pwc-archive/papers-with-abstracts huggingface dataset:
Using Clustering to extract Personality Information from socio economic data
Authors: Alexandros Ladas, Uwe Aickelin, Jon Garibaldi, Eamonn Ferguson
Date: 2013-07-08 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

It has become apparent that models that have been applied widely in
economics, including Machine Learning techniques and Data Mining methods,
should take into consideration principles that derive from the theories of
Personality Psychology in order to discover more comprehensive knowledge
regarding complicated economic behaviours. In this work, we present a method to
extract Behavioural Groups by using simple clustering techniques that can
potentially reveal aspects of the Personalities for their members. We believe
that this is very important because the psychological information regarding the
Personalities of individuals is limited in real world applications and because
it can become a useful tool in improving the traditional models of Knowledge
Economy.
Machine Learning in Proof General: Interfacing Interfaces
Authors: Ekaterina Komendantskaya, Jónathan Heras, Gudmund Grov
Date: 2012-12-14 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

We present ML4PG - a machine learning extension for Proof General. It allows
users to gather proof statistics related to shapes of goals, sequences of
applied tactics, and proof tree structures from the libraries of interactive
higher-order proofs written in Coq and SSReflect. The gathered data is
clustered using the state-of-the-art machine learning algorithms available in
MATLAB and Weka. ML4PG provides automated interfacing between Proof General and
MATLAB/Weka. The results of clustering are used by ML4PG to provide proof hints
in the process of interactive proof development.
READ-EVAL-PRINT in Parallel and Asynchronous Proof-checking
Authors: Makarius Wenzel
Date: 2013-07-08 00:00:00
Tasks: Automated Theorem Proving

The LCF tradition of interactive theorem proving, which was started by Milner
in the 1970-ies, appears to be tied to the classic READ-EVAL-PRINT-LOOP of
sequential and synchronous evaluation of prover commands. We break up this loop
and retrofit the read-eval-print phases into a model of parallel and
asynchronous proof processing. Thus we explain some key concepts of the
Isabelle/Scala approach to prover interaction and integration, and the
Isabelle/jEdit Prover IDE as front-end technology. We hope to open up the
scientific discussion about non-trivial interaction models for ITP systems
again, and help getting other old-school proof assistants on a similar track.
A Dynamic Algorithm for the Longest Common Subsequence Problem using Ant Colony Optimization Technique
Authors: Arindam Chaudhuri
Date: 2013-07-07 00:00:00
Tasks: Combinatorial Optimization

We present a dynamic algorithm for solving the Longest Common Subsequence
Problem using Ant Colony Optimization Technique. The Ant Colony Optimization
Technique has been applied to solve many problems in Optimization Theory,
Machine Learning and Telecommunication Networks etc. In particular, application
of this theory in NP-Hard Problems has a remarkable significance. Given two
strings, the traditional technique for finding Longest Common Subsequence is
based on Dynamic Programming which consists of creating a recurrence relation
and filling a table of size . The proposed algorithm draws analogy with
behavior of ant colonies function and this new computational paradigm is known
as Ant System. It is a viable new approach to Stochastic Combinatorial
Optimization. The main characteristics of this model are positive feedback,
distributed computation, and the use of constructive greedy heuristic. Positive
feedback accounts for rapid discovery of good solutions, distributed
computation avoids premature convergence and greedy heuristic helps find
acceptable solutions in minimum number of stages. We apply the proposed
methodology to Longest Common Subsequence Problem and give the simulation
results. The effectiveness of this approach is demonstrated by efficient
Computational Complexity. To the best of our knowledge, this is the first Ant
Colony Optimization Algorithm for Longest Common Subsequence Problem.
Achieving greater Explanatory Power and Forecasting Accuracy with Non-uniform spread Fuzzy Linear Regression
Authors: Arindam Chaudhuri, Kajal De
Date: 2013-07-07 00:00:00
Tasks: regression

Fuzzy regression models have been applied to several Operations Research
applications viz., forecasting and prediction. Earlier works on fuzzy
regression analysis obtain crisp regression coefficients for eliminating the
problem of increasing spreads for the estimated fuzzy responses as the
magnitude of the independent variable increases. But they cannot deal with the
problem of non-uniform spreads. In this work, a three-phase approach is
discussed to construct the fuzzy regression model with non-uniform spreads to
deal with this problem. The first phase constructs the membership functions of
the least-squares estimates of regression coefficients based on extension
principle to completely conserve the fuzziness of observations. They are then
defuzzified by the centre of area method to obtain crisp regression
coefficients in the second phase. Finally, the error terms of the method are
determined by setting each estimated spread equal to its corresponding observed
spread. The Tagaki-Sugeno inference system is used for improving the accuracy
of forecasts. The simulation example demonstrates the strength of fuzzy linear
regression model in terms of higher explanatory power and forecasting
performance.
Fuzzy Integer Linear Programming Mathematical Models for Examination Timetable Problem
Authors: Arindam Chaudhuri, Kajal De
Date: 2013-07-07 00:00:00
Tasks: Combinatorial Optimization

