pwc-archive/papers-with-abstracts huggingface dataset:
Bypassing Captcha By Machine A Proof For Passing The Turing Test
Authors: Ahmad B. A. Hassanat
Date: 2014-09-03 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

For the last ten years, CAPTCHAs have been widely used by websites to prevent
their data being automatically updated by machines. By supposedly allowing only
humans to do so, CAPTCHAs take advantage of the reverse Turing test (TT),
knowing that humans are more intelligent than machines. Generally, CAPTCHAs
have defeated machines, but things are changing rapidly as technology improves.
Hence, advanced research into optical character recognition (OCR) is overtaking
attempts to strengthen CAPTCHAs against machine-based attacks. This paper
investigates the immunity of CAPTCHA, which was built on the failure of the TT.
We show that some CAPTCHAs are easily broken using a simple OCR machine built
for the purpose of this study. By reviewing other techniques, we show that even
more difficult CAPTCHAs can be broken using advanced OCR machines. Current
advances in OCR should enable machines to pass the TT in the image recognition
domain, which is exactly where machines are seeking to overcome CAPTCHAs. We
enhance traditional CAPTCHAs by employing not only characters, but also natural
language and multiple objects within the same CAPTCHA. The proposed CAPTCHAs
might be able to hold out against machines, at least until the advent of a
machine that passes the TT completely.
Visual Passwords Using Automatic Lip Reading
Authors: Ahmad Basheer Hassanat
Date: 2014-09-02 00:00:00
Tasks: Lip Reading

This paper presents a visual passwords system to increase security. The
system depends mainly on recognizing the speaker using the visual speech signal
alone. The proposed scheme works in two stages: setting the visual password
stage and the verification stage. At the setting stage the visual passwords
system request the user to utter a selected password, a video recording of the
user face is captured, and processed by a special words-based VSR system which
extracts a sequence of feature vectors. In the verification stage, the same
procedure is executed, the features will be sent to be compared with the stored
visual password. The proposed scheme has been evaluated using a video database
of 20 different speakers (10 females and 10 males), and 15 more males in
another video database with different experiment sets. The evaluation has
proved the system feasibility, with average error rate in the range of 7.63% to
20.51% at the worst tested scenario, and therefore, has potential to be a
practical approach with the support of other conventional authentication
methods such as the use of usernames and passwords.
Dimensionality Invariant Similarity Measure
Authors: Ahmad Basheer Hassanat
Date: 2014-09-02 00:00:00

This paper presents a new similarity measure to be used for general tasks
including supervised learning, which is represented by the K-nearest neighbor
classifier (KNN). The proposed similarity measure is invariant to large
differences in some dimensions in the feature space. The proposed metric is
proved mathematically to be a metric. To test its viability for different
applications, the KNN used the proposed metric for classifying test examples
chosen from a number of real datasets. Compared to some other well known
metrics, the experimental results show that the proposed metric is a promising
distance measure for the KNN classifier with strong potential for a wide range
of applications.
Solving the Problem of the K Parameter in the KNN Classifier Using an Ensemble Learning Approach
Authors: Ahmad Basheer Hassanat, Mohammad Ali Abbadi, Ghada Awad Altarawneh, Ahmad Ali Alhasanat
Date: 2014-09-02 00:00:00
Tasks: Ensemble Learning

This paper presents a new solution for choosing the K parameter in the
k-nearest neighbor (KNN) algorithm, the solution depending on the idea of
ensemble learning, in which a weak KNN classifier is used each time with a
different K, starting from one to the square root of the size of the training
set. The results of the weak classifiers are combined using the weighted sum
rule. The proposed solution was tested and compared to other solutions using a
group of experiments in real life problems. The experimental results show that
the proposed classifier outperforms the traditional KNN classifier that uses a
different number of neighbors, is competitive with other classifiers, and is a
promising classifier with strong potential for a wide range of applications.
Deformation corrected compressed sensing (DC-CS): a novel framework for accelerated dynamic MRI
Authors: Sajan Goud Lingala, Edward DiBella, Mathews Jacob
Date: 2014-05-29 00:00:00
Tasks: compressed sensing, Denoising

