pwc-archive/papers-with-abstracts huggingface dataset:
Identifying cancer subtypes in glioblastoma by combining genomic, transcriptomic and epigenomic data
Authors: Richard S. Savage, Zoubin Ghahramani, Jim E. Griffin, Paul Kirk, David L. Wild
Date: 2013-04-12 00:00:00
Tasks: Clustering, feature selection

We present a nonparametric Bayesian method for disease subtype discovery in
multi-dimensional cancer data. Our method can simultaneously analyse a wide
range of data types, allowing for both agreement and disagreement between their
underlying clustering structure. It includes feature selection and infers the
most likely number of disease subtypes, given the data.
  We apply the method to 277 glioblastoma samples from The Cancer Genome Atlas,
for which there are gene expression, copy number variation, methylation and
microRNA data. We identify 8 distinct consensus subtypes and study their
prognostic value for death, new tumour events, progression and recurrence. The
consensus subtypes are prognostic of tumour recurrence (log-rank p-value of
$3.6 \times 10^{-4}$ after correction for multiple hypothesis tests). This is
driven principally by the methylation data (log-rank p-value of $2.0 \times
10^{-3}$) but the effect is strengthened by the other 3 data types,
demonstrating the value of integrating multiple data types.
  Of particular note is a subtype of 47 patients characterised by very low
levels of methylation. This subtype has very low rates of tumour recurrence and
no new events in 10 years of follow up. We also identify a small gene
expression subtype of 6 patients that shows particularly poor survival
outcomes. Additionally, we note a consensus subtype that showly a highly
distinctive data signature and suggest that it is therefore a biologically
distinct subtype of glioblastoma.
  The code is available from https://sites.google.com/site/multipledatafusion/
A Fuzzy Logic Based Certain Trust Model for E-Commerce
Authors: Kawser Wazed Nafi, Tonny Shekha Kar, Amjad Hossain, M. M. A. Hashem
Date: 2013-04-15 00:00:00
Tasks: Cloud Computing

Trustworthiness especially for service oriented system is very important
topic now a day in IT field of the whole world. There are many successful
E-commerce organizations presently run in the whole world, but E-commerce has
not reached its full potential. The main reason behind this is lack of Trust of
people in e-commerce. Again, proper models are still absent for calculating
trust of different e-commerce organizations. Most of the present trust models
are subjective and have failed to account vagueness and ambiguity of different
domain. In this paper we have proposed a new fuzzy logic based Certain Trust
model which considers these ambiguity and vagueness of different domain. Fuzzy
Based Certain Trust Model depends on some certain values given by experts and
developers. can be applied in a system like cloud computing, internet, website,
e-commerce, etc. to ensure trustworthiness of these platforms. In this paper we
show, although fuzzy works with uncertainties, proposed model works with some
certain values. Some experimental results and validation of the model with
linguistics terms are shown at the last part of the paper.
Off-policy Learning with Eligibility Traces: A Survey
Authors: Matthieu Geist, Bruno Scherrer
Date: 2013-04-15 00:00:00
Tasks: Survey

In the framework of Markov Decision Processes, off-policy learning, that is
the problem of learning a linear approximation of the value function of some
fixed policy from one trajectory possibly generated by some other policy. We
briefly review on-policy learning algorithms of the literature (gradient-based
and least-squares-based), adopting a unified algorithmic view. Then, we
highlight a systematic approach for adapting them to off-policy learning with
eligibility traces. This leads to some known algorithms - off-policy
LSTD(\lambda), LSPE(\lambda), TD(\lambda), TDC/GQ(\lambda) - and suggests new
extensions - off-policy FPKF(\lambda), BRM(\lambda), gBRM(\lambda),
GTD2(\lambda). We describe a comprehensive algorithmic derivation of all
algorithms in a recursive and memory-efficent form, discuss their known
convergence properties and illustrate their relative empirical behavior on
Garnet problems. Our experiments suggest that the most standard algorithms on
and off-policy LSTD(\lambda)/LSPE(\lambda) - and TD(\lambda) if the feature
space dimension is too large for a least-squares approach - perform the best.
GPU Acclerated Automated Feature Extraction from Satellite Images
Authors: K. Phani Tejaswi, D. Shanmukha Rao, Thara Nair, A. V. V. Prasad
Date: 2013-04-15 00:00:00
Tasks: GPU, image smoothing