ETP is NP Hard combinatorial optimization problem. It has received tremendous
research attention during the past few years given its wide use in
universities. In this Paper, we develop three mathematical models for NSOU,
Kolkata, India using FILP technique. To deal with impreciseness and vagueness
we model various allocation variables through fuzzy numbers. The solution to
the problem is obtained using Fuzzy number ranking method. Each feasible
solution has fuzzy number obtained by Fuzzy objective function. The different
FILP technique performance are demonstrated by experimental data generated
through extensive simulation from NSOU, Kolkata, India in terms of its
execution times. The proposed FILP models are compared with commonly used
heuristic viz. ILP approach on experimental data which gives an idea about
quality of heuristic. The techniques are also compared with different
Artificial Intelligence based heuristics for ETP with respect to best and mean
cost as well as execution time measures on Carter benchmark datasets to
illustrate its effectiveness. FILP takes an appreciable amount of time to
generate satisfactory solution in comparison to other heuristics. The
formulation thus serves as good benchmark for other heuristics. The
experimental study presented here focuses on producing a methodology that
generalizes well over spectrum of techniques that generates significant results
for one or more datasets. The performance of FILP model is finally compared to
the best results cited in literature for Carter benchmarks to assess its
potential. The problem can be further reduced by formulating with lesser number
of allocation variables it without affecting optimality of solution obtained.
FLIP model for ETP can also be adapted to solve other ETP as well as
combinatorial optimization problems.
Discovering Stock Price Prediction Rules of Bombay Stock Exchange Using Rough Fuzzy Multi Layer Perception Networks
Authors: Arindam Chaudhuri, Kajal De, Dipak Chatterjee
Date: 2013-07-07 00:00:00
Tasks: Attribute, Stock Price Prediction

In India financial markets have existed for many years. A functionally
accented, diverse, efficient and flexible financial system is vital to the
national objective of creating a market driven, productive and competitive
economy. Today markets of varying maturity exist in equity, debt, commodities
and foreign exchange. In this work we attempt to generate prediction rules
scheme for stock price movement at Bombay Stock Exchange using an important
Soft Computing paradigm viz., Rough Fuzzy Multi Layer Perception. The use of
Computational Intelligence Systems such as Neural Networks, Fuzzy Sets, Genetic
Algorithms, etc. for Stock Market Predictions has been widely established. The
process is to extract knowledge in the form of rules from daily stock
movements. These rules can then be used to guide investors. To increase the
efficiency of the prediction process, Rough Sets is used to discretize the
data. The methodology uses a Genetic Algorithm to obtain a structured network
suitable for both classification and rule extraction. The modular concept,
based on divide and conquer strategy, provides accelerated training and a
compact network suitable for generating a minimum number of rules with high
certainty values. The concept of variable mutation operator is introduced for
preserving the localized structure of the constituting Knowledge Based
sub-networks, while they are integrated and evolved. Rough Set Dependency Rules
are generated directly from the real valued attribute table containing Fuzzy
membership values. The paradigm is thus used to develop a rule extraction
algorithm. The extracted rules are compared with some of the related rule
extraction techniques on the basis of some quantitative performance indices.
The proposed methodology extracts rules which are less in number, are accurate,
have high certainty factor and have low confusion with less computation time.
Trapezoidal Fuzzy Numbers for the Transportation Problem
Authors: Arindam Chaudhuri, Kajal De, Dipak Chatterjee, Pabitra Mitra
Date: 2013-07-07 00:00:00

Transportation Problem is an important problem which has been widely studied
in Operations Research domain. It has been often used to simulate different
real life problems. In particular, application of this Problem in NP Hard
Problems has a remarkable significance. In this Paper, we present the closed,
bounded and non empty feasible region of the transportation problem using fuzzy
trapezoidal numbers which ensures the existence of an optimal solution to the
balanced transportation problem. The multivalued nature of Fuzzy Sets allows
handling of uncertainty and vagueness involved in the cost values of each cells
in the transportation table. For finding the initial solution of the
transportation problem we use the Fuzzy Vogel Approximation Method and for
determining the optimality of the obtained solution Fuzzy Modified Distribution
Method is used. The fuzzification of the cost of the transportation problem is
discussed with the help of a numerical example. Finally, we discuss the
computational complexity involved in the problem. To the best of our knowledge,
this is the first work on obtaining the solution of the transportation problem
using fuzzy trapezoidal numbers.
A Comparative study of Transportation Problem under Probabilistic and Fuzzy Uncertainties
Authors: Arindam Chaudhuri, Kajal De
Date: 2013-07-07 00:00:00