We propose a novel deformation corrected compressed sensing (DC-CS) framework
to recover dynamic magnetic resonance images from undersampled measurements. We
introduce a generalized formulation that is capable of handling a wide class of
sparsity/compactness priors on the deformation corrected dynamic signal. In
this work, we consider example compactness priors such as sparsity in temporal
Fourier domain, sparsity in temporal finite difference domain, and nuclear norm
penalty to exploit low rank structure. Using variable splitting, we decouple
the complex optimization problem to simpler and well understood sub problems;
the resulting algorithm alternates between simple steps of shrinkage based
denoising, deformable registration, and a quadratic optimization step.
Additionally, we employ efficient continuation strategies to minimize the risk
of convergence to local minima. The proposed formulation contrasts with
existing DC-CS schemes that are customized for free breathing cardiac cine
applications, and other schemes that rely on fully sampled reference frames or
navigator signals to estimate the deformation parameters. The efficient
decoupling enabled by the proposed scheme allows its application to a wide
range of applications including contrast enhanced dynamic MRI. Through
experiments on numerical phantom and in vivo myocardial perfusion MRI datasets,
we demonstrate the utility of the proposed DC-CS scheme in providing robust
reconstructions with reduced motion artifacts over classical compressed sensing
schemes that utilize the compact priors on the original deformation
un-corrected signal.
An Approach for Text Steganography Based on Markov Chains
Authors: H. Hernan Moraldo
Date: 2014-09-02 00:00:00
Tasks: Language Modeling, Language Modelling

A text steganography method based on Markov chains is introduced, together
with a reference implementation. This method allows for information hiding in
texts that are automatically generated following a given Markov model. Other
Markov - based systems of this kind rely on big simplifications of the language
model to work, which produces less natural looking and more easily detectable
texts. The method described here is designed to generate texts within a good
approximation of the original language model provided.
Action Recognition in the Frequency Domain
Authors: Anh Tran, Jinyan Guan, Thanima Pilantanakitti, Paul Cohen
Date: 2014-09-02 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Temporal Action Localization

In this paper, we describe a simple strategy for mitigating variability in
temporal data series by shifting focus onto long-term, frequency domain
features that are less susceptible to variability. We apply this method to the
human action recognition task and demonstrate how working in the frequency
domain can yield good recognition features for commonly used optical flow and
articulated pose features, which are highly sensitive to small differences in
motion, viewpoint, dynamic backgrounds, occlusion and other sources of
variability. We show how these frequency-based features can be used in
combination with a simple forest classifier to achieve good and robust results
on the popular KTH Actions dataset.
Empirical Evaluation of Tree distances for Parser Evaluation
Authors: Taraka Rama
Date: 2014-09-01 00:00:00

In this empirical study, I compare various tree distance measures --
originally developed in computational biology for the purpose of tree
comparison -- for the purpose of parser evaluation. I will control for the
parser setting by comparing the automatically generated parse trees from the
state-of-the-art parser Charniak, 2000) with the gold-standard parse trees. The
article describes two different tree distance measures (RF and QD) along with
its variants (GRF and GQD) for the purpose of parser evaluation. The article
will argue that RF measure captures similar information as the standard EvalB
metric (Sekine and Collins, 1997) and the tree edit distance (Zhang and Shasha,
1989) applied by Tsarfaty et al. (2011). Finally, the article also provides
empirical evidence by reporting high correlations between the different tree
distances and EvalB metric's scores.
New Ideas for Brain Modelling 2
Authors: Kieran Greer
Date: 2014-08-23 00:00:00

