pwc-archive/papers-with-abstracts huggingface dataset:
Exploiting Data Parallelism in the yConvex Hypergraph Algorithm for Image Representation using GPGPUs
Authors: Saurabh Jha, Tejaswi Agarwal, B. Rajesh Kanna
Date: 2013-06-23 00:00:00

To define and identify a region-of-interest (ROI) in a digital image, the
shape descriptor of the ROI has to be described in terms of its boundary
characteristics. To address the generic issues of contour tracking, the yConvex
Hypergraph (yCHG) model was proposed by Kanna et al [1]. In this work, we
propose a parallel approach to implement the yCHG model by exploiting massively
parallel cores of NVIDIA's Compute Unified Device Architecture (CUDA). We
perform our experiments on the MODIS satellite image database by NASA, and
based on our analysis we observe that the performance of the serial
implementation is better on smaller images, but once the threshold is achieved
in terms of image resolution, the parallel implementation outperforms its
sequential counterpart by 2 to 10 times (2x-10x). We also conclude that an
increase in the number of hyperedges in the ROI of a given size does not impact
the performance of the overall algorithm.
Characterizing Ambiguity in Light Source Invariant Shape from Shading
Authors: Benjamin Kunsberg, Steven W. Zucker
Date: 2013-06-23 00:00:00

Shape from shading is a classical inverse problem in computer vision. This
shape reconstruction problem is inherently ill-defined; it depends on the
assumed light source direction. We introduce a novel mathematical formulation
for calculating local surface shape based on covariant derivatives of the
shading flow field, rather than the customary integral minimization or P.D.E
approaches. On smooth surfaces, we show second derivatives of brightness are
independent of the light sources and can be directly related to surface
properties. We use these measurements to define the matching local family of
surfaces that can result from any given shading patch, changing the emphasis to
characterizing ambiguity in the problem. We give an example of how these local
surface ambiguities collapse along certain image contours and how this can be
used for the reconstruction problem.
Loss-Proportional Subsampling for Subsequent ERM
Authors: Paul Mineiro, Nikos Karampatziakis
Date: 2013-06-07 00:00:00

We propose a sampling scheme suitable for reducing a data set prior to
selecting a hypothesis with minimum empirical risk. The sampling only considers
a subset of the ultimate (unknown) hypothesis set, but can nonetheless
guarantee that the final excess risk will compare favorably with utilizing the
entire original data set. We demonstrate the practical benefits of our approach
on a large dataset which we subsample and subsequently fit with boosted trees.
A Statistical Perspective on Algorithmic Leveraging
Authors: Ping Ma, Michael W. Mahoney, Bin Yu
Date: 2013-06-23 00:00:00
Tasks: Computational Efficiency

One popular method for dealing with large-scale data sets is sampling. For
example, by using the empirical statistical leverage scores as an importance
sampling distribution, the method of algorithmic leveraging samples and
rescales rows/columns of data matrices to reduce the data size before
performing computations on the subproblem. This method has been successful in
improving computational efficiency of algorithms for matrix problems such as
least-squares approximation, least absolute deviations approximation, and
low-rank matrix approximation. Existing work has focused on algorithmic issues
such as worst-case running times and numerical issues associated with providing
high-quality implementations, but none of it addresses statistical aspects of
this method.
  In this paper, we provide a simple yet effective framework to evaluate the
statistical properties of algorithmic leveraging in the context of estimating
parameters in a linear regression model with a fixed number of predictors. We
show that from the statistical perspective of bias and variance, neither
leverage-based sampling nor uniform sampling dominates the other. This result
is particularly striking, given the well-known result that, from the
algorithmic perspective of worst-case analysis, leverage-based sampling
provides uniformly superior worst-case algorithmic results, when compared with
uniform sampling. Based on these theoretical results, we propose and analyze
two new leveraging algorithms. A detailed empirical evaluation of existing
leverage-based methods as well as these two new methods is carried out on both
synthetic and real data sets. The empirical results indicate that our theory is
a good predictor of practical performance of existing and new leverage-based
algorithms and that the new algorithms achieve improved performance.
Song-based Classification techniques for Endangered Bird Conservation
Authors: Erick Stattner, Wilfried Segretier, Martine Collard, Philippe Hunel, Nicolas Vidot
Date: 2013-06-22 00:00:00
Tasks: Classification, Clustering, General Classification