Transportation Problem is an important aspect which has been widely studied
in Operations Research domain. It has been studied to simulate different real
life problems. In particular, application of this Problem in NP- Hard Problems
has a remarkable significance. In this Paper, we present a comparative study of
Transportation Problem through Probabilistic and Fuzzy Uncertainties. Fuzzy
Logic is a computational paradigm that generalizes classical two-valued logic
for reasoning under uncertainty. In order to achieve this, the notation of
membership in a set needs to become a matter of degree. By doing this we
accomplish two things viz., (i) ease of describing human knowledge involving
vague concepts and (ii) enhanced ability to develop cost-effective solution to
real-world problem. The multi-valued nature of Fuzzy Sets allows handling
uncertain and vague information. It is a model-less approach and a clever
disguise of Probability Theory. We give comparative simulation results of both
approaches and discuss the Computational Complexity. To the best of our
knowledge, this is the first work on comparative study of Transportation
Problem using Probabilistic and Fuzzy Uncertainties.
Solution of Rectangular Fuzzy Games by Principle of Dominance Using LR-type Trapezoidal Fuzzy Numbers
Authors: Arindam Chaudhuri
Date: 2013-07-07 00:00:00
Tasks: Decision Making, Management

Fuzzy Set Theory has been applied in many fields such as Operations Research,
Control Theory, and Management Sciences etc. In particular, an application of
this theory in Managerial Decision Making Problems has a remarkable
significance. In this Paper, we consider a solution of Rectangular Fuzzy game
with pay-off as imprecise numbers instead of crisp numbers viz., interval and
LR-type Trapezoidal Fuzzy Numbers. The solution of such Fuzzy games with pure
strategies by minimax-maximin principle is discussed. The Algebraic Method to
solve Fuzzy games without saddle point by using mixed strategies is also
illustrated. Here, pay-off matrix is reduced to pay-off matrix by Dominance
Method. This fact is illustrated by means of Numerical Example.
Intelligent Hybrid Man-Machine Translation Quality Estimation
Authors: Ibrahim Sabek, Noha A. Yousri, Nagwa Elmakky, Mona Habib
Date: 2013-07-07 00:00:00
Tasks: Machine Translation, Translation

Inferring evaluation scores based on human judgments is invaluable compared
to using current evaluation metrics which are not suitable for real-time
applications e.g. post-editing. However, these judgments are much more
expensive to collect especially from expert translators, compared to evaluation
based on indicators contrasting source and translation texts. This work
introduces a novel approach for quality estimation by combining learnt
confidence scores from a probabilistic inference model based on human
judgments, with selective linguistic features-based scores, where the proposed
inference model infers the credibility of given human ranks to solve the
scarcity and inconsistency issues of human judgments. Experimental results,
using challenging language-pairs, demonstrate improvement in correlation with
human judgments over traditional evaluation metrics.
Lifting Structural Tractability to CSP with Global Constraints
Authors: Evgenij Thorstensen
Date: 2013-07-06 00:00:00

A wide range of problems can be modelled as constraint satisfaction problems
(CSPs), that is, a set of constraints that must be satisfied simultaneously.
Constraints can either be represented extensionally, by explicitly listing
allowed combinations of values, or implicitly, by special-purpose algorithms
provided by a solver. Such implicitly represented constraints, known as global
constraints, are widely used; indeed, they are one of the key reasons for the
success of constraint programming in solving real-world problems.
  In recent years, a variety of restrictions on the structure of CSP instances
that yield tractable classes have been identified. However, many such
restrictions fail to guarantee tractability for CSPs with global constraints.
In this paper, we investigate the properties of extensionally represented
constraints that these restrictions exploit to achieve tractability, and show
that there are large classes of global constraints that also possess these
properties. This allows us to lift these restrictions to the global case, and
identify new tractable classes of CSPs with global constraints.
Ensemble Methods for Multi-label Classification
Authors: Lior Rokach, Alon Schclar, Ehud Itach
Date: 2013-07-06 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Ensemble methods have been shown to be an effective tool for solving
multi-label classification tasks. In the RAndom k-labELsets (RAKEL) algorithm,
each member of the ensemble is associated with a small randomly-selected subset
of k labels. Then, a single label classifier is trained according to each
combination of elements in the subset. In this paper we adopt a similar
approach, however, instead of randomly choosing subsets, we select the minimum
required subsets of k labels that cover all labels and meet additional
constraints such as coverage of inter-label correlations. Construction of the
cover is achieved by formulating the subset selection as a minimum set covering
problem (SCP) and solving it by using approximation algorithms. Every cover
needs only to be prepared once by offline algorithms. Once prepared, a cover
may be applied to the classification of any given multi-label dataset whose
properties conform with those of the cover. The contribution of this paper is
two-fold. First, we introduce SCP as a general framework for constructing label
covers while allowing the user to incorporate cover construction constraints.
We demonstrate the effectiveness of this framework by proposing two
construction constraints whose enforcement produces covers that improve the
prediction performance of random selection. Second, we provide theoretical
bounds that quantify the probabilities of random selection to produce covers
that meet the proposed construction criteria. The experimental results indicate
that the proposed methods improve multi-label classification accuracy and
stability compared with the RAKEL algorithm and to other state-of-the-art
algorithms.
Indexing Medical Images based on Collaborative Experts Reports
Authors: Abir Messaoudi, Riadh Bouslimi, Jalel Akaichi
Date: 2013-05-17 00:00:00
Tasks: Medical Image Analysis

