pwc-archive/papers-with-abstracts huggingface dataset:
Parallel Support Vector Machines in Practice
Authors: Stephen Tyree, Jacob R. Gardner, Kilian Q. Weinberger, Kunal Agrawal, John Tran
Date: 2014-04-03 00:00:00
Tasks: GPU

In this paper, we evaluate the performance of various parallel optimization
methods for Kernel Support Vector Machines on multicore CPUs and GPUs. In
particular, we provide the first comparison of algorithms with explicit and
implicit parallelization. Most existing parallel implementations for multi-core
or GPU architectures are based on explicit parallelization of Sequential
Minimal Optimization (SMO)---the programmers identified parallelizable
components and hand-parallelized them, specifically tuned for a particular
architecture. We compare these approaches with each other and with implicitly
parallelized algorithms---where the algorithm is expressed such that most of
the work is done within few iterations with large dense linear algebra
operations. These can be computed with highly-optimized libraries, that are
carefully parallelized for a large variety of parallel platforms. We highlight
the advantages and disadvantages of both approaches and compare them on various
benchmark data sets. We find an approximate implicitly parallel algorithm which
is surprisingly efficient, permits a much simpler implementation, and leads to
unprecedented speedups in SVM training.
Bayes and Naive Bayes Classifier
Authors: Vikramkumar, Vijaykumar B, Trilochan
Date: 2014-04-03 00:00:00
Tasks: Classification, General Classification

The Bayesian Classification represents a supervised learning method as well
as a statistical method for classification. Assumes an underlying probabilistic
model and it allows us to capture uncertainty about the model in a principled
way by determining probabilities of the outcomes. This Classification is named
after Thomas Bayes (1702-1761), who proposed the Bayes Theorem. Bayesian
classification provides practical learning algorithms and prior knowledge and
observed data can be combined. Bayesian Classification provides a useful
perspective for understanding and evaluating many learning algorithms. It
calculates explicit probabilities for hypothesis and it is robust to noise in
input data. In statistical classification the Bayes classifier minimises the
probability of misclassification. That was a visual intuition for a simple case
of the Bayes classifier, also called: 1)Idiot Bayes 2)Naive Bayes 3)Simple
Bayes
Affect Control Processes: Intelligent Affective Interaction using a Partially Observable Markov Decision Process
Authors: Jesse Hoey, Tobias Schroeder, Areej Alhothali
Date: 2013-06-22 00:00:00

This paper describes a novel method for building affectively intelligent
human-interactive agents. The method is based on a key sociological insight
that has been developed and extensively verified over the last twenty years,
but has yet to make an impact in artificial intelligence. The insight is that
resource bounded humans will, by default, act to maintain affective
consistency. Humans have culturally shared fundamental affective sentiments
about identities, behaviours, and objects, and they act so that the transient
affective sentiments created during interactions confirm the fundamental
sentiments. Humans seek and create situations that confirm or are consistent
with, and avoid and supress situations that disconfirm or are inconsistent
with, their culturally shared affective sentiments. This "affect control
principle" has been shown to be a powerful predictor of human behaviour. In
this paper, we present a probabilistic and decision-theoretic generalisation of
this principle, and we demonstrate how it can be leveraged to build affectively
intelligent artificial agents. The new model, called BayesAct, can maintain
multiple hypotheses about sentiments simultaneously as a probability
distribution, and can make use of an explicit utility function to make
value-directed action choices. This allows the model to generate affectively
intelligent interactions with people by learning about their identity,
predicting their behaviours using the affect control principle, and taking
actions that are simultaneously goal-directed and affect-sensitive. We
demonstrate this generalisation with a set of simulations. We then show how our
model can be used as an emotional "plug-in" for artificially intelligent
systems that interact with humans in two different settings: an exam practice
assistant (tutor) and an assistive device for persons with a cognitive
disability.
On a correlational clustering of integers
Authors: L. Aszalós, L. Hajdu, A. Pethő
Date: 2014-04-03 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