The availability of large volumes of remote sensing data insists on higher
degree of automation in feature extraction, making it a need of the hour.The
huge quantum of data that needs to be processed entails accelerated processing
to be enabled.GPUs, which were originally designed to provide efficient
visualization, are being massively employed for computation intensive parallel
processing environments. Image processing in general and hence automated
feature extraction, is highly computation intensive, where performance
improvements have a direct impact on societal needs. In this context, an
algorithm has been formulated for automated feature extraction from a
panchromatic or multispectral image based on image processing techniques. Two
Laplacian of Guassian (LoG) masks were applied on the image individually
followed by detection of zero crossing points and extracting the pixels based
on their standard deviation with the surrounding pixels. The two extracted
images with different LoG masks were combined together which resulted in an
image with the extracted features and edges. Finally the user is at liberty to
apply the image smoothing step depending on the noise content in the extracted
image. The image is passed through a hybrid median filter to remove the salt
and pepper noise from the image. This paper discusses the aforesaid algorithm
for automated feature extraction, necessity of deployment of GPUs for the same;
system-level challenges and quantifies the benefits of integrating GPUs in such
environment. The results demonstrate that substantial enhancement in
performance margin can be achieved with the best utilization of GPU resources
and an efficient parallelization strategy. Performance results in comparison
with the conventional computing scenario have provided a speedup of 20x, on
realization of this parallelizing strategy.
Managing sparsity, time, and quality of inference in topic models
Authors: Khoat Than, Tu Bao Ho
Date: 2012-10-26 00:00:00
Tasks: Topic Models

Inference is an integral part of probabilistic topic models, but is often
non-trivial to derive an efficient algorithm for a specific model. It is even
much more challenging when we want to find a fast inference algorithm which
always yields sparse latent representations of documents. In this article, we
introduce a simple framework for inference in probabilistic topic models,
denoted by FW. This framework is general and flexible enough to be easily
adapted to mixture models. It has a linear convergence rate, offers an easy way
to incorporate prior knowledge, and provides us an easy way to directly trade
off sparsity against quality and time. We demonstrate the goodness and
flexibility of FW over existing inference methods by a number of tasks.
Finally, we show how inference in topic models with nonconjugate priors can be
done efficiently.
Single View Depth Estimation from Examples
Authors: Tal Hassner, Ronen Basri
Date: 2013-04-14 00:00:00
Tasks: Depth Estimation, Object

We describe a non-parametric, "example-based" method for estimating the depth
of an object, viewed in a single photo. Our method consults a database of
example 3D geometries, searching for those which look similar to the object in
the photo. The known depths of the selected database objects act as shape
priors which constrain the process of estimating the object's depth. We show
how this process can be performed by optimizing a well defined target
likelihood function, via a hard-EM procedure. We address the problem of
representing the (possibly infinite) variability of viewing conditions with a
finite (and often very small) example set, by proposing an on-the-fly example
update scheme. We further demonstrate the importance of non-stationarity in
avoiding misleading examples when estimating structured shapes. We evaluate our
method and present both qualitative as well as quantitative results for
challenging object classes. Finally, we show how this same technique may be
readily applied to a number of related problems. These include the novel task
of estimating the occluded depth of an object's backside and the task of
tailoring custom fitting image-maps for input depths.
An accelerated CLPSO algorithm
Authors: Muhammad Omer Bin Saeed, Muhammad Saqib Sohail, Syed Zeeshan Rizvi, Mobien Shoaib, Asrar Ul Haq Sheikh
Date: 2013-04-14 00:00:00

The particle swarm approach provides a low complexity solution to the
optimization problem among various existing heuristic algorithms. Recent
advances in the algorithm resulted in improved performance at the cost of
increased computational complexity, which is undesirable. Literature shows that
the particle swarm optimization algorithm based on comprehensive learning
provides the best complexity-performance trade-off. We show how to reduce the
complexity of this algorithm further, with a slight but acceptable performance
loss. This enhancement allows the application of the algorithm in time critical
applications, such as, real-time tracking, equalization etc.
Automatic case acquisition from texts for process-oriented case-based reasoning
Authors: Valmi Dufour-Lussier, Florence Le Ber, Jean Lieber, Emmanuel Nauer
Date: 2013-04-14 00:00:00