The work presented in this paper is part of a global framework which long
term goal is to design a wireless sensor network able to support the
observation of a population of endangered birds. We present the first stage for
which we have conducted a knowledge discovery approach on a sample of
acoustical data. We use MFCC features extracted from bird songs and we exploit
two knowledge discovery techniques. One that relies on clustering-based
approaches, that highlights the homogeneity in the songs of the species. The
other, based on predictive modeling, that demonstrates the good performances of
various machine learning techniques for the identification process. The
knowledge elicited provides promising results to consider a widespread study
and to elicit guidelines for designing a first version of the automatic
approach for data collection based on acoustic sensors.
Online dictionary learning for kernel LMS. Analysis and forward-backward splitting algorithm
Authors: Wei Gao, Jie Chen, Cédric Richard, Jianguo Huang
Date: 2013-06-22 00:00:00
Tasks: Dictionary Learning

Adaptive filtering algorithms operating in reproducing kernel Hilbert spaces
have demonstrated superiority over their linear counterpart for nonlinear
system identification. Unfortunately, an undesirable characteristic of these
methods is that the order of the filters grows linearly with the number of
input data. This dramatically increases the computational burden and memory
requirement. A variety of strategies based on dictionary learning have been
proposed to overcome this severe drawback. Few, if any, of these works analyze
the problem of updating the dictionary in a time-varying environment. In this
paper, we present an analytical study of the convergence behavior of the
Gaussian least-mean-square algorithm in the case where the statistics of the
dictionary elements only partially match the statistics of the input data. This
allows us to emphasize the need for updating the dictionary in an online way,
by discarding the obsolete elements and adding appropriate ones. We introduce a
kernel least-mean-square algorithm with L1-norm regularization to automatically
perform this task. The stability in the mean of this method is analyzed, and
its performance is tested with experiments.
Cognitive Interpretation of Everyday Activities: Toward Perceptual Narrative Based Visuo-Spatial Scene Interpretation
Authors: Mehul Bhatt, Jakob Suchan, Carl Schultz
Date: 2013-06-22 00:00:00
Tasks: Position, Scene Understanding

We position a narrative-centred computational model for high-level knowledge
representation and reasoning in the context of a range of assistive
technologies concerned with "visuo-spatial perception and cognition" tasks. Our
proposed narrative model encompasses aspects such as \emph{space, events,
actions, change, and interaction} from the viewpoint of commonsense reasoning
and learning in large-scale cognitive systems. The broad focus of this paper is
on the domain of "human-activity interpretation" in smart environments, ambient
intelligence etc. In the backdrop of a "smart meeting cinematography" domain,
we position the proposed narrative model, preliminary work on perceptual
narrativisation, and the immediate outlook on constructing general-purpose
open-source tools for perceptual narrativisation.
  ACM Classification: I.2 Artificial Intelligence: I.2.0 General -- Cognitive
Simulation, I.2.4 Knowledge Representation Formalisms and Methods, I.2.10
Vision and Scene Understanding: Architecture and control structures, Motion,
Perceptual reasoning, Shape, Video analysis
  General keywords: cognitive systems; human-computer interaction; spatial
cognition and computation; commonsense reasoning; spatial and temporal
reasoning; assistive technologies
Segmenting DNA sequence into `words'
Authors: Wang Liang
Date: 2012-02-12 00:00:00
Tasks: Language Modeling, Language Modelling

This paper presents a novel method to segment/decode DNA sequences based on
n-grams statistical language model. Firstly, we find the length of most DNA
'words' is 12 to 15 bps by analyzing the genomes of 12 model species. Then we
design an unsupervised probability based approach to segment the DNA sequences.
The benchmark of segmenting method is also proposed.
New Approach of Estimating PSNR-B For De-blocked Images
Authors: S. Aruna Mastani, K. Shilpa
Date: 2013-06-22 00:00:00
Tasks: Blocking, SSIM