Correlation clustering is a concept of machine learning. The ultimate goal of
such a clustering is to find a partition with minimal conflicts. In this paper
we investigate a correlation clustering of integers, based upon the greatest
common divisor.
A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem
Authors: Jun He, Feidun He, Hongbin Dong
Date: 2014-04-03 00:00:00

The 0-1 knapsack problem is a well-known combinatorial optimisation problem.
Approximation algorithms have been designed for solving it and they return
provably good solutions within polynomial time. On the other hand, genetic
algorithms are well suited for solving the knapsack problem and they find
reasonably good solutions quickly. A naturally arising question is whether
genetic algorithms are able to find solutions as good as approximation
algorithms do. This paper presents a novel multi-objective optimisation genetic
algorithm for solving the 0-1 knapsack problem. Experiment results show that
the new algorithm outperforms its rivals, the greedy algorithm, mixed strategy
genetic algorithm, and greedy algorithm + mixed strategy genetic algorithm.
Enabling Automatic Certification of Online Auctions
Authors: Wei Bai, Emmanuel M. Tadjouddine, Yu Guo
Date: 2014-04-03 00:00:00
Tasks: Translation

We consider the problem of building up trust in a network of online auctions
by software agents. This requires agents to have a deeper understanding of
auction mechanisms and be able to verify desirable properties of a given
mechanism. We have shown how these mechanisms can be formalised as semantic web
services in OWL-S, a good enough expressive machine-readable formalism enabling
software agents, to discover, invoke, and execute a web service. We have also
used abstract interpretation to translate the auction's specifications from
OWL-S, based on description logic, to COQ, based on typed lambda calculus, in
order to enable automatic verification of desirable properties of the auction
by the software agents. For this language translation, we have discussed the
syntactic transformation as well as the semantics connections between both
concrete and abstract domains. This work contributes to the implementation of
the vision of agent-mediated e-commerce systems.
Application of Ontologies in Identifying Requirements Patterns in Use Cases
Authors: Rui Couto, António Nestor Ribeiro, José Creissac Campos
Date: 2014-04-03 00:00:00

Use case specifications have successfully been used for requirements
description. They allow joining, in the same modeling space, the expectations
of the stakeholders as well as the needs of the software engineer and analyst
involved in the process. While use cases are not meant to describe a system's
implementation, by formalizing their description we are able to extract
implementation relevant information from them. More specifically, we are
interested in identifying requirements patterns (common requirements with
typical implementation solutions) in support for a requirements based software
development approach. In the paper we propose the transformation of Use Case
descriptions expressed in a Controlled Natural Language into an ontology
expressed in the Web Ontology Language (OWL). OWL's query engines can then be
used to identify requirements patterns expressed as queries over the ontology.
We describe a tool that we have developed to support the approach and provide
an example of usage.
A Resolution Prover for Coalition Logic
Authors: Cláudia Nalon, Lan Zhang, Clare Dixon, Ullrich Hustadt
Date: 2014-04-03 00:00:00

We present a prototype tool for automated reasoning for Coalition Logic, a
non-normal modal logic that can be used for reasoning about cooperative agency.
The theorem prover CLProver is based on recent work on a resolution-based
calculus for Coalition Logic that operates on coalition problems, a normal form
for Coalition Logic. We provide an overview of coalition problems and of the
resolution-based calculus for Coalition Logic. We then give details of the
implementation of CLProver and present the results for a comparison with an
existing tableau-based solver.
Reasoning about Knowledge and Strategies: Epistemic Strategy Logic
Authors: Francesco Belardinelli
Date: 2014-04-03 00:00:00

