pwc-archive/papers-with-abstracts huggingface dataset:
Graph Colouring Problem Based on Discrete Imperialist Competitive Algorithm
Authors: Hojjat Emami, Shahriar Lotfi
Date: 2013-08-17 00:00:00
Tasks: valid

In graph theory, Graph Colouring Problem (GCP) is an assignment of colours to
vertices of any given graph such that the colours on adjacent vertices are
different. The GCP is known to be an optimization and NP-hard problem.
Imperialist Competitive Algorithm (ICA) is a meta-heuristic optimization and
stochastic search strategy which is inspired from socio-political phenomenon of
imperialistic competition. The ICA contains two main operators: the
assimilation and the imperialistic competition. The ICA has excellent
capabilities such as high convergence rate and better global optimum
achievement. In this research, a discrete version of ICA is proposed to deal
with the solution of GCP. We call this algorithm as the DICA. The performance
of the proposed method is compared with Genetic Algorithm (GA) on seven
well-known graph colouring benchmarks. Experimental results demonstrate the
superiority of the DICA for the benchmarks. This means DICA can produce optimal
and valid solutions for different GCP instances.
From Causal Models To Counterfactual Structures
Authors: Joseph Y. Halpern
Date: 2011-06-14 00:00:00
Tasks: counterfactual

Galles and Pearl claimed that "for recursive models, the causal model
framework does not add any restrictions to counterfactuals, beyond those
imposed by Lewis's [possible-worlds] framework." This claim is examined
carefully, with the goal of clarifying the exact relationship between causal
models and Lewis's framework. Recursive models are shown to correspond
precisely to a subclass of (possible-world) counterfactual structures. On the
other hand, a slight generalization of recursive models, models where all
equations have unique solutions, is shown to be incomparable in expressive
power to counterfactual structures, despite the fact that the Galles and Pearl
arguments should apply to them as well. The problem with the Galles and Pearl
argument is identified: an axiom that they viewed as irrelevant, because it
involved disjunction (which was not in their language), is not irrelevant at
all.
Decision Theory with Resource-Bounded Agents
Authors: Joseph Y. Halpern, Rafael Pass, Lior Seeman
Date: 2013-08-17 00:00:00

There have been two major lines of research aimed at capturing
resource-bounded players in game theory. The first, initiated by Rubinstein,
charges an agent for doing costly computation; the second, initiated by Neyman,
does not charge for computation, but limits the computation that agents can do,
typically by modeling agents as finite automata. We review recent work on
applying both approaches in the context of decision theory. For the first
approach, we take the objects of choice in a decision problem to be Turing
machines, and charge players for the ``complexity'' of the Turing machine
chosen (e.g., its running time). This approach can be used to explain
well-known phenomena like first-impression-matters biases (i.e., people tend to
put more weight on evidence they hear early on) and belief polarization (two
people with different prior beliefs, hearing the same evidence, can end up with
diametrically opposed conclusions) as the outcomes of quite rational decisions.
For the second approach, we model people as finite automata, and provide a
simple algorithm that, on a problem that captures a number of settings of
interest, provably performs optimally as the number of states in the automaton
increases.
Comment on "robustness and regularization of support vector machines" by H. Xu, et al., (Journal of Machine Learning Research, vol. 10, pp. 1485-1510, 2009, arXiv:0803.3490)
Authors: Yahya Forghani, Hadi Sadoghi Yazdi
Date: 2013-08-17 00:00:00
Tasks: BIG-bench Machine Learning

This paper comments on the published work dealing with robustness and
regularization of support vector machines (Journal of Machine Learning
Research, vol. 10, pp. 1485-1510, 2009) [arXiv:0803.3490] by H. Xu, etc. They
proposed a theorem to show that it is possible to relate robustness in the
feature space and robustness in the sample space directly. In this paper, we
propose a counter example that rejects their theorem.
Development of Comprehensive Devnagari Numeral and Character Database for Offline Handwritten Character Recognition
Authors: Vikas J. Dongre, Vijay H. Mankar
Date: 2013-08-17 00:00:00
Tasks: Handwriting Recognition