This paper introduces a method for the automatic acquisition of a rich case
representation from free text for process-oriented case-based reasoning. Case
engineering is among the most complicated and costly tasks in implementing a
case-based reasoning system. This is especially so for process-oriented
case-based reasoning, where more expressive case representations are generally
used and, in our opinion, actually required for satisfactory case adaptation.
In this context, the ability to acquire cases automatically from procedural
texts is a major step forward in order to reason on processes. We therefore
detail a methodology that makes case acquisition from processes described as
free text possible, with special attention given to assembly instruction texts.
This methodology extends the techniques we used to extract actions from cooking
recipes. We argue that techniques taken from natural language processing are
required for this task, and that they give satisfactory results. An evaluation
based on our implemented prototype extracting workflows from recipe texts is
provided.
Justificatory and Explanatory Argumentation for Committing Agents
Authors: Ioan Alfred Letia, Adrian Groza
Date: 2013-04-13 00:00:00

In the interaction between agents we can have an explicative discourse, when
communicating preferences or intentions, and a normative discourse, when
considering normative knowledge. For justifying their actions our agents are
endowed with a Justification and Explanation Logic (JEL), capable to cover both
the justification for their commitments and explanations why they had to act in
that way, due to the current situation in the environment. Social commitments
are used to formalise justificatory and explanatory patterns. The combination
of ex- planation, justification, and commitments
Proceedings of the Second Conference on Uncertainty in Artificial Intelligence (1986)
Authors: Laveen Kanal, John Lemmer
Date: 2013-04-13 00:00:00

This is the Proceedings of the Second Conference on Uncertainty in Artificial
Intelligence, which was held in Philadelphia, PA, August 8-10, 1986
Proceedings of the Third Conference on Uncertainty in Artificial Intelligence (1987)
Authors: Laveen Kanal, John Lemmer, Tod Levitt
Date: 2013-04-13 00:00:00

This is the Proceedings of the Third Conference on Uncertainty in Artificial
Intelligence, which was held in Seattle, WA, July 10-12, 1987
Proceedings of the Fourth Conference on Uncertainty in Artificial Intelligence (1988)
Authors: Laveen Kanal, John Lemmer, Tod Levitt, Ross Shachter
Date: 2013-04-13 00:00:00

This is the Proceedings of the Fourth Conference on Uncertainty in Artificial
Intelligence, which was held in Minneapolis, MN, July 10-12, 1988
Proceedings of the Fifth Conference on Uncertainty in Artificial Intelligence (1989)
Authors: Max Henrion, Laveen Kanal, John Lemmer, Ross Shachter
Date: 2013-04-13 00:00:00

This is the Proceedings of the Fifth Conference on Uncertainty in Artificial
Intelligence, which was held in Windsor, ON, August 18-20, 1989
Proceedings of the Seventh Conference on Uncertainty in Artificial Intelligence (1991)
Authors: Piero Bonissone, Bruce D'Ambrosio, Philippe Smets
Date: 2013-04-13 00:00:00

This is the Proceedings of the Seventh Conference on Uncertainty in
Artificial Intelligence, which was held in Los Angeles, CA, July 13-15, 1991
Proceedings of the Eighth Conference on Uncertainty in Artificial Intelligence (1992)
Authors: Bruce D'Ambrosio, Didier Dubois, Philippe Smets, Michael Wellman
Date: 2013-04-13 00:00:00

This is the Proceedings of the Eighth Conference on Uncertainty in Artificial
Intelligence, which was held in Stanford, CA, July 17-19, 1992
Proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence (1993)
Authors: David Heckerman, E. Mamdani
Date: 2013-04-13 00:00:00

This is the Proceedings of the Ninth Conference on Uncertainty in Artificial
Intelligence, which was held in Washington, DC, July 9-11, 1993
Proceedings of the Tenth Conference on Uncertainty in Artificial Intelligence (1994)
Authors: Ramon Lopez de Mantaras, David Poole
Date: 2013-04-13 00:00:00

This is the Proceedings of the Tenth Conference on Uncertainty in Artificial
Intelligence, which was held in Seattle, WA, July 29-31, 1994
Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence (1997)
Authors: Dan Geiger, Prakash Shenoy
Date: 2013-04-13 00:00:00

