pwc-archive/papers-with-abstracts huggingface dataset:
Using MathML to Represent Units of Measurement for Improved Ontology Alignment
Authors: Chau Do, Eric J. Pauwels
Date: 2013-07-05 00:00:00

Ontologies provide a formal description of concepts and their relationships
in a knowledge domain. The goal of ontology alignment is to identify
semantically matching concepts and relationships across independently developed
ontologies that purport to describe the same knowledge. In order to handle the
widest possible class of ontologies, many alignment algorithms rely on
terminological and structural meth- ods, but the often fuzzy nature of concepts
complicates the matching process. However, one area that should provide clear
matching solutions due to its mathematical nature, is units of measurement.
Several on- tologies for units of measurement are available, but there has been
no attempt to align them, notwithstanding the obvious importance for tech-
nical interoperability. We propose a general strategy to map these (and
similar) ontologies by introducing MathML to accurately capture the semantic
description of concepts specified therein. We provide mapping results for three
ontologies, and show that our approach improves on lexical comparisons.
A Sub-block Based Image Retrieval Using Modified Integrated Region Matching
Authors: E. R. Vimina, K. Poulose Jacob
Date: 2013-07-05 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval

This paper proposes a content based image retrieval (CBIR) system using the
local colour and texture features of selected image sub-blocks and global
colour and shape features of the image. The image sub-blocks are roughly
identified by segmenting the image into partitions of different configuration,
finding the edge density in each partition using edge thresholding followed by
morphological dilation. The colour and texture features of the identified
regions are computed from the histograms of the quantized HSV colour space and
Gray Level Co- occurrence Matrix (GLCM) respectively. The colour and texture
feature vectors is computed for each region. The shape features are computed
from the Edge Histogram Descriptor (EHD). A modified Integrated Region Matching
(IRM) algorithm is used for finding the minimum distance between the sub-blocks
of the query and target image. Experimental results show that the proposed
method provides better retrieving result than retrieval using some of the
existing methods.
Towards Combining HTN Planning and Geometric Task Planning
Authors: Lavindra de Silva, Amit Kumar Pandey, Mamoun Gharbi, Rachid Alami
Date: 2013-07-04 00:00:00
Tasks: Task Planning

In this paper we present an interface between a symbolic planner and a
geometric task planner, which is different to a standard trajectory planner in
that the former is able to perform geometric reasoning on abstract
entities---tasks. We believe that this approach facilitates a more principled
interface to symbolic planning, while also leaving more room for the geometric
planner to make independent decisions. We show how the two planners could be
interfaced, and how their planning and backtracking could be interleaved. We
also provide insights for a methodology for using the combined system, and
experimental results to use as a benchmark with future extensions to both the
combined system, as well as to the geometric task planner.
Toward Guaranteed Illumination Models for Non-Convex Objects
Authors: Yuqian Zhang, Cun Mu, Han-Wen Kuo, John Wright
Date: 2013-07-04 00:00:00
Tasks: Dimensionality Reduction, object-detection, Object Detection

Illumination variation remains a central challenge in object detection and
recognition. Existing analyses of illumination variation typically pertain to
convex, Lambertian objects, and guarantee quality of approximation in an
average case sense. We show that it is possible to build V(vertex)-description
convex cone models with worst-case performance guarantees, for non-convex
Lambertian objects. Namely, a natural verification test based on the angle to
the constructed cone guarantees to accept any image which is sufficiently
well-approximated by an image of the object under some admissible lighting
condition, and guarantees to reject any image that does not have a sufficiently
good approximation. The cone models are generated by sampling point
illuminations with sufficient density, which follows from a new perturbation
bound for point images in the Lambertian model. As the number of point images
required for guaranteed verification may be large, we introduce a new
formulation for cone preserving dimensionality reduction, which leverages tools
from sparse and low-rank decomposition to reduce the complexity, while
controlling the approximation error with respect to the original cone.
Discovering Sequential Patterns in a UK General Practice Database
Authors: Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard
Date: 2013-07-04 00:00:00