In handwritten character recognition, benchmark database plays an important
role in evaluating the performance of various algorithms and the results
obtained by various researchers. In Devnagari script, there is lack of such
official benchmark. This paper focuses on the generation of offline benchmark
database for Devnagari handwritten numerals and characters. The present work
generated 5137 and 20305 isolated samples for numeral and character database,
respectively, from 750 writers of all ages, sex, education, and profession. The
offline sample images are stored in TIFF image format as it occupies less
memory. Also, the data is presented in binary level so that memory requirement
is further reduced. It will facilitate research on handwriting recognition of
Devnagari script through free access to the researchers.
A History of Cluster Analysis Using the Classification Society's Bibliography Over Four Decades
Authors: Fionn Murtagh, Michael J. Kurtz
Date: 2012-09-01 00:00:00
Tasks: Classification, General Classification, Management

The Classification Literature Automated Search Service, an annual
bibliography based on citation of one or more of a set of around 80 book or
journal publications, ran from 1972 to 2012. We analyze here the years 1994 to
2011. The Classification Society's Service, as it was termed, has been produced
by the Classification Society. In earlier decades it was distributed as a
diskette or CD with the Journal of Classification. Among our findings are the
following: an enormous increase in scholarly production post approximately
2000; a very major increase in quantity, coupled with work in different
disciplines, from approximately 2004; and a major shift also from cluster
analysis in earlier times having mathematics and psychology as disciplines of
the journals published in, and affiliations of authors, contrasted with, in
more recent times, a "centre of gravity" in management and engineering.
Standardizing Interestingness Measures for Association Rules
Authors: Mateen Shaikh, Paul D. McNicholas, M. Luiza Antonie, T. Brendan Murphy
Date: 2013-08-16 00:00:00

Interestingness measures provide information that can be used to prune or
select association rules. A given value of an interestingness measure is often
interpreted relative to the overall range of the values that the
interestingness measure can take. However, properties of individual association
rules restrict the values an interestingness measure can achieve. An
interesting measure can be standardized to take this into account, but this has
only been done for one interestingness measure to date, i.e., the lift.
Standardization provides greater insight than the raw value and may even alter
researchers' perception of the data. We derive standardized analogues of three
interestingness measures and use real and simulated data to compare them to
their raw versions, each other, and the standardized lift.
The Royal Birth of 2013: Analysing and Visualising Public Sentiment in the UK Using Twitter
Authors: Vu Dung Nguyen, Blesson Varghese, Adam Barker
Date: 2013-08-08 00:00:00
Tasks: BIG-bench Machine Learning, Sentiment Analysis

Analysis of information retrieved from microblogging services such as Twitter
can provide valuable insight into public sentiment in a geographic region. This
insight can be enriched by visualising information in its geographic context.
Two underlying approaches for sentiment analysis are dictionary-based and
machine learning. The former is popular for public sentiment analysis, and the
latter has found limited use for aggregating public sentiment from Twitter
data. The research presented in this paper aims to extend the machine learning
approach for aggregating public sentiment. To this end, a framework for
analysing and visualising public sentiment from a Twitter corpus is developed.
A dictionary-based approach and a machine learning approach are implemented
within the framework and compared using one UK case study, namely the royal
birth of 2013. The case study validates the feasibility of the framework for
analysis and rapid visualisation. One observation is that there is good
correlation between the results produced by the popular dictionary-based
approach and the machine learning approach when large volumes of tweets are
analysed. However, for rapid analysis to be possible faster methods need to be
developed using big data techniques and parallel methods.
Fast Stochastic Alternating Direction Method of Multipliers
Authors: Leon Wenliang Zhong, James T. Kwok
Date: 2013-08-16 00:00:00