This paper describes a relatively simple way of allowing a brain model to
self-organise its concept patterns through nested structures. For a simulation,
time reduction is helpful and it would be able to show how patterns may form
and then fire in sequence, as part of a search or thought process. It uses a
very simple equation to show how the inhibitors in particular, can switch off
certain areas, to allow other areas to become the prominent ones and thereby
define the current brain state. This allows for a small amount of control over
what appears to be a chaotic structure inside of the brain. It is attractive
because it is still mostly mechanical and therefore can be added as an
automatic process, or the modelling of that. The paper also describes how the
nested pattern structure can be used as a basic counting mechanism. Another
mathematical conclusion provides a basis for maintaining memory or concept
patterns. The self-organisation can space itself through automatic processes.
This might allow new neurons to be added in a more even manner and could help
to maintain the concept integrity. The process might also help with finding
memory structures afterwards. This extended version integrates further with the
existing cognitive model and provides some new conclusions.
Deontic modality based on preference
Authors: Daniel Osherson, Scott Weinstein
Date: 2014-09-02 00:00:00
Tasks: Relation

Deontic modalities are here defined in terms of the preference relation
explored in our previous work (Osherson and Weinstein, 2012). Some consequences
of the system are discussed.
CoMOGrad and PHOG: From Computer Vision to Fast and Accurate Protein Tertiary Structure Retrieval
Authors: Rezaul Karim, Mohd. Momin Al Aziz, Swakkhar Shatabda, M. Sohel Rahman, Md. Abul Kashem Mia, Farhana Zaman, Salman Rakin
Date: 2014-09-02 00:00:00
Tasks: Retrieval

Due to the advancements in technology number of entries in the structural
database of proteins are increasing day by day. Methods for retrieving protein
tertiary structures from this large database is the key to comparative analysis
of structures which plays an important role to understand proteins and their
function. In this paper, we present fast and accurate methods for the retrieval
of proteins from a large database with tertiary structures similar to a query
protein. Our proposed methods borrow ideas from the field of computer vision.
The speed and accuracy of our methods comes from the two newly introduced
features, the co-occurrence matrix of the oriented gradient and pyramid
histogram of oriented gradient and from the use of Euclidean distance as the
distance measure. Experimental results clearly indicate the superiority of our
approach in both running time and accuracy. Our method is readily available for
use from this website: http://research.buet.ac.bd:8080/Comograd/.
Axiomatic Construction of Hierarchical Clustering in Asymmetric Networks
Authors: Gunnar Carlsson, Facundo Mémoli, Alejandro Ribeiro, Santiago Segarra
Date: 2013-01-31 00:00:00
Tasks: Clustering

This paper considers networks where relationships between nodes are
represented by directed dissimilarities. The goal is to study methods for the
determination of hierarchical clusters, i.e., a family of nested partitions
indexed by a connectivity parameter, induced by the given dissimilarity
structures. Our construction of hierarchical clustering methods is based on
defining admissible methods to be those methods that abide by the axioms of
value - nodes in a network with two nodes are clustered together at the maximum
of the two dissimilarities between them - and transformation - when
dissimilarities are reduced, the network may become more clustered but not
less. Several admissible methods are constructed and two particular methods,
termed reciprocal and nonreciprocal clustering, are shown to provide upper and
lower bounds in the space of admissible methods. Alternative clustering
methodologies and axioms are further considered. Allowing the outcome of
hierarchical clustering to be asymmetric, so that it matches the asymmetry of
the original data, leads to the inception of quasi-clustering methods. The
existence of a unique quasi-clustering method is shown. Allowing clustering in
a two-node network to proceed at the minimum of the two dissimilarities
generates an alternative axiomatic construction. There is a unique clustering
method in this case too. The paper also develops algorithms for the computation
of hierarchical clusters using matrix powers on a min-max dioid algebra and
studies the stability of the methods proposed. We proved that most of the
methods introduced in this paper are such that similar networks yield similar
hierarchical clustering results. Algorithms are exemplified through their
application to networks describing internal migration within states of the
United States (U.S.) and the interrelation between sectors of the U.S. economy.
Feature Engineering for Map Matching of Low-Sampling-Rate GPS Trajectories in Road Network
Authors: Jian Yang, Liqiu Meng
Date: 2014-09-02 00:00:00
Tasks: Feature Engineering