Measurement of image quality is very crucial to many image processing
applications. Quality metrics are used to measure the quality of improvement in
the images after they are processed and compared with the original images.
Compression is one of the applications where it is required to monitor the
quality of decompressed or decoded image. JPEG compression is the lossy
compression which is most prevalent technique for image codecs. But it suffers
from blocking artifacts. Various deblocking filters are used to reduce blocking
artifacts. The efficiency of deblocking filters which improves visual signals
degraded by blocking artifacts from compression will also be studied. Objective
quality metrics like PSNR, SSIM, and PSNRB for analyzing the quality of
deblocked images will be studied. We introduce a new approach of PSNR-B for
analyzing quality of deblocked images. Simulation results show that new
approach of PSNR-B called modified PSNR-B. it gives even better results
compared to existing well known blockiness specific indices
Discriminative Training: Learning to Describe Video with Sentences, from Video Described with Sentences
Authors: Haonan Yu, Jeffrey Mark Siskind
Date: 2013-06-21 00:00:00
Tasks: Sentence

We present a method for learning word meanings from complex and realistic
video clips by discriminatively training (DT) positive sentential labels
against negative ones, and then use the trained word models to generate
sentential descriptions for new video. This new work is inspired by recent work
which adopts a maximum likelihood (ML) framework to address the same problem
using only positive sentential labels. The new method, like the ML-based one,
is able to automatically determine which words in the sentence correspond to
which concepts in the video (i.e., ground words to meanings) in a weakly
supervised fashion. While both DT and ML yield comparable results with
sufficient training data, DT outperforms ML significantly with smaller training
sets because it can exploit negative training labels to better constrain the
learning problem.
Near-optimal Coresets For Least-Squares Regression
Authors: Christos Boutsidis, Petros Drineas, Malik Magdon-Ismail
Date: 2012-02-16 00:00:00
Tasks: regression

We study (constrained) least-squares regression as well as multiple response
least-squares regression and ask the question of whether a subset of the data,
a coreset, suffices to compute a good approximate solution to the regression.
We give deterministic, low order polynomial-time algorithms to construct such
coresets with approximation guarantees, together with lower bounds indicating
that there is not much room for improvement upon our results.
Deterministic Feature Selection for $k$-means Clustering
Authors: Christos Boutsidis, Malik Magdon-Ismail
Date: 2011-09-26 00:00:00
Tasks: Clustering, feature selection

We study feature selection for $k$-means clustering. Although the literature
contains many methods with good empirical performance, algorithms with provable
theoretical behavior have only recently been developed. Unfortunately, these
algorithms are randomized and fail with, say, a constant probability. We
address this issue by presenting a deterministic feature selection algorithm
for k-means with theoretical guarantees. At the heart of our algorithm lies a
deterministic method for decompositions of the identity.
Epistemology of Modeling and Simulation: How can we gain Knowledge from Simulations?
Authors: Andreas Tolk, Saikou Y. Diallo, Jose J. Padilla, Ross Gore
Date: 2013-06-21 00:00:00
Tasks: Philosophy, Two-sample testing

Epistemology is the branch of philosophy that deals with gaining knowledge. It is closely related to ontology. The branch that deals with questions like "What is real?" and "What do we know?" as it provides these components. When using modeling and simulation, we usually imply that we are doing so to either apply knowledge, in particular when we are using them for training and teaching, or that we want to gain new knowledge, for example when doing analysis or conducting virtual experiments. This paper looks at the history of science to give a context to better cope with the question, how we can gain knowledge from simulation. It addresses aspects of computability and the general underlying mathematics, and applies the findings to validation and verification and development of federations. As simulations are understood as computable executable hypotheses, validation can be understood as hypothesis testing and theory building. The mathematical framework allows furthermore addressing some challenges when developing federations and the potential introduction of contradictions when composing different theories, as they are represented by the federated simulation systems.
The Arcade Learning Environment: An Evaluation Platform for General Agents
Authors: Marc G. Bellemare, Yavar Naddaf, Joel Veness, Michael Bowling
Date: 2012-07-19 00:00:00
Tasks: Atari Games, Benchmarking, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