In this paper, we propose a new stochastic alternating direction method of
multipliers (ADMM) algorithm, which incrementally approximates the full
gradient in the linearized ADMM formulation. Besides having a low per-iteration
complexity as existing stochastic ADMM algorithms, the proposed algorithm
improves the convergence rate on convex problems from $O(\frac 1 {\sqrt{T}})$
to $O(\frac 1 T)$, where $T$ is the number of iterations. This matches the
convergence rate of the batch ADMM algorithm, but without the need to visit all
the samples in each iteration. Experiments on the graph-guided fused lasso
demonstrate that the new algorithm is significantly faster than
state-of-the-art stochastic and batch ADMM algorithms.
Innovative Second-Generation Wavelets Construction With Recurrent Neural Networks for Solar Radiation Forecasting
Authors: Giacomo Capizzi, Christian Napoli, Francesco Bonanno
Date: 2013-08-15 00:00:00
Tasks: Prediction, Time Series, Time Series Analysis

Solar radiation prediction is an important challenge for the electrical
engineer because it is used to estimate the power developed by commercial
photovoltaic modules. This paper deals with the problem of solar radiation
prediction based on observed meteorological data. A 2-day forecast is obtained
by using novel wavelet recurrent neural networks (WRNNs). In fact, these WRNNS
are used to exploit the correlation between solar radiation and
timescale-related variations of wind speed, humidity, and temperature. The
input to the selected WRNN is provided by timescale-related bands of wavelet
coefficients obtained from meteorological time series. The experimental setup
available at the University of Catania, Italy, provided this information. The
novelty of this approach is that the proposed WRNN performs the prediction in
the wavelet domain and, in addition, also performs the inverse wavelet
transform, giving the predicted signal as output. The obtained simulation
results show a very low root-mean-square error compared to the results of the
solar radiation prediction approaches obtained by hybrid neural networks
reported in the recent literature.
Hidden Parameter Markov Decision Processes: A Semiparametric Regression Approach for Discovering Latent Task Parametrizations
Authors: Finale Doshi-Velez, George Konidaris
Date: 2013-08-15 00:00:00
Tasks: regression

Control applications often feature tasks with similar, but not identical,
dynamics. We introduce the Hidden Parameter Markov Decision Process (HiP-MDP),
a framework that parametrizes a family of related dynamical systems with a
low-dimensional set of latent factors, and introduce a semiparametric
regression approach for learning its structure from data. In the control
setting, we show that a learned HiP-MDP rapidly identifies the dynamics of a
new task instance, allowing an agent to flexibly adapt to task variations.
Stochastic Optimization for Machine Learning
Authors: Andrew Cotter
Date: 2013-08-15 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, Stochastic Optimization

It has been found that stochastic algorithms often find good solutions much
more rapidly than inherently-batch approaches. Indeed, a very useful rule of
thumb is that often, when solving a machine learning problem, an iterative
technique which relies on performing a very large number of
relatively-inexpensive updates will often outperform one which performs a
smaller number of much "smarter" but computationally-expensive updates.
  In this thesis, we will consider the application of stochastic algorithms to
two of the most important machine learning problems. Part i is concerned with
the supervised problem of binary classification using kernelized linear
classifiers, for which the data have labels belonging to exactly two classes
(e.g. "has cancer" or "doesn't have cancer"), and the learning problem is to
find a linear classifier which is best at predicting the label. In Part ii, we
will consider the unsupervised problem of Principal Component Analysis, for
which the learning task is to find the directions which contain most of the
variance of the data distribution.
  Our goal is to present stochastic algorithms for both problems which are,
above all, practical--they work well on real-world data, in some cases better
than all known competing algorithms. A secondary, but still very important,
goal is to derive theoretical bounds on the performance of these algorithms
which are at least competitive with, and often better than, those known for
other approaches.
Computational Rationalization: The Inverse Equilibrium Problem
Authors: Kevin Waugh, Brian D. Ziebart, J. Andrew Bagnell
Date: 2013-08-15 00:00:00

