pwc-archive/papers-with-abstracts huggingface dataset:
Military Simulator - A Case Study of Behaviour Tree and Unity based architecture
Authors: Shruti Jadon, Anubhav Singhal, Suma Dawn
Date: 2014-05-30 00:00:00
Tasks: Unity

In this paper we show how the combination of Behaviour Tree and Utility Based
AI architecture can be used to design more realistic bots for Military
Simulators. In this work, we have designed a mathematical model of a simulator
system which in turn helps in analyzing the results and finding out the various
spaces on which our favorable situation might exist, this is done
geometrically. In the mathematical model, we have explained the matrix
formation and its significance followed up in dynamic programming approach we
explained the possible graph formation which will led improvisation of AI,
latter we explained the possible geometrical structure of the matrix operations
and its impact on a particular decision, we also explained the conditions under
which it tend to fail along with a possible solution in future works.
Semantic Composition and Decomposition: From Recognition to Generation
Authors: Peter D. Turney
Date: 2014-05-30 00:00:00
Tasks: Semantic Composition

Semantic composition is the task of understanding the meaning of text by
composing the meanings of the individual words in the text. Semantic
decomposition is the task of understanding the meaning of an individual word by
decomposing it into various aspects (factors, constituents, components) that
are latent in the meaning of the word. We take a distributional approach to
semantics, in which a word is represented by a context vector. Much recent work
has considered the problem of recognizing compositions and decompositions, but
we tackle the more difficult generation problem. For simplicity, we focus on
noun-modifier bigrams and noun unigrams. A test for semantic composition is,
given context vectors for the noun and modifier in a noun-modifier bigram ("red
salmon"), generate a noun unigram that is synonymous with the given bigram
("sockeye"). A test for semantic decomposition is, given a context vector for a
noun unigram ("snifter"), generate a noun-modifier bigram that is synonymous
with the given unigram ("brandy glass"). With a vocabulary of about 73,000
unigrams from WordNet, there are 73,000 candidate unigram compositions for a
bigram and 5,300,000,000 (73,000 squared) candidate bigram decompositions for a
unigram. We generate ranked lists of potential solutions in two passes. A fast
unsupervised learning algorithm generates an initial list of candidates and
then a slower supervised learning algorithm refines the list. We evaluate the
candidate solutions by comparing them to WordNet synonym sets. For
decomposition (unigram to bigram), the top 100 most highly ranked bigrams
include a WordNet synonym of the given unigram 50.7% of the time. For
composition (bigram to unigram), the top 100 most highly ranked unigrams
include a WordNet synonym of the given bigram 77.8% of the time.
The Shortlist Method for Fast Computation of the Earth Mover's Distance and Finding Optimal Solutions to Transportation Problems
Authors: Carsten Gottschlich, Dominic Schuhmacher
Date: 2014-05-30 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Object Tracking, Retrieval

Finding solutions to the classical transportation problem is of great
importance, since this optimization problem arises in many engineering and
computer science applications. Especially the Earth Mover's Distance is used in
a plethora of applications ranging from content-based image retrieval, shape
matching, fingerprint recognition, object tracking and phishing web page
detection to computing color differences in linguistics and biology. Our
starting point is the well-known revised simplex algorithm, which iteratively
improves a feasible solution to optimality. The Shortlist Method that we
propose substantially reduces the number of candidates inspected for improving
the solution, while at the same time balancing the number of pivots required.
Tests on simulated benchmarks demonstrate a considerable reduction in
computation time for the new method as compared to the usual revised simplex
algorithm implemented with state-of-the-art initialization and pivot
strategies. As a consequence, the Shortlist Method facilitates the computation
of large scale transportation problems in viable time. In addition we describe
a novel method for finding an initial feasible solution which we coin Modified
Russell's Method.
Flip-Flop Sublinear Models for Graphs: Proof of Theorem 1
Authors: Brijnesh Jain
Date: 2014-05-30 00:00:00

We prove that there is no class-dual for almost all sublinear models on
graphs.
Stochastic Backpropagation and Approximate Inference in Deep Generative Models
Authors: Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra
Date: 2014-01-16 00:00:00
Tasks: Bayesian Inference

We marry ideas from deep neural networks and approximate Bayesian inference
to derive a generalised class of deep, directed generative models, endowed with
a new algorithm for scalable inference and learning. Our algorithm introduces a
recognition model to represent approximate posterior distributions, and that
acts as a stochastic encoder of the data. We develop stochastic
back-propagation -- rules for back-propagation through stochastic variables --
and use this to develop an algorithm that allows for joint optimisation of the
parameters of both the generative and recognition model. We demonstrate on
several real-world data sets that the model generates realistic samples,
provides accurate imputations of missing data and is a useful tool for
high-dimensional data visualisation.
ELM Solutions for Event-Based Systems
Authors: Jonathan Tapson, André van Schaik
Date: 2014-05-30 00:00:00