The wealth of computerised medical information becoming readily available
presents the opportunity to examine patterns of illnesses, therapies and
responses. These patterns may be able to predict illnesses that a patient is
likely to develop, allowing the implementation of preventative actions. In this
paper sequential rule mining is applied to a General Practice database to find
rules involving a patients age, gender and medical history. By incorporating
these rules into current health-care a patient can be highlighted as
susceptible to a future illness based on past or current illnesses, gender and
year of birth. This knowledge has the ability to greatly improve health-care
and reduce health-care costs.
Detect adverse drug reactions for drug Alendronate
Authors: Yihui Liu, Uwe Aickelin
Date: 2013-07-04 00:00:00
Tasks: feature selection

Adverse drug reaction (ADR) is widely concerned for public health issue. In
this study we propose an original approach to detect the ADRs using feature
matrix and feature selection. The experiments are carried out on the drug
Simvastatin. Major side effects for the drug are detected and better
performance is achieved compared to other computerized methods. The detected
ADRs are based on the computerized method, further investigation is needed.
Quiet in Class: Classification, Noise and the Dendritic Cell Algorithm
Authors: Feng Gu, Jan Feyereisl, Robert Oates, Jenna Reps, Julie Greensmith, Uwe Aickelin
Date: 2013-07-04 00:00:00
Tasks: Anomaly Detection, Classification, General Classification

Theoretical analyses of the Dendritic Cell Algorithm (DCA) have yielded
several criticisms about its underlying structure and operation. As a result,
several alterations and fixes have been suggested in the literature to correct
for these findings. A contribution of this work is to investigate the effects
of replacing the classification stage of the DCA (which is known to be flawed)
with a traditional machine learning technique. This work goes on to question
the merits of those unique properties of the DCA that are yet to be thoroughly
analysed. If none of these properties can be found to have a benefit over
traditional approaches, then "fixing" the DCA is arguably less efficient than
simply creating a new algorithm. This work examines the dynamic filtering
property of the DCA and questions the utility of this unique feature for the
anomaly detection problem. It is found that this feature, while advantageous
for noisy, time-ordered classification, is not as useful as a traditional
static filter for processing a synthetic dataset. It is concluded that there
are still unique features of the DCA left to investigate. Areas that may be of
benefit to the Artificial Immune Systems community are suggested.
Introducing Memory and Association Mechanism into a Biologically Inspired Visual Model
Authors: Qiao Hong, Li Yinlin, Tang Tang, Wang Peng
Date: 2013-07-04 00:00:00
Tasks: Object Recognition, Position

A famous biologically inspired hierarchical model firstly proposed by
Riesenhuber and Poggio has been successfully applied to multiple visual
recognition tasks. The model is able to achieve a set of position- and
scale-tolerant recognition, which is a central problem in pattern recognition.
In this paper, based on some other biological experimental results, we
introduce the Memory and Association Mechanisms into the above biologically
inspired model. The main motivations of the work are (a) to mimic the active
memory and association mechanism and add the 'top down' adjustment to the above
biologically inspired hierarchical model and (b) to build up an algorithm which
can save the space and keep a good recognition performance. The new model is
also applied to object recognition processes. The primary experimental results
show that our method is efficient with much less memory requirement.
Examining the Classification Accuracy of TSVMs with ?Feature Selection in Comparison with the GLAD Algorithm
Authors: Hala Helmi, Jon M. Garibaldi, Uwe Aickelin
Date: 2013-07-04 00:00:00
Tasks: Classification, Diagnostic, feature selection, General Classification