Modeling the purposeful behavior of imperfect agents from a small number of
observations is a challenging task. When restricted to the single-agent
decision-theoretic setting, inverse optimal control techniques assume that
observed behavior is an approximately optimal solution to an unknown decision
problem. These techniques learn a utility function that explains the example
behavior and can then be used to accurately predict or imitate future behavior
in similar observed or unobserved situations.
  In this work, we consider similar tasks in competitive and cooperative
multi-agent domains. Here, unlike single-agent settings, a player cannot
myopically maximize its reward; it must speculate on how the other agents may
act to influence the game's outcome. Employing the game-theoretic notion of
regret and the principle of maximum entropy, we introduce a technique for
predicting and generalizing behavior.
Using Incomplete Information for Complete Weight Annotation of Road Networks -- Extended Version
Authors: Bin Yang, Manohar Kaul, Christian S. Jensen
Date: 2013-08-02 00:00:00

We are witnessing increasing interests in the effective use of road networks.
For example, to enable effective vehicle routing, weighted-graph models of
transportation networks are used, where the weight of an edge captures some
cost associated with traversing the edge, e.g., greenhouse gas (GHG) emissions
or travel time. It is a precondition to using a graph model for routing that
all edges have weights. Weights that capture travel times and GHG emissions can
be extracted from GPS trajectory data collected from the network. However, GPS
trajectory data typically lack the coverage needed to assign weights to all
edges. This paper formulates and addresses the problem of annotating all edges
in a road network with travel cost based weights from a set of trips in the
network that cover only a small fraction of the edges, each with an associated
ground-truth travel cost. A general framework is proposed to solve the problem.
Specifically, the problem is modeled as a regression problem and solved by
minimizing a judiciously designed objective function that takes into account
the topology of the road network. In particular, the use of weighted PageRank
values of edges is explored for assigning appropriate weights to all edges, and
the property of directional adjacency of edges is also taken into account to
assign weights. Empirical studies with weights capturing travel time and GHG
emissions on two road networks (Skagen, Denmark, and North Jutland, Denmark)
offer insight into the design properties of the proposed techniques and offer
evidence that the techniques are effective.
Quantum Entanglement in Concept Combinations
Authors: Diederik Aerts, Sandro Sozzo
Date: 2013-02-15 00:00:00

Research in the application of quantum structures to cognitive science
confirms that these structures quite systematically appear in the dynamics of
concepts and their combinations and quantum-based models faithfully represent
experimental data of situations where classical approaches are problematical.
In this paper, we analyze the data we collected in an experiment on a specific
conceptual combination, showing that Bell's inequalities are violated in the
experiment. We present a new refined entanglement scheme to model these data
within standard quantum theory rules, where 'entangled measurements and
entangled evolutions' occur, in addition to the expected 'entangled states',
and present a full quantum representation in complex Hilbert space of the data.
This stronger form of entanglement in measurements and evolutions might have
relevant applications in the foundations of quantum theory, as well as in the
interpretation of nonlocality tests. It could indeed explain some
non-negligible 'anomalies' identified in EPR-Bell experiments.
Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
Authors: Yoshua Bengio, Nicholas Léonard, Aaron Courville
Date: 2013-08-15 00:00:00