A patient is often willing to quickly get, from his physician, reliable
analysis and concise explanation according to provided linked medical images.
The fact of making choices individually by the patient's physician may lead to
malpractices and consequently generates unforeseeable damages. The Institute of
Medicine of the National Sciences Academy(IMNAS) in USA published a study
estimating that up to 98,000 hospital deathseach year can be attributed to
medical malpractice [1]. Moreover, physician, in charge of medical image
analysis, might be unavailable at the right time, which may complicate the
patient's state. The goal of this paper is to provide to physicians and
patients, a social network that permits to foster cooperation and to overcome
the problem of unavailability of doctors on site any time. Therefore, patients
can submit their medical images to be diagnosed and commented by several
experts instantly. Consequently, the need to process opinions and to extract
information automatically from the proposed social network became a necessity
due to the huge number of comments expressing specialist's reviews. For this
reason, we propose a kind of comments' summary keywords-based method which
extracts the major current terms and relevant words existing on physicians'
annotations. The extracted keywords will present a new and robust method for
image indexation. In fact, significant extracted terms will be used later to
index images in order to facilitate their discovery for any appropriate use. To
overcome this challenge, we propose our Terminology Extraction of Annotation
(TEA) mixed approach which focuses on algorithms mainly based on statistical
methods and on external semantic resources.
Stochastic Optimization of PCA with Capped MSG
Authors: Raman Arora, Andrew Cotter, Nathan Srebro
Date: 2013-07-05 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Stochastic Optimization

We study PCA as a stochastic optimization problem and propose a novel
stochastic approximation algorithm which we refer to as "Matrix Stochastic
Gradient" (MSG), as well as a practical variant, Capped MSG. We study the
method both theoretically and empirically.
Biomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification
Authors: Chris Roadknight, Uwe Aickelin, Alex Ladas, Daniele Soria, John Scholefield, Lindy Durrant
Date: 2013-07-05 00:00:00
Tasks: Classification, Clustering, General Classification, 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. Attempts are made
to cluster this dataset and important subsets of it in an effort to
characterize the data and validate existing standards for tumour
classification. It is apparent from optimal clustering that existing tumour
classification is largely unrelated to immunological factors within a patient
and that there may be scope for re-evaluating treatment options and survival
estimates based on a combination of tumour physiology and patient
histochemistry.
Supervised Learning and Anti-learning of Colorectal Cancer Classes and Survival Rates from Cellular Biology Parameters
Authors: Chris Roadknight, Uwe Aickelin, Guoping Qiu, John Scholefield, Lindy Durrant
Date: 2013-07-05 00:00:00
Tasks: 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. Attempts are made
to learn relationships between attributes (physical and immunological) and the
resulting tumour stage and survival. Results for conventional machine learning
approaches can be considered poor, especially for predicting tumour stages for
the most important types of cancer. This poor performance is further
investigated and compared with a synthetic, dataset based on the logical
exclusive-OR function and it is shown that there is a significant level of
'anti-learning' present in all supervised methods used and this can be
explained by the highly dimensional, complex and sparsely representative
dataset. For predicting the stage of cancer from the immunological attributes,
anti-learning approaches outperform a range of popular algorithms.
Comparing Data-mining Algorithms Developed for Longitudinal Observational Databases
Authors: Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin, Daniele Soria, Jack E. Gibson, Richard B. Hubbard
Date: 2013-07-05 00:00:00
Tasks: Marketing

Longitudinal observational databases have become a recent interest in the
post marketing drug surveillance community due to their ability of presenting a
new perspective for detecting negative side effects. Algorithms mining
longitudinal observation databases are not restricted by many of the
limitations associated with the more conventional methods that have been
developed for spontaneous reporting system databases. In this paper we
investigate the robustness of four recently developed algorithms that mine
longitudinal observational databases by applying them to The Health Improvement
Network (THIN) for six drugs with well document known negative side effects.
Our results show that none of the existing algorithms was able to consistently
identify known adverse drug reactions above events related to the cause of the
drug and no algorithm was superior.