Whilst most engineered systems use signals that are continuous in time, there
is a domain of systems in which signals consist of events. Events, like Dirac
delta functions, have no meaningful time duration. Many important real-world
systems are intrinsically event-based, including the mammalian brain, in which
the primary packets of data are spike events, or action potentials. In this
domain, signal processing requires responses to spatio-temporal patterns of
events. We show that some straightforward modifications to the standard ELM
topology produce networks that are able to perform spatio-temporal event
processing online with a high degree of accuracy. The modifications involve the
re-definition of hidden layer units as synaptic kernels, in which the input
delta functions are transformed into continuous-valued signals using a variety
of impulse-response functions. This permits the use of linear solution methods
in the output layer, which can produce events as output, if modeled as a
classifier; the output classes are 'event' or 'no event'. We illustrate the
method in application to a spike-processing problem.
Online and Adaptive Pseudoinverse Solutions for ELM Weights
Authors: André van Schaik, Jonathan Tapson
Date: 2014-05-30 00:00:00
Tasks: General Classification

The ELM method has become widely used for classification and regressions
problems as a result of its accuracy, simplicity and ease of use. The solution
of the hidden layer weights by means of a matrix pseudoinverse operation is a
significant contributor to the utility of the method; however, the conventional
calculation of the pseudoinverse by means of a singular value decomposition
(SVD) is not always practical for large data sets or for online updates to the
solution. In this paper we discuss incremental methods for solving the
pseudoinverse which are suitable for ELM. We show that careful choice of
methods allows us to optimize for accuracy, ease of computation, or
adaptability of the solution.
DEM Registration and Error Analysis using ASCII values
Authors: Suma Dawn, Vikas Saxena, Bhu Dev Sharma
Date: 2014-05-30 00:00:00
Tasks: Graph Matching

Digital Elevation Model (DEM), while providing a bare earth look, is heavily
used in many applications including construction modeling, visualization, and
GIS. Their registration techniques have not been explored much. Methods like
Coarse-to-fine or pyramid making are common in DEM-to-image or DEM-to-map
registration. Self-consistency measure is used to detect any change in terrain
elevation and hence was used for DEM-to-DEM registration. But these methods
apart from being time and complexity intensive, lack in error matrix
evaluation. This paper gives a method of registration of DEMs using specified
height values as control points by initially converting these DEMs to ASCII
files. These control points may be found by two mannerisms - either by direct
detection of appropriate height data in ASCII files or by edge matching along
congruous quadrangle of the control point, followed by sub-graph matching.
Error analysis for the same has also been done.
Efficient State-Space Inference of Periodic Latent Force Models
Authors: Steven Reece, Stephen Roberts, Siddhartha Ghosh, Alex Rogers, Nicholas Jennings
Date: 2013-10-23 00:00:00
Tasks: Computational Efficiency

Latent force models (LFM) are principled approaches to incorporating
solutions to differential equations within non-parametric inference methods.
Unfortunately, the development and application of LFMs can be inhibited by
their computational cost, especially when closed-form solutions for the LFM are
unavailable, as is the case in many real world problems where these latent
forces exhibit periodic behaviour. Given this, we develop a new sparse
representation of LFMs which considerably improves their computational
efficiency, as well as broadening their applicability, in a principled way, to
domains with periodic or near periodic latent forces. Our approach uses a
linear basis model to approximate one generative model for each periodic force.
We assume that the latent forces are generated from Gaussian process priors and
develop a linear basis model which fully expresses these priors. We apply our
approach to model the thermal dynamics of domestic buildings and show that it
is effective at predicting day-ahead temperatures within the homes. We also
apply our approach within queueing theory in which quasi-periodic arrival rates
are modelled as latent forces. In both cases, we demonstrate that our approach
can be implemented efficiently using state-space methods which encode the
linear dynamic systems via LFMs. Further, we show that state estimates obtained
using periodic latent force models can reduce the root mean squared error to
17% of that from non-periodic models and 27% of the nearest rival approach
which is the resonator model.
Managing Change in Graph-structured Data Using Description Logics (long version with appendix)
Authors: Shqiponja Ahmetaj, Diego Calvanese, Magdalena Ortiz, Mantas Simkus
Date: 2014-04-16 00:00:00
Tasks: Management