This is the Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence, which was held in Providence, RI, August 1-3, 1997
A New Homogeneity Inter-Clusters Measure in SemiSupervised Clustering
Authors: Badreddine Meftahi, Ourida Ben Boubaker Saidi
Date: 2013-04-13 00:00:00
Tasks: Clustering

Many studies in data mining have proposed a new learning called
semi-Supervised. Such type of learning combines unlabeled and labeled data
which are hard to obtain. However, in unsupervised methods, the only unlabeled
data are used. The problem of significance and the effectiveness of
semi-supervised clustering results is becoming of main importance. This paper
pursues the thesis that muchgreater accuracy can be achieved in such clustering
by improving the similarity computing. Hence, we introduce a new approach of
semisupervised clustering using an innovative new homogeneity measure of
generated clusters. Our experimental results demonstrate significantly improved
accuracy as a result.
Solving Linear Equations Using a Jacobi Based Time-Variant Adaptive Hybrid Evolutionary Algorithm
Authors: A. R. M. Jalal Uddin Jamali, M. M. A. Hashem, Md. Bazlar Rahman
Date: 2013-04-13 00:00:00
Tasks: Evolutionary Algorithms

Large set of linear equations, especially for sparse and structured
coefficient (matrix) equations, solutions using classical methods become
arduous. And evolutionary algorithms have mostly been used to solve various
optimization and learning problems. Recently, hybridization of classical
methods (Jacobi method and Gauss-Seidel method) with evolutionary computation
techniques have successfully been applied in linear equation solving. In the
both above hybrid evolutionary methods, uniform adaptation (UA) techniques are
used to adapt relaxation factor. In this paper, a new Jacobi Based Time-Variant
Adaptive (JBTVA) hybrid evolutionary algorithm is proposed. In this algorithm,
a Time-Variant Adaptive (TVA) technique of relaxation factor is introduced
aiming at both improving the fine local tuning and reducing the disadvantage of
uniform adaptation of relaxation factors. This algorithm integrates the Jacobi
based SR method with time variant adaptive evolutionary algorithm. The
convergence theorems of the proposed algorithm are proved theoretically. And
the performance of the proposed algorithm is compared with JBUA hybrid
evolutionary algorithm and classical methods in the experimental domain. The
proposed algorithm outperforms both the JBUA hybrid algorithm and classical
methods in terms of convergence speed and effectiveness.
Improving Generalization Ability of Genetic Programming: Comparative Study
Authors: Tejashvi R. Naik, Vipul K. Dabhi
Date: 2013-04-13 00:00:00

In the field of empirical modeling using Genetic Programming (GP), it is
important to evolve solution with good generalization ability. Generalization
ability of GP solutions get affected by two important issues: bloat and
over-fitting. Bloat is uncontrolled growth of code without any gain in fitness
and important issue in GP. We surveyed and classified existing literature
related to different techniques used by GP research community to deal with the
issue of bloat. Moreover, the classifications of different bloat control
approaches and measures for bloat are discussed. Next, we tested four bloat
control methods: Tarpeian, double tournament, lexicographic parsimony pressure
with direct bucketing and ratio bucketing on six different problems and
identified where each bloat control method performs well on per problem basis.
Based on the analysis of each method, we combined two methods: double
tournament (selection method) and Tarpeian method (works before evaluation) to
avoid bloated solutions and compared with the results obtained from individual
performance of double tournament method. It was found that the results were
improved with this combination of two methods.
An Improved ACS Algorithm for the Solutions of Larger TSP Problems
Authors: Md. Rakib Hassan, Md. Kamrul Hasan, M. M. A. Hashem
Date: 2013-04-13 00:00:00
Tasks: Combinatorial Optimization, Traveling Salesman Problem

Solving large traveling salesman problem (TSP) in an efficient way is a
challenging area for the researchers of computer science. This paper presents a
modified version of the ant colony system (ACS) algorithm called Red-Black Ant
Colony System (RB-ACS) for the solutions of TSP which is the most prominent
member of the combinatorial optimization problem. RB-ACS uses the concept of
ant colony system together with the parallel search of genetic algorithm for
obtaining the optimal solutions quickly. In this paper, it is shown that the
proposed RB-ACS algorithm yields significantly better performance than the
existing best-known algorithms.
Evolutionary Turing in the Context of Evolutionary Machines
Authors: Mark Burgin, Eugene Eberbach
Date: 2013-04-13 00:00:00

