a cooperative coevolutionary genetic algorithm for learning
Bayesian network structures from fully observable data sets. Since this problem
can be decomposed into two dependent subproblems, that is to find an ordering
of the nodes and an optimal connectivity matrix, our algorithm uses two
subpopulations, each one representing a subtask. We describe the empirical
results obtained with simulations of the Alarm and Insurance networks. We show
that our algorithm outperforms the deterministic algorithm K2.