Map matching of GPS trajectories from a sequence of noisy observations serves
the purpose of recovering the original routes in a road network. In this work
in progress, we attempt to share our experience of feature construction in a
spatial database by reporting our ongoing experiment of feature extrac-tion in
Conditional Random Fields (CRFs) for map matching. Our preliminary results are
obtained from real-world taxi GPS trajectories.
Feature Selection in Conditional Random Fields for Map Matching of GPS Trajectories
Authors: Jian Yang, Liqiu Meng
Date: 2014-09-02 00:00:00
Tasks: feature selection

Map matching of the GPS trajectory serves the purpose of recovering the
original route on a road network from a sequence of noisy GPS observations. It
is a fundamental technique to many Location Based Services. However, map
matching of a low sampling rate on urban road network is still a challenging
task. In this paper, the characteristics of Conditional Random Fields with
regard to inducing many contextual features and feature selection are explored
for the map matching of the GPS trajectories at a low sampling rate.
Experiments on a taxi trajectory dataset show that our method may achieve
competitive results along with the success of reducing model complexity for
computation-limited applications.
Ensemble Learning of Colorectal Cancer Survival Rates
Authors: Chris Roadknight, Uwe Aickelin, John Scholefield, Lindy Durrant
Date: 2014-09-02 00:00:00
Tasks: Clustering, Ensemble Learning, General Classification, Prognosis, Survival Prediction, Tumour Classification

In this paper, we describe a dataset relating to cellular and physical
conditions of patients who are operated upon to remove colorectal tumours. This
data provides a unique insight into immunological status at the point of tumour
removal, tumour classification and post-operative survival. We build on
existing research on clustering and machine learning facets of this data to
demonstrate a role for an ensemble approach to highlighting patients with
clearer prognosis parameters. Results for survival prediction using 3 different
approaches are shown for a subset of the data which is most difficult to model.
The performance of each model individually is compared with subsets of the data
where some agreement is reached for multiple models. Significant improvements
in model accuracy on an unseen test set can be achieved for patients where
agreement between models is achieved.
Feature selection in detection of adverse drug reactions from the Health Improvement Network (THIN) database
Authors: Yihui Liu, Uwe Aickelin
Date: 2014-09-02 00:00:00
Tasks: feature selection

Adverse drug reaction (ADR) is widely concerned for public health issue. ADRs
are one of most common causes to withdraw some drugs from market. Prescription
event monitoring (PEM) is an important approach to detect the adverse drug
reactions. The main problem to deal with this method is how to automatically
extract the medical events or side effects from high-throughput medical events,
which are collected from day to day clinical practice. In this study we propose
a novel concept of feature matrix to detect the ADRs. Feature matrix, which is
extracted from big medical data from The Health Improvement Network (THIN)
database, is created to characterize the medical events for the patients who
take drugs. Feature matrix builds the foundation for the irregular and big
medical data. Then feature selection methods are performed on feature matrix to
detect the significant features. Finally the ADRs can be located based on the
significant features. The experiments are carried out on three drugs:
Atorvastatin, Alendronate, and Metoclopramide. Major side effects for each drug
are detected and better performance is achieved compared to other computerized
methods. The detected ADRs are based on computerized methods, further
investigation is needed.
Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs
Authors: Jenna M. Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard
Date: 2014-09-02 00:00:00
Tasks: Pharmacovigilance, Specificity

Background: Children are frequently prescribed medication off-label, meaning
there has not been sufficient testing of the medication to determine its safety
or effectiveness. The main reason this safety knowledge is lacking is due to
ethical restrictions that prevent children from being included in the majority
of clinical trials. Objective: The objective of this paper is to investigate
whether an ensemble of simple study designs can be implemented to signal
acutely occurring side effects effectively within the paediatric population by
using historical longitudinal data. The majority of pharmacovigilance
techniques are unsupervised, but this research presents a supervised framework.
Methods: Multiple measures of association are calculated for each drug and
medical event pair and these are used as features that are fed into a
classiffier to determine the likelihood of the drug and medical event pair
corresponding to an adverse drug reaction. The classiffier is trained using
known adverse drug reactions or known non-adverse drug reaction relationships.
Results: The novel ensemble framework obtained a false positive rate of 0:149,
a sensitivity of 0:547 and a specificity of 0:851 when implemented on a
reference set of drug and medical event pairs. The novel framework consistently
outperformed each individual simple study design. Conclusion: This research
shows that it is possible to exploit the mechanism of causality and presents a
framework for signalling adverse drug reactions effectively.
A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery
Authors: Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard
Date: 2014-09-02 00:00:00
Tasks: Clustering, Marketing, Metric Learning