One of the roots of evolutionary computation was the idea of Turing about
unorganized machines. The goal of this work is the development of foundations
for evolutionary computations, connecting Turing's ideas and the contemporary
state of art in evolutionary computations. To achieve this goal, we develop a
general approach to evolutionary processes in the computational context,
building mathematical models of computational systems, functioning of which is
based on evolutionary processes, and studying properties of such systems.
Operations with evolutionary machines are described and it is explored when
definite classes of evolutionary machines are closed with respect to basic
operations with these machines. We also study such properties as linguistic and
functional equivalence of evolutionary machines and their classes, as well as
computational power of evolutionary machines and their classes, comparing of
evolutionary machines to conventional automata, such as finite automata or
Turing machines.
Towards more accurate clustering method by using dynamic time warping
Authors: Khadoudja Ghanem
Date: 2013-04-12 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Dynamic Time Warping

An intrinsic problem of classifiers based on machine learning (ML) methods is
that their learning time grows as the size and complexity of the training
dataset increases. For this reason, it is important to have efficient
computational methods and algorithms that can be applied on large datasets,
such that it is still possible to complete the machine learning tasks in
reasonable time. In this context, we present in this paper a more accurate
simple process to speed up ML methods. An unsupervised clustering algorithm is
combined with Expectation, Maximization (EM) algorithm to develop an efficient
Hidden Markov Model (HMM) training. The idea of the proposed process consists
of two steps. In the first step, training instances with similar inputs are
clustered and a weight factor which represents the frequency of these instances
is assigned to each representative cluster. Dynamic Time Warping technique is
used as a dissimilarity function to cluster similar examples. In the second
step, all formulas in the classical HMM training algorithm (EM) associated with
the number of training instances are modified to include the weight factor in
appropriate terms. This process significantly accelerates HMM training while
maintaining the same initial, transition and emission probabilities matrixes as
those obtained with the classical HMM training algorithm. Accordingly, the
classification accuracy is preserved. Depending on the size of the training
set, speedups of up to 2200 times is possible when the size is about 100.000
instances. The proposed approach is not limited to training HMMs, but it can be
employed for a large variety of MLs methods.
General Quantum Hilbert Space Modeling Scheme for Entanglement
Authors: Diederik Aerts, Sandro Sozzo
Date: 2013-04-12 00:00:00
Tasks: General Classification

We work out a classification scheme for quantum modeling in Hilbert space of
any kind of composite entity violating Bell's inequalities and exhibiting
entanglement. Our theoretical framework includes situations with entangled
states and product measurements ('customary quantum situation'), and also
situations with both entangled states and entangled measurements ('nonlocal box
situation', 'nonlocal non-marginal box situation'). We show that entanglement
is structurally a joint property of states and measurements. Furthermore,
entangled measurements enable quantum modeling of situations that are usually
believed to be 'beyond quantum'. Our results are also extended from pure states
to quantum mixtures.
Advice-Efficient Prediction with Expert Advice
Authors: Yevgeny Seldin, Peter Bartlett, Koby Crammer
Date: 2013-04-12 00:00:00
Tasks: Prediction

Advice-efficient prediction with expert advice (in analogy to label-efficient
prediction) is a variant of prediction with expert advice game, where on each
round of the game we are allowed to ask for advice of a limited number $M$ out
of $N$ experts. This setting is especially interesting when asking for advice
of every expert on every round is expensive. We present an algorithm for
advice-efficient prediction with expert advice that achieves
$O(\sqrt{\frac{N}{M}T\ln N})$ regret on $T$ rounds of the game.
A Novel Metaheuristics To Solve Mixed Shop Scheduling Problems
Authors: V. Ravibabu
Date: 2013-04-12 00:00:00
Tasks: Scheduling

This paper represents the metaheuristics proposed for solving a class of Shop
Scheduling problem. The Bacterial Foraging Optimization algorithm is featured
with Ant Colony Optimization algorithm and proposed as a natural inspired
computing approach to solve the Mixed Shop Scheduling problem. The Mixed Shop
is the combination of Job Shop, Flow Shop and Open Shop scheduling problems.
The sample instances for all mentioned Shop problems are used as test data and
Mixed Shop survive its computational complexity to minimize the makespan. The
computational results show that the proposed algorithm is gentler to solve and
performs better than the existing algorithms.