In this paper, we consider the setting of graph-structured data that evolves
as a result of operations carried out by users or applications. We study
different reasoning problems, which range from ensuring the satisfaction of a
given set of integrity constraints after a given sequence of updates, to
deciding the (non-)existence of a sequence of actions that would take the data
to an (un)desirable state, starting either from a specific data instance or
from an incomplete description of it. We consider an action language in which
actions are finite sequences of conditional insertions and deletions of nodes
and labels, and use Description Logics for describing integrity constraints and
(partial) states of the data. We then formalize the above data management
problems as a static verification problem and several planning problems. We
provide algorithms and tight complexity bounds for the formalized problems,
both for an expressive DL and for a variant of DL-Lite.
Classification of Basmati Rice Grain Variety using Image Processing and Principal Component Analysis
Authors: Rubi Kambo, Amit Yerpude
Date: 2014-05-29 00:00:00
Tasks: Classification, General Classification

All important decisions about the variety of rice grain end product are based
on the different features of rice grain.There are various methods available for
classification of basmati rice. This paper proposed a new principal component
analysis based approach for classification of different variety of basmati
rice. The experimental result shows the effectiveness of the proposed
methodology for various samples of different variety of basmati rice.
Simultaneous Feature and Expert Selection within Mixture of Experts
Authors: Billy Peralta
Date: 2014-05-29 00:00:00
Tasks: feature selection, Mixture-of-Experts

A useful strategy to deal with complex classification scenarios is the
"divide and conquer" approach. The mixture of experts (MOE) technique makes use
of this strategy by joinly training a set of classifiers, or experts, that are
specialized in different regions of the input space. A global model, or gate
function, complements the experts by learning a function that weights their
relevance in different parts of the input space. Local feature selection
appears as an attractive alternative to improve the specialization of experts
and gate function, particularly, for the case of high dimensional data. Our
main intuition is that particular subsets of dimensions, or subspaces, are
usually more appropriate to classify instances located in different regions of
the input space. Accordingly, this work contributes with a regularized variant
of MoE that incorporates an embedded process for local feature selection using
$L1$ regularization, with a simultaneous expert selection. The experiments are
still pending.
Functional Gaussian processes for regression with linear PDE models
Authors: Ngoc-Cuong Nguyen, Jaime Peraire
Date: 2014-05-29 00:00:00
Tasks: Gaussian Processes, regression

In this paper, we present a new statistical approach to the problem of
incorporating experimental observations into a mathematical model described by
linear partial differential equations (PDEs) to improve the prediction of the
state of a physical system. We augment the linear PDE with a functional that
accounts for the uncertainty in the mathematical model and is modeled as a {\em
Gaussian process}. This gives rise to a stochastic PDE which is characterized
by the Gaussian functional. We develop a {\em functional Gaussian process
regression} method to determine the posterior mean and covariance of the
Gaussian functional, thereby solving the stochastic PDE to obtain the posterior
distribution for our prediction of the physical state. Our method has the
following features which distinguish itself from other regression methods.
First, it incorporates both the mathematical model and the observations into
the regression procedure. Second, it can handle the observations given in the
form of linear functionals of the field variable. Third, the method is
non-parametric in the sense that it provides a systematic way to optimally
determine the prior covariance operator of the Gaussian functional based on the
observations. Fourth, it provides the posterior distribution quantifying the
magnitude of uncertainty in our prediction of the physical state. We present
numerical results to illustrate these features of the method and compare its
performance to that of the standard Gaussian process regression.
Analogy-Based and Case-Based Reasoning: Two sides of the same coin
Authors: Michael Gr. Voskoglou, Abdel-Badeeh M. Salem
Date: 2014-05-29 00:00:00

Analogy-Based (or Analogical) and Case-Based Reasoning (ABR and CBR) are two
similar problem solving processes based on the adaptation of the solution of
past problems for use with a new analogous problem. In this paper we review
these two processes and we give some real world examples with emphasis to the
field of Medicine, where one can find some of the most common and useful CBR
applications. We also underline the differences between CBR and the classical
rule-induction algorithms, we discuss the criticism for CBR methods and we
focus on the future trends of research in the area of CBR.
Feature sampling and partitioning for visual vocabulary generation on large action classification datasets
Authors: Michael Sapienza, Fabio Cuzzolin, Philip H. S. Torr
Date: 2014-05-29 00:00:00
Tasks: Action Classification, Action Recognition, General Classification, Temporal Action Localization

The recent trend in action recognition is towards larger datasets, an
increasing number of action classes and larger visual vocabularies.
State-of-the-art human action classification in challenging video data is
currently based on a bag-of-visual-words pipeline in which space-time features
are aggregated globally to form a histogram. The strategies chosen to sample
features and construct a visual vocabulary are critical to performance, in fact
often dominating performance. In this work we provide a critical evaluation of
various approaches to building a vocabulary and show that good practises do
have a significant impact. By subsampling and partitioning features
strategically, we are able to achieve state-of-the-art results on 5 major
action recognition datasets using relatively small visual vocabularies.
Aspect Based Sentiment Analysis to Extract Meticulous Opinion Value
Authors: Deepali Virmani, Vikrant Malhotra, Ridhi Tyagi
Date: 2014-05-29 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Opinion Mining, Sentiment Analysis