Gene expression data sets are used to classify and predict patient diagnostic
categories. As we know, it is extremely difficult and expensive to obtain gene
expression labelled examples. Moreover, conventional supervised approaches
cannot function properly when labelled data (training examples) are
insufficient using Support Vector Machines (SVM) algorithms. Therefore, in this
paper, we suggest Transductive Support Vector Machines (TSVMs) as
semi-supervised learning algorithms, learning with both labelled samples data
and unlabelled samples to perform the classification of microarray data. To
prune the superfluous genes and samples we used a feature selection method
called Recursive Feature Elimination (RFE), which is supposed to enhance the
output of classification and avoid the local optimization problem. We examined
the classification prediction accuracy of the TSVM-RFE algorithm in comparison
with the Genetic Learning Across Datasets (GLAD) algorithm, as both are
semi-supervised learning methods. Comparing these two methods, we found that
the TSVM-RFE surpassed both a SVM using RFE and GLAD.
Creating Personalised Energy Plans. From Groups to Individuals using Fuzzy C Means Clustering
Authors: Ian Dent, Christian Wagner, Uwe Aickelin, Tom Rodden
Date: 2013-07-04 00:00:00
Tasks: Clustering, Marketing

Changes in the UK electricity market mean that domestic users will be
required to modify their usage behaviour in order that supplies can be
maintained. Clustering allows usage profiles collected at the household level
to be clustered into groups and assigned a stereotypical profile which can be
used to target marketing campaigns. Fuzzy C Means clustering extends this by
allowing each household to be a member of many groups and hence provides the
opportunity to make personalised offers to the household dependent on their
degree of membership of each group. In addition, feedback can be provided on
how user's changing behaviour is moving them towards more "green" or cost
effective stereotypical usage.
The Application of a Data Mining Framework to Energy Usage Profiling in Domestic Residences using UK data
Authors: Ian Dent, Uwe Aickelin, Tom Rodden
Date: 2013-07-04 00:00:00
Tasks: Clustering

This paper describes a method for defining representative load profiles for
domestic electricity users in the UK. It considers bottom up and clustering
methods and then details the research plans for implementing and improving
existing framework approaches based on the overall usage profile. The work
focuses on adapting and applying analysis framework approaches to UK energy
data in order to determine the effectiveness of creating a few (single figures)
archetypical users with the intention of improving on the current methods of
determining usage profiles. The work is currently in progress and the paper
details initial results using data collected in Milton Keynes around 1990.
Various possible enhancements to the work are considered including a split
based on temperature to reflect the varying UK weather conditions.
Image registration with sparse approximations in parametric dictionaries
Authors: Alhussein Fawzi, Pascal Frossard
Date: 2013-01-28 00:00:00
Tasks: Image Registration

We examine in this paper the problem of image registration from the new
perspective where images are given by sparse approximations in parametric
dictionaries of geometric functions. We propose a registration algorithm that
looks for an estimate of the global transformation between sparse images by
examining the set of relative geometrical transformations between the
respective features. We propose a theoretical analysis of our registration
algorithm and we derive performance guarantees based on two novel important
properties of redundant dictionaries, namely the robust linear independence and
the transformation inconsistency. We propose several illustrations and insights
about the importance of these dictionary properties and show that common
properties such as coherence or restricted isometry property fail to provide
sufficient information in registration problems. We finally show with
illustrative experiments on simple visual objects and handwritten digits images
that our algorithm outperforms baseline competitor methods in terms of
transformation-invariant distance computation and classification.
Further results on dissimilarity spaces for hyperspectral images RF-CBIR
Authors: Miguel Angel Veganzones, Mihai Datcu, Manuel Graña
Date: 2013-07-04 00:00:00
Tasks: BIG-bench Machine Learning, Content-Based Image Retrieval, Image Retrieval, Retrieval

Content-Based Image Retrieval (CBIR) systems are powerful search tools in
image databases that have been little applied to hyperspectral images.
Relevance feedback (RF) is an iterative process that uses machine learning
techniques and user's feedback to improve the CBIR systems performance. We
pursued to expand previous research in hyperspectral CBIR systems built on
dissimilarity functions defined either on spectral and spatial features
extracted by spectral unmixing techniques, or on dictionaries extracted by
dictionary-based compressors. These dissimilarity functions were not suitable
for direct application in common machine learning techniques. We propose to use
a RF general approach based on dissimilarity spaces which is more appropriate
for the application of machine learning algorithms to the hyperspectral
RF-CBIR. We validate the proposed RF method for hyperspectral CBIR systems over
a real hyperspectral dataset.
Evidence and plausibility in neighborhood structures
Authors: Johan van Benthem, David Fernández-Duque, Eric Pacuit
Date: 2013-07-04 00:00:00