Stochastic neurons and hard non-linearities can be useful for a number of
reasons in deep learning models, but in many cases they pose a challenging
problem: how to estimate the gradient of a loss function with respect to the
input of such stochastic or non-smooth neurons? I.e., can we "back-propagate"
through these stochastic neurons? We examine this question, existing
approaches, and compare four families of solutions, applicable in different
settings. One of them is the minimum variance unbiased gradient estimator for
stochatic binary neurons (a special case of the REINFORCE algorithm). A second
approach, introduced here, decomposes the operation of a binary stochastic
neuron into a stochastic binary part and a smooth differentiable part, which
approximates the expected effect of the pure stochatic binary neuron to first
order. A third approach involves the injection of additive or multiplicative
noise in a computational graph that is otherwise differentiable. A fourth
approach heuristically copies the gradient with respect to the stochastic
output directly as an estimator of the gradient with respect to the sigmoid
argument (we call this the straight-through estimator). To explore a context
where these estimators are useful, we consider a small-scale version of {\em
conditional computation}, where sparse stochastic units form a distributed
representation of gaters that can turn off in combinatorially many ways large
chunks of the computation performed in the rest of the neural network. In this
case, it is important that the gating units produce an actual 0 most of the
time. The resulting sparsity can be potentially be exploited to greatly reduce
the computational cost of large deep networks for which conditional computation
would be useful.
Probabilistic Event Calculus for Event Recognition
Authors: Anastasios Skarlatidis, Georgios Paliouras, Alexander Artikis, George A. Vouros
Date: 2012-07-13 00:00:00
Tasks: Activity Recognition

Symbolic event recognition systems have been successfully applied to a
variety of application domains, extracting useful information in the form of
events, allowing experts or other systems to monitor and respond when
significant events are recognised. In a typical event recognition application,
however, these systems often have to deal with a significant amount of
uncertainty. In this paper, we address the issue of uncertainty in logic-based
event recognition by extending the Event Calculus with probabilistic reasoning.
Markov Logic Networks are a natural candidate for our logic-based formalism.
However, the temporal semantics of the Event Calculus introduce a number of
challenges for the proposed model. We show how and under what assumptions we
can overcome these problems. Additionally, we study how probabilistic modelling
changes the behaviour of the formalism, affecting its key property, the inertia
of fluents. Furthermore, we demonstrate the advantages of the probabilistic
Event Calculus through examples and experiments in the domain of activity
recognition, using a publicly available dataset for video surveillance.
Learning ambiguous functions by neural networks
Authors: Rui Ligeiro, R. Vilela Mendes
Date: 2013-08-15 00:00:00

It is not, in general, possible to have access to all variables that
determine the behavior of a system. Having identified a number of variables
whose values can be accessed, there may still be hidden variables which
influence the dynamics of the system. The result is model ambiguity in the
sense that, for the same (or very similar) input values, different objective
outputs should have been obtained. In addition, the degree of ambiguity may
vary widely across the whole range of input values. Thus, to evaluate the
accuracy of a model it is of utmost importance to create a method to obtain the
degree of reliability of each output result. In this paper we present such a
scheme composed of two coupled artificial neural networks: the first one being
responsible for outputting the predicted value, whereas the other evaluates the
reliability of the output, which is learned from the error values of the first
one. As an illustration, the scheme is applied to a model for tracking slopes
in a straw chamber and to a credit scoring model.
History Based Coalition Formation in Hedonic Context Using Trust
Authors: Ahmadreza Ghaffarizadeh, Vicki H. Allan
Date: 2013-08-15 00:00:00

In this paper we address the problem of coalition formation in hedonic
context. Our modelling tries to be as realistic as possible. In previous
models, once an agent joins a coalition it would not be able to leave the
coalition and join the new one; in this research we made it possible to leave a
coalition but put some restrictions to control the behavior of agents. Leaving
or staying of an agent in a coalition will affect on the trust of the other
agents included in this coalition. Agents will use the trust values in
computing the expected utility of coalitions. Three different risk behaviors
are introduced for agents that want to initiate a coalition. Using these risk
behaviors, some simulations are made and results are analyzed.
The algorithm of noisy k-means
Authors: Camille Brunet, Sébastien Loustau
Date: 2013-08-15 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

In this note, we introduce a new algorithm to deal with finite dimensional
clustering with errors in variables. The design of this algorithm is based on
recent theoretical advances (see Loustau (2013a,b)) in statistical learning
with errors in variables. As the previous mentioned papers, the algorithm mixes
different tools from the inverse problem literature and the machine learning
community. Coarsely, it is based on a two-step procedure: (1) a deconvolution
step to deal with noisy inputs and (2) Newton's iterations as the popular
k-means.