Opinion Mining and Sentiment Analysis is a process of identifying opinions in
large unstructured/structured data and then analysing polarity of those
opinions. Opinion mining and sentiment analysis have found vast application in
analysing online ratings, analysing product based reviews, e-governance, and
managing hostile content over the internet. This paper proposes an algorithm to
implement aspect level sentiment analysis. The algorithm takes input from the
remarks submitted by various teachers of a student. An aspect tree is formed
which has various levels and weights are assigned to each branch to identify
level of aspect. Aspect value is calculated by the algorithm by means of the
proposed aspect tree. Dictionary based method is implemented to evaluate the
polarity of the remark. The algorithm returns the aspect value clubbed with
opinion value and sentiment value which helps in concluding the summarized
value of remark.
Knowledge Base of an Expert System Used for Dyslalic Children Therapy
Authors: Ovidiu-Andrei Schipor, Stefan-Gheorghe Pentiuc, Doina-Maria Schipor
Date: 2014-05-29 00:00:00

In order to improve children speech therapy, we develop a Fuzzy Expert System
based on a speech therapy guide. This guide, write in natural language, was
formalized using fuzzy logic paradigm. In this manner we obtain a knowledge
base with over 150 rules and 19 linguistic variables. All these researches,
including expert system validation, are part of TERAPERS project.
Architecture of a Fuzzy Expert System Used for Dyslalic Children Therapy
Authors: Ovidiu-Andrei Schipor, Stefan-Gheorghe Pentiuc, Maria-Doina Schipor
Date: 2014-05-29 00:00:00

In this paper we present architecture of a fuzzy expert system used for
therapy of dyslalic children. With fuzzy approach we can create a better model
for speech therapist decisions. A software interface was developed for
validation of the system. The main objectives of this task are: personalized
therapy (the therapy must be in according with child's problems level, context
and possibilities), speech therapist assistant (the expert system offer some
suggestion regarding what exercises are better for a specific moment and from a
specific child), (self) teaching (when system's conclusion is different that
speech therapist's conclusion the last one must have the knowledge base change
possibility).
Effect of Different Distance Measures on the Performance of K-Means Algorithm: An Experimental Study in Matlab
Authors: Mr. Dibya Jyoti Bora, Dr. Anil Kumar Gupta
Date: 2014-05-29 00:00:00
Tasks: Clustering

K-means algorithm is a very popular clustering algorithm which is famous for
its simplicity. Distance measure plays a very important rule on the performance
of this algorithm. We have different distance measure techniques available. But
choosing a proper technique for distance calculation is totally dependent on
the type of the data that we are going to cluster. In this paper an
experimental study is done in Matlab to cluster the iris and wine data sets
with different distance measures and thereby observing the variation of the
performances shown.
How Hard Is It to Control an Election by Breaking Ties?
Authors: Nicholas Mattei, Nina Narodytska, Toby Walsh
Date: 2013-04-23 00:00:00

We study the computational complexity of controlling the result of an
election by breaking ties strategically. This problem is equivalent to the
problem of deciding the winner of an election under parallel universes
tie-breaking. When the chair of the election is only asked to break ties to
choose between one of the co-winners, the problem is trivially easy. However,
in multi-round elections, we prove that it can be NP-hard for the chair to
compute how to break ties to ensure a given result. Additionally, we show that
the form of the tie-breaking function can increase the opportunities for
control. Indeed, we prove that it can be NP-hard to control an election by
breaking ties even with a two-stage voting rule.
Universal Compression of Envelope Classes: Tight Characterization via Poisson Sampling
Authors: Jayadev Acharya, Ashkan Jafarpour, Alon Orlitsky, Ananda Theertha Suresh
Date: 2014-05-29 00:00:00

The Poisson-sampling technique eliminates dependencies among symbol
appearances in a random sequence. It has been used to simplify the analysis and
strengthen the performance guarantees of randomized algorithms. Applying this
method to universal compression, we relate the redundancies of fixed-length and
Poisson-sampled sequences, use the relation to derive a simple single-letter
formula that approximates the redundancy of any envelope class to within an
additive logarithmic term. As a first application, we consider i.i.d.
distributions over a small alphabet as a step-envelope class, and provide a
short proof that determines the redundancy of discrete distributions over a
small al- phabet up to the first order terms. We then show the strength of our
method by applying the formula to tighten the existing bounds on the redundancy
of exponential and power-law classes, in particular answering a question posed
by Boucheron, Garivier and Gassiat.