In this article we introduce the Arcade Learning Environment (ALE): both a
challenge problem and a platform and methodology for evaluating the development
of general, domain-independent AI technology. ALE provides an interface to
hundreds of Atari 2600 game environments, each one different, interesting, and
designed to be a challenge for human players. ALE presents significant research
challenges for reinforcement learning, model learning, model-based planning,
imitation learning, transfer learning, and intrinsic motivation. Most
importantly, it provides a rigorous testbed for evaluating and comparing
approaches to these problems. We illustrate the promise of ALE by developing
and benchmarking domain-independent agents designed using well-established AI
techniques for both reinforcement learning and planning. In doing so, we also
propose an evaluation methodology made possible by ALE, reporting empirical
results on over 55 different games. All of the software, including the
benchmark agents, is publicly available.
Clinical Relationships Extraction Techniques from Patient Narratives
Authors: Wafaa Tawfik Abdel-moneim, Mohamed Hashem Abdel-Aziz, Mohamed Monier Hassan
Date: 2013-06-21 00:00:00
Tasks: BIG-bench Machine Learning, Relationship Extraction (Distant Supervised)

The Clinical E-Science Framework (CLEF) project was used to extract important
information from medical texts by building a system for the purpose of clinical
research, evidence-based healthcare and genotype-meets-phenotype informatics.
The system is divided into two parts, one part concerns with the identification
of relationships between clinically important entities in the text. The full
parses and domain-specific grammars had been used to apply many approaches to
extract the relationship. In the second part of the system, statistical machine
learning (ML) approaches are applied to extract relationship. A corpus of
oncology narratives that hand annotated with clinical relationships can be used
to train and test a system that has been designed and implemented by supervised
machine learning (ML) approaches. Many features can be extracted from these
texts that are used to build a model by the classifier. Multiple supervised
machine learning algorithms can be applied for relationship extraction. Effects
of adding the features, changing the size of the corpus, and changing the type
of the algorithm on relationship extraction are examined. Keywords: Text
mining; information extraction; NLP; entities; and relations.
Fine-Grained Visual Classification of Aircraft
Authors: Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, Andrea Vedaldi
Date: 2013-06-21 00:00:00
Tasks: Classification, Fine-Grained Image Classification, General Classification

This paper introduces FGVC-Aircraft, a new dataset containing 10,000 images
of aircraft spanning 100 aircraft models, organised in a three-level hierarchy.
At the finer level, differences between models are often subtle but always
visually measurable, making visual recognition challenging but possible. A
benchmark is obtained by defining corresponding classification tasks and
evaluation protocols, and baseline results are presented. The construction of
this dataset was made possible by the work of aircraft enthusiasts, a strategy
that can extend to the study of number of other object classes. Compared to the
domains usually considered in fine-grained visual classification (FGVC), for
example animals, aircraft are rigid and hence less deformable. They, however,
present other interesting modes of variation, including purpose, size,
designation, structure, historical style, and branding.
Computer Aided ECG Analysis - State of the Art and Upcoming Challenges
Authors: Marko Velic, Ivan Padavic, Sinisa Car
Date: 2013-06-21 00:00:00
Tasks: Medical Diagnosis, Rhythm

In this paper we present current achievements in computer aided ECG analysis
and their applicability in real world medical diagnosis process. Most of the
current work is covering problems of removing noise, detecting heartbeats and
rhythm-based analysis. There are some advancements in particular ECG segments
detection and beat classifications but with limited evaluations and without
clinical approvals. This paper presents state of the art advancements in those
areas till present day. Besides this short computer science and signal
processing literature review, paper covers future challenges regarding the ECG
signal morphology analysis deriving from the medical literature review. Paper
is concluded with identified gaps in current advancements and testing, upcoming
challenges for future research and a bullseye test is suggested for morphology
analysis evaluation.
3-SAT Problem A New Memetic-PSO Algorithm
Authors: Nasser Lotfi, Jamshid Tamouk, Mina Farmanbar
Date: 2013-06-21 00:00:00