In this paper we introduce Epistemic Strategy Logic (ESL), an extension of
Strategy Logic with modal operators for individual knowledge. This enhanced
framework allows us to represent explicitly and to reason about the knowledge
agents have of their own and other agents' strategies. We provide a semantics
to ESL in terms of epistemic concurrent game models, and consider the
corresponding model checking problem. We show that the complexity of model
checking ESL is not worse than (non-epistemic) Strategy Logic
Methods Of Measurement The Three-Dimensional Wind Waves Spectra, Based On The Processing Of Video Images Of The Sea Surface
Authors: Boris M. Salin, Mikhail B. Salin
Date: 2013-03-21 00:00:00

Optical instruments for measuring surface-wave characteristics provide a
better spatial and temporal resolution than other methods, but they face
difficulties while converting the results of indirect measurements into
absolute levels of the waves. We have solved this problem to some extent. In
this paper, we propose an optical method for measuring the 3D power spectral
density of the surface waves and spatio-temporal samples of the wave profiles.
The method involves, first, synchronous recording of the brightness field over
a patch of a rough surface and measurement of surface oscillations at one or
more points and, second, filtering of the spatial image spectrum. Filter
parameters are chosen to maximize the correlation of the surface oscillations
recovered and measured at one or two points. In addition to the measurement
procedure, the paper provides experimental results of measuring
multidimensional spectra of roughness, which generally agree with theoretical
expectations and the results of other authors.
GPU Accelerated Fractal Image Compression for Medical Imaging in Parallel Computing Platform
Authors: Md. Enamul Haque, Abdullah Al Kaisan, Mahmudur R Saniat, Aminur Rahman
Date: 2014-04-03 00:00:00
Tasks: GPU, Image Compression

In this paper, we implemented both sequential and parallel version of fractal
image compression algorithms using CUDA (Compute Unified Device Architecture)
programming model for parallelizing the program in Graphics Processing Unit for
medical images, as they are highly similar within the image itself. There are
several improvement in the implementation of the algorithm as well. Fractal
image compression is based on the self similarity of an image, meaning an image
having similarity in majority of the regions. We take this opportunity to
implement the compression algorithm and monitor the effect of it using both
parallel and sequential implementation. Fractal compression has the property of
high compression rate and the dimensionless scheme. Compression scheme for
fractal image is of two kind, one is encoding and another is decoding. Encoding
is very much computational expensive. On the other hand decoding is less
computational. The application of fractal compression to medical images would
allow obtaining much higher compression ratios. While the fractal magnification
an inseparable feature of the fractal compression would be very useful in
presenting the reconstructed image in a highly readable form. However, like all
irreversible methods, the fractal compression is connected with the problem of
information loss, which is especially troublesome in the medical imaging. A
very time consuming encoding pro- cess, which can last even several hours, is
another bothersome drawback of the fractal compression.
An Efficient Search Strategy for Aggregation and Discretization of Attributes of Bayesian Networks Using Minimum Description Length
Authors: Jem Corcoran, Daniel Tran, Nicholas Levine
Date: 2014-04-03 00:00:00
Tasks: Management

Bayesian networks are convenient graphical expressions for high dimensional
probability distributions representing complex relationships between a large
number of random variables. They have been employed extensively in areas such
as bioinformatics, artificial intelligence, diagnosis, and risk management. The
recovery of the structure of a network from data is of prime importance for the
purposes of modeling, analysis, and prediction. Most recovery algorithms in the
literature assume either discrete of continuous but Gaussian data. For general
continuous data, discretization is usually employed but often destroys the very
structure one is out to recover. Friedman and Goldszmidt suggest an approach
based on the minimum description length principle that chooses a discretization
which preserves the information in the original data set, however it is one
which is difficult, if not impossible, to implement for even moderately sized
networks. In this paper we provide an extremely efficient search strategy which
allows one to use the Friedman and Goldszmidt discretization in practice.
Computational Optimization, Modelling and Simulation: Recent Trends and Challenges
Authors: Xin-She Yang, Slawomir Koziel, Leifur Leifsson
Date: 2014-04-02 00:00:00