Drugs are frequently prescribed to patients with the aim of improving each
patient's medical state, but an unfortunate consequence of most prescription
drugs is the occurrence of undesirable side effects. Side effects that occur in
more than one in a thousand patients are likely to be signalled efficiently by
current drug surveillance methods, however, these same methods may take decades
before generating signals for rarer side effects, risking medical morbidity or
mortality in patients prescribed the drug while the rare side effect is
undiscovered. In this paper we propose a novel computational meta-analysis
framework for signalling rare side effects that integrates existing methods,
knowledge from the web, metric learning and semi-supervised clustering. The
novel framework was able to signal many known rare and serious side effects for
the selection of drugs investigated, such as tendon rupture when prescribed
Ciprofloxacin or Levofloxacin, renal failure with Naproxen and depression
associated with Rimonabant. Furthermore, for the majority of the drug
investigated it generated signals for rare side effects at a more stringent
signalling threshold than existing methods and shows the potential to become a
fundamental part of post marketing surveillance to detect rare side effects.
Data classification using the Dempster-Shafer method
Authors: Qi Chen, Amanda Whitbrook, Uwe Aickelin, Chris Roadknight
Date: 2014-09-02 00:00:00
Tasks: Attribute, Classification, General Classification

In this paper, the Dempster-Shafer method is employed as the theoretical
basis for creating data classification systems. Testing is carried out using
three popular (multiple attribute) benchmark datasets that have two, three and
four classes. In each case, a subset of the available data is used for training
to establish thresholds, limits or likelihoods of class membership for each
attribute, and hence create mass functions that establish probability of class
membership for each attribute of the test data. Classification of each data
item is achieved by combination of these probabilities via Dempster's Rule of
Combination. Results for the first two datasets show extremely high
classification accuracy that is competitive with other popular methods. The
third dataset is non-numerical and difficult to classify, but good results can
be achieved provided the system and mass functions are designed carefully and
the right attributes are chosen for combination. In all cases the
Dempster-Shafer method provides comparable performance to other more popular
algorithms, but the overhead of generating accurate mass functions increases
the complexity with the addition of new attributes. Overall, the results
suggest that the D-S approach provides a suitable framework for the design of
classification systems and that automating the mass function design and
calculation would increase the viability of the algorithm for complex
classification problems.
Image Retrieval And Classification Using Local Feature Vectors
Authors: Vikas Verma
Date: 2014-09-02 00:00:00
Tasks: Classification, Content-Based Image Retrieval, General Classification, image-classification, Image Classification, Image Retrieval, Information Retrieval, Meta-Learning, Retrieval

Content Based Image Retrieval(CBIR) is one of the important subfield in the
field of Information Retrieval. The goal of a CBIR algorithm is to retrieve
semantically similar images in response to a query image submitted by the end
user. CBIR is a hard problem because of the phenomenon known as $\textit
{semantic gap}$.
  In this thesis, we aim at analyzing the performance of a CBIR system build
using local feature vectors and Intermediate Matching Kernel. We also propose a
Two-Step Matching process for reducing the response time of the CBIR systems.
Further, we develop a Meta-Learning framework for improving the retrieval
performance of these systems. Our results show that the Two-Step Matching
process significantly reduces response time and the Meta-Learning Framework
improves the retrieval performance by more than two fold. We also analyze the
performance of various image classification systems that use different image
representations constructed from the local feature vectors.