3-SAT problem is of great importance to many technical and scientific
applications. This paper presents a new hybrid evolutionary algorithm for
solving this satisfiability problem. 3-SAT problem has the huge search space
and hence it is known as a NP-hard problem. So, deterministic approaches are
not applicable in this context. Thereof, application of evolutionary processing
approaches and especially PSO will be very effective for solving these kinds of
problems. In this paper, we introduce a new evolutionary optimization technique
based on PSO, Memetic algorithm and local search approaches. When some
heuristics are mixed, their advantages are collected as well and we can reach
to the better outcomes. Finally, we test our proposed algorithm over some
benchmarks used by some another available algorithms. Obtained results show
that our new method leads to the suitable results by the appropriate time.
Thereby, it achieves a better result in compared with the existent approaches
such as pure genetic algorithm and some verified types
Locally adaptive factor processes for multivariate time series
Authors: Daniele Durante, Bruno Scarpa, David B. Dunson
Date: 2012-10-07 00:00:00
Tasks: Bayesian Inference, Gaussian Processes, Time Series, Time Series Analysis

In modeling multivariate time series, it is important to allow time-varying
smoothness in the mean and covariance process. In particular, there may be
certain time intervals exhibiting rapid changes and others in which changes are
slow. If such time-varying smoothness is not accounted for, one can obtain
misleading inferences and predictions, with over-smoothing across erratic time
intervals and under-smoothing across times exhibiting slow variation. This can
lead to mis-calibration of predictive intervals, which can be substantially too
narrow or wide depending on the time. We propose a locally adaptive factor
process for characterizing multivariate mean-covariance changes in continuous
time, allowing locally varying smoothness in both the mean and covariance
matrix. This process is constructed utilizing latent dictionary functions
evolving in time through nested Gaussian processes and linearly related to the
observed data with a sparse mapping. Using a differential equation
representation, we bypass usual computational bottlenecks in obtaining MCMC and
online algorithms for approximate Bayesian inference. The performance is
assessed in simulations and illustrated in a financial application.
Breaking Symmetry with Different Orderings
Authors: Nina Narodytska, Toby Walsh
Date: 2013-06-21 00:00:00

We can break symmetry by eliminating solutions within each symmetry class.
For instance, the Lex-Leader method eliminates all but the smallest solution in
the lexicographical ordering. Unfortunately, the Lex-Leader method is
intractable in general. We prove that, under modest assumptions, we cannot
reduce the worst case complexity of breaking symmetry by using other orderings
on solutions. We also prove that a common type of symmetry, where rows and
columns in a matrix of decision variables are interchangeable, is intractable
to break when we use two promising alternatives to the lexicographical
ordering: the Gray code ordering (which uses a different ordering on
solutions), and the Snake-Lex ordering (which is a variant of the
lexicographical ordering that re-orders the variables). Nevertheless, we show
experimentally that using other orderings like the Gray code to break symmetry
can be beneficial in practice as they may better align with the objective
function and branching heuristic.
Safeguarding E-Commerce against Advisor Cheating Behaviors: Towards More Robust Trust Models for Handling Unfair Ratings
Authors: Lizi Zhang
Date: 2013-06-20 00:00:00

In electronic marketplaces, after each transaction buyers will rate the
products provided by the sellers. To decide the most trustworthy sellers to
transact with, buyers rely on trust models to leverage these ratings to
evaluate the reputation of sellers. Although the high effectiveness of
different trust models for handling unfair ratings have been claimed by their
designers, recently it is argued that these models are vulnerable to more
intelligent attacks, and there is an urgent demand that the robustness of the
existing trust models has to be evaluated in a more comprehensive way. In this
work, we classify the existing trust models into two broad categories and
propose an extendable e-marketplace testbed to evaluate their robustness
against different unfair rating attacks comprehensively. On top of highlighting
the robustness of the existing trust models for handling unfair ratings is far
from what they were claimed to be, we further propose and validate a novel
combination mechanism for the existing trust models, Discount-then-Filter, to
notably enhance their robustness against the investigated attacks.