Modelling, simulation and optimization form an integrated part of modern
design practice in engineering and industry. Tremendous progress has been
observed for all three components over the last few decades. However, many
challenging issues remain unresolved, and the current trends tend to use
nature-inspired algorithms and surrogate-based techniques for modelling and
optimization. This 4th workshop on Computational Optimization, Modelling and
Simulation (COMS 2013) at ICCS 2013 will further summarize the latest
developments of optimization and modelling and their applications in science,
engineering and industry. In this review paper, we will analyse the recent
trends in modelling and optimization, and their associated challenges. We will
discuss important topics for further research, including parameter-tuning,
large-scale problems, and the gaps between theory and applications.
Multi-objective Flower Algorithm for Optimization
Authors: Xin-She Yang, M. Karamanoglu, Xing-Shi He
Date: 2014-04-02 00:00:00

Flower pollination algorithm is a new nature-inspired algorithm, based on the
characteristics of flowering plants. In this paper, we extend this flower
algorithm to solve multi-objective optimization problems in engineering. By
using the weighted sum method with random weights, we show that the proposed
multi-objective flower algorithm can accurately find the Pareto fronts for a
set of test functions. We then solve a bi-objective disc brake design problem,
which indeed converges quickly.
Active Deformable Part Models
Authors: Menglong Zhu, Nikolay Atanasov, George J. Pappas, Kostas Daniilidis
Date: 2014-04-01 00:00:00
Tasks: General Classification, object-detection, Object Detection, Scheduling

This paper presents an active approach for part-based object detection, which
optimizes the order of part filter evaluations and the time at which to stop
and make a prediction. Statistics, describing the part responses, are learned
from training data and are used to formalize the part scheduling problem as an
offline optimization. Dynamic programming is applied to obtain a policy, which
balances the number of part evaluations with the classification accuracy.
During inference, the policy is used as a look-up table to choose the part
order and the stopping time based on the observed filter responses. The method
is faster than cascade detection with deformable part models (which does not
optimize the part order) with negligible loss in accuracy when evaluated on the
PASCAL VOC 2007 and 2010 datasets.
Conceptive Artificial Intelligence: Insights from design theory
Authors: Akin Osman Kazakci
Date: 2014-04-02 00:00:00

The current paper offers a perspective on what we term conceptive
intelligence - the capacity of an agent to continuously think of new object
definitions (tasks, problems, physical systems, etc.) and to look for methods
to realize them. The framework, called a Brouwer machine, is inspired by
previous research in design theory and modeling, with its roots in the
constructivist mathematics of intuitionism. The dual constructivist perspective
we describe offers the possibility to create novelty both in terms of the types
of objects and the methods for constructing objects. More generally, the
theoretical work on which Brouwer machines are based is called imaginative
constructivism. Based on the framework and the theory, we discuss many
paradigms and techniques omnipresent in AI research and their merits and
shortcomings for modeling aspects of design, as described by imaginative
constructivism. To demonstrate and explain the type of creative process
expressed by the notion of a Brouwer machine, we compare this concept with a
system using genetic algorithms for scientific law discovery.
Extraction of Projection Profile, Run-Histogram and Entropy Features Straight from Run-Length Compressed Text-Documents
Authors: Mohammed Javed, P. Nagabhushan, B. B. Chaudhuri
Date: 2014-04-02 00:00:00

Document Image Analysis, like any Digital Image Analysis requires
identification and extraction of proper features, which are generally extracted
from uncompressed images, though in reality images are made available in
compressed form for the reasons such as transmission and storage efficiency.
However, this implies that the compressed image should be decompressed, which
indents additional computing resources. This limitation induces the motivation
to research in extracting features directly from the compressed image. In this
research, we propose to extract essential features such as projection profile,
run-histogram and entropy for text document analysis directly from run-length
compressed text-documents. The experimentation illustrates that features are
extracted directly from the compressed image without going through the stage of
decompression, because of which the computing time is reduced. The feature
values so extracted are exactly identical to those extracted from uncompressed
images.
From ADP to the Brain: Foundations, Roadmap, Challenges and Research Priorities
Authors: Paul J. Werbos
Date: 2014-04-02 00:00:00