The intuitive notion of evidence has both semantic and syntactic features. In
this paper, we develop an {\em evidence logic} for epistemic agents faced with
possibly contradictory evidence from different sources. The logic is based on a
neighborhood semantics, where a neighborhood $N$ indicates that the agent has
reason to believe that the true state of the world lies in $N$. Further notions
of relative plausibility between worlds and beliefs based on the latter
ordering are then defined in terms of this evidence structure, yielding our
intended models for evidence-based beliefs. In addition, we also consider a
second more general flavor, where belief and plausibility are modeled using
additional primitive relations, and we prove a representation theorem showing
that each such general model is a $p$-morphic image of an intended one. This
semantics invites a number of natural special cases, depending on how uniform
we make the evidence sets, and how coherent their total structure. We give a
structural study of the resulting `uniform' and `flat' models. Our main result
are sound and complete axiomatizations for the logics of all four major model
classes with respect to the modal language of evidence, belief and safe belief.
We conclude with an outlook toward logics for the dynamics of changing
evidence, and the resulting language extensions and connections with logics of
plausibility change.
Constructing Hierarchical Image-tags Bimodal Representations for Word Tags Alternative Choice
Authors: Fangxiang Feng, Ruifan Li, Xiaojie Wang
Date: 2013-07-04 00:00:00
Tasks: TAG

This paper describes our solution to the multi-modal learning challenge of
ICML. This solution comprises constructing three-level representations in three
consecutive stages and choosing correct tag words with a data-specific
strategy. Firstly, we use typical methods to obtain level-1 representations.
Each image is represented using MPEG-7 and gist descriptors with additional
features released by the contest organizers. And the corresponding word tags
are represented by bag-of-words model with a dictionary of 4000 words.
Secondly, we learn the level-2 representations using two stacked RBMs for each
modality. Thirdly, we propose a bimodal auto-encoder to learn the
similarities/dissimilarities between the pairwise image-tags as level-3
representations. Finally, during the test phase, based on one observation of
the dataset, we come up with a data-specific strategy to choose the correct tag
words leading to a leap of an improved overall performance. Our final average
accuracy on the private test set is 100%, which ranks the first place in this
challenge.
AdaBoost and Forward Stagewise Regression are First-Order Convex Optimization Methods
Authors: Robert M. Freund, Paul Grigas, Rahul Mazumder
Date: 2013-07-04 00:00:00
Tasks: regression

Boosting methods are highly popular and effective supervised learning methods
which combine weak learners into a single accurate model with good statistical
performance. In this paper, we analyze two well-known boosting methods,
AdaBoost and Incremental Forward Stagewise Regression (FS$_\varepsilon$), by
establishing their precise connections to the Mirror Descent algorithm, which
is a first-order method in convex optimization. As a consequence of these
connections we obtain novel computational guarantees for these boosting
methods. In particular, we characterize convergence bounds of AdaBoost, related
to both the margin and log-exponential loss function, for any step-size
sequence. Furthermore, this paper presents, for the first time, precise
computational complexity results for FS$_\varepsilon$.
Demosaicing and Superresolution for Color Filter Array via Residual Image Reconstruction and Sparse Representation
Authors: Guangling Sun
Date: 2012-09-29 00:00:00
Tasks: Demosaicking, Image Reconstruction

A framework of demosaicing and superresolution for color filter array (CFA)
via residual image reconstruction and sparse representation is presented.Given
the intermediate image produced by certain demosaicing and interpolation
technique, a residual image between the final reconstruction image and the
intermediate image is reconstructed using sparse representation.The final
reconstruction image has richer edges and details than that of the intermediate
image. Specifically, a generic dictionary is learned from a large set of
composite training data composed of intermediate data and residual data. The
learned dictionary implies a mapping between the two data. A specific
dictionary adaptive to the input CFA is learned thereafter. Using the adaptive
dictionary, the sparse coefficients of intermediate data are computed and
transformed to predict residual image. The residual image is added back into
the intermediate image to obtain the final reconstruction image. Experimental
results demonstrate the state-of-the-art performance in terms of PSNR and
subjective visual perception.
Learning Mixed Graphical Models
Authors: Jason D. Lee, Trevor J. Hastie
Date: 2012-05-22 00:00:00

We consider the problem of learning the structure of a pairwise graphical
model over continuous and discrete variables. We present a new pairwise model
for graphical models with both continuous and discrete variables that is
amenable to structure learning. In previous work, authors have considered
structure learning of Gaussian graphical models and structure learning of
discrete models. Our approach is a natural generalization of these two lines of
work to the mixed case. The penalization scheme involves a novel symmetric use
of the group-lasso norm and follows naturally from a particular parametrization
of the model.
A Novel Robust Method to Add Watermarks to Bitmap Images by Fading Technique
Authors: Firas A. Jassim
Date: 2013-07-03 00:00:00
Tasks: SSIM

Digital water marking is one of the essential fields in image security and
copyright protection. The proposed technique in this paper was based on the
principle of protecting images by hide an invisible watermark in the image. The
technique starts with merging the cover image and the watermark image with
suitable ratios, i.e., 99% from the cover image will be merged with 1% from the
watermark image. Technically, the fading process is irreversible but with the
proposed technique, the probability to reconstruct the original watermark image
is great. There is no perceptible difference between the original and
watermarked image by human eye. The experimental results show that the proposed
technique proven its ability to hide images that have the same size of the
cover image. Three performance measures were implemented to support the
proposed techniques which are MSE, PSNR, and SSIM. Fortunately, all the three
measures have excellent values.
Greedy Feature Selection for Subspace Clustering
Authors: Eva L. Dyer, Aswin C. Sankaranarayanan, Richard G. Baraniuk
Date: 2013-03-19 00:00:00
Tasks: Clustering, feature selection

Unions of subspaces provide a powerful generalization to linear subspace
models for collections of high-dimensional data. To learn a union of subspaces
from a collection of data, sets of signals in the collection that belong to the
same subspace must be identified in order to obtain accurate estimates of the
subspace structures present in the data. Recently, sparse recovery methods have
been shown to provide a provable and robust strategy for exact feature
selection (EFS)--recovering subsets of points from the ensemble that live in
the same subspace. In parallel with recent studies of EFS with L1-minimization,
in this paper, we develop sufficient conditions for EFS with a greedy method
for sparse signal recovery known as orthogonal matching pursuit (OMP).
Following our analysis, we provide an empirical study of feature selection
strategies for signals living on unions of subspaces and characterize the gap
between sparse recovery methods and nearest neighbor (NN)-based approaches. In
particular, we demonstrate that sparse recovery methods provide significant
advantages over NN methods and the gap between the two approaches is
particularly pronounced when the sampling of subspaces in the dataset is
sparse. Our results suggest that OMP may be employed to reliably recover exact
feature sets in a number of regimes where NN approaches fail to reveal the
subspace membership of points in the ensemble.
Application of a clustering framework to UK domestic electricity data
Authors: Ian Dent, Uwe Aickelin, Tom Rodden
Date: 2013-07-03 00:00:00
Tasks: Clustering, Management

This paper takes an approach to clustering domestic electricity load profiles
that has been successfully used with data from Portugal and applies it to UK
data. Clustering techniques are applied and it is found that the preferred
technique in the Portuguese work (a two stage process combining Self Organised
Maps and Kmeans) is not appropriate for the UK data. The work shows that up to
nine clusters of households can be identified with the differences in usage
profiles being visually striking. This demonstrates the appropriateness of
breaking the electricity usage patterns down to more detail than the two load
profiles currently published by the electricity industry. The paper details
initial results using data collected in Milton Keynes around 1990. Further work
is described and will concentrate on building accurate and meaningful clusters
of similar electricity users in order to better direct demand side management
initiatives to the most relevant target customers.