This paper defines and discusses Mouse Level Computational Intelligence
(MLCI) as a grand challenge for the coming century. It provides a specific
roadmap to reach that target, citing relevant work and review papers and
discussing the relation to funding priorities in two NSF funding activities:
the ongoing Energy, Power and Adaptive Systems program (EPAS) and the recent
initiative in Cognitive Optimization and Prediction (COPN). It elaborates on
the first step, vector intelligence, a challenge in the development of
universal learning systems, which itself will require considerable new research
to attain. This in turn is a crucial prerequisite to true functional
understanding of how mammal brains achieve such general learning capabilities.
Modeling contaminant intrusion in water distribution networks based on D numbers
Authors: Li Gou, Yong Deng, Rehan Sadiq, Sankaran Mahadevan
Date: 2014-04-02 00:00:00

Efficient modeling on uncertain information plays an important role in
estimating the risk of contaminant intrusion in water distribution networks.
Dempster-Shafer evidence theory is one of the most commonly used methods.
However, the Dempster-Shafer evidence theory has some hypotheses including the
exclusive property of the elements in the frame of discernment, which may not
be consistent with the real world. In this paper, based on a more effective
representation of uncertainty, called D numbers, a new method that allows the
elements in the frame of discernment to be non-exclusive is proposed. To
demonstrate the efficiency of the proposed method, we apply it to the water
distribution networks to estimate the risk of contaminant intrusion.
A Comparative Study of Modern Inference Techniques for Structured Discrete Energy Minimization Problems
Authors: Jörg H. Kappes, Bjoern Andres, Fred A. Hamprecht, Christoph Schnörr, Sebastian Nowozin, Dhruv Batra, Sungwoong Kim, Bernhard X. Kausler, Thorben Kröger, Jan Lellmann, Nikos Komodakis, Bogdan Savchynskyy, Carsten Rother
Date: 2014-04-02 00:00:00

Szeliski et al. published an influential study in 2006 on energy minimization
methods for Markov Random Fields (MRF). This study provided valuable insights
in choosing the best optimization technique for certain classes of problems.
While these insights remain generally useful today, the phenomenal success of
random field models means that the kinds of inference problems that have to be
solved changed significantly. Specifically, the models today often include
higher order interactions, flexible connectivity structures, large
la\-bel-spaces of different cardinalities, or learned energy tables. To reflect
these changes, we provide a modernized and enlarged study. We present an
empirical comparison of 32 state-of-the-art optimization techniques on a corpus
of 2,453 energy minimization instances from diverse applications in computer
vision. To ensure reproducibility, we evaluate all methods in the OpenGM 2
framework and report extensive results regarding runtime and solution quality.
Key insights from our study agree with the results of Szeliski et al. for the
types of models they studied. However, on new and challenging types of models
our findings disagree and suggest that polyhedral methods and integer
programming solvers are competitive in terms of runtime and solution quality
over a large range of model types.
Constrained speaker linking
Authors: David A. van Leeuwen, Niko Brümmer
Date: 2014-03-26 00:00:00
Tasks: Speaker Recognition

In this paper we study speaker linking (a.k.a.\ partitioning) given
constraints of the distribution of speaker identities over speech recordings.
Specifically, we show that the intractable partitioning problem becomes
tractable when the constraints pre-partition the data in smaller cliques with
non-overlapping speakers. The surprisingly common case where speakers in
telephone conversations are known, but the assignment of channels to identities
is unspecified, is treated in a Bayesian way. We show that for the Dutch CGN
database, where this channel assignment task is at hand, a lightweight speaker
recognition system can quite effectively solve the channel assignment problem,
with 93% of the cliques solved. We further show that the posterior distribution
over channel assignment configurations is well calibrated.