pwc-archive/papers-with-abstracts huggingface dataset:
Dispute Resolution Using Argumentation-Based Mediation
Authors: Tomas Trescak, Carles Sierra, Simeon Simoff, Ramon Lopez de Mantaras
Date: 2014-09-15 00:00:00

Mediation is a process, in which both parties agree to resolve their dispute
by negotiating over alternative solutions presented by a mediator. In order to
construct such solutions, mediation brings more information and knowledge, and,
if possible, resources to the negotiation table. The contribution of this paper
is the automated mediation machinery which does that. It presents an
argumentation-based mediation approach that extends the logic-based approach to
argumentation-based negotiation involving BDI agents. The paper describes the
mediation algorithm. For comparison it illustrates the method with a case study
used in an earlier work. It demonstrates how the computational mediator can
deal with realistic situations in which the negotiating agents would otherwise
fail due to lack of knowledge and/or resources.
Crowdsourcing Pareto-Optimal Object Finding by Pairwise Comparisons
Authors: Abolfazl Asudeh, Gensheng Zhang, Naeemul Hassan, Chengkai Li, Gergely V. Zaruba
Date: 2014-09-15 00:00:00
Tasks: Decision Making, Object, Question Selection

This is the first study on crowdsourcing Pareto-optimal object finding, which
has applications in public opinion collection, group decision making, and
information exploration. Departing from prior studies on crowdsourcing skyline
and ranking queries, it considers the case where objects do not have explicit
attributes and preference relations on objects are strict partial orders. The
partial orders are derived by aggregating crowdsourcers' responses to pairwise
comparison questions. The goal is to find all Pareto-optimal objects by the
fewest possible questions. It employs an iterative question-selection
framework. Guided by the principle of eagerly identifying non-Pareto optimal
objects, the framework only chooses candidate questions which must satisfy
three conditions. This design is both sufficient and efficient, as it is proven
to find a short terminal question sequence. The framework is further steered by
two ideas---macro-ordering and micro-ordering. By different micro-ordering
heuristics, the framework is instantiated into several algorithms with varying
power in pruning questions. Experiment results using both real crowdsourcing
marketplace and simulations exhibited not only orders of magnitude reductions
in questions when compared with a brute-force approach, but also
close-to-optimal performance from the most efficient instantiation.
Serialising the ISO SynAF Syntactic Object Model
Authors: Laurent Romary, Amir Zeldes, Florian Zipser
Date: 2011-08-02 00:00:00
Tasks: model, Object

This paper introduces, an XML format developed to serialise the object model
defined by the ISO Syntactic Annotation Framework SynAF. Based on widespread
best practices we adapt a popular XML format for syntactic annotation,
TigerXML, with additional features to support a variety of syntactic phenomena
including constituent and dependency structures, binding, and different node
types such as compounds or empty elements. We also define interfaces to other
formats and standards including the Morpho-syntactic Annotation Framework MAF
and the ISOCat Data Category Registry. Finally a case study of the German
Treebank TueBa-D/Z is presented, showcasing the handling of constituent
structures, topological fields and coreference annotation in tandem.
Active Metric Learning from Relative Comparisons
Authors: Sicheng Xiong, Rómer Rosales, Yuanli Pei, Xiaoli Z. Fern
Date: 2014-09-15 00:00:00
Tasks: Active Learning, Metric Learning, Triplet

This work focuses on active learning of distance metrics from relative
comparison information. A relative comparison specifies, for a data point
triplet $(x_i,x_j,x_k)$, that instance $x_i$ is more similar to $x_j$ than to
$x_k$. Such constraints, when available, have been shown to be useful toward
defining appropriate distance metrics. In real-world applications, acquiring
constraints often require considerable human effort. This motivates us to study
how to select and query the most useful relative comparisons to achieve
effective metric learning with minimum user effort. Given an underlying class
concept that is employed by the user to provide such constraints, we present an
information-theoretic criterion that selects the triplet whose answer leads to
the highest expected gain in information about the classes of a set of
examples. Directly applying the proposed criterion requires examining $O(n^3)$
triplets with $n$ instances, which is prohibitive even for datasets of moderate
size. We show that a randomized selection strategy can be used to reduce the
selection pool from $O(n^3)$ to $O(n)$, allowing us to scale up to larger-size
problems. Experiments show that the proposed method consistently outperforms
two baseline policies.
A feasible roadmap for developing volumetric probability atlas of localized prostate cancer
Authors: Liang Zhao, Jianhua Xuan, Yue Wang
Date: 2014-09-15 00:00:00

A statistical volumetric model, showing the probability map of localized
prostate cancer within the host anatomical structure, has been developed from
90 optically-imaged surgical specimens. This master model permits an accurate
characterization of prostate cancer distribution patterns and an atlas-informed
biopsy sampling strategy. The model is constructed by mapping individual
prostate models onto a site model, together with localized tumors. An accurate
multi-object non-rigid warping scheme is developed based on a mixture of
principal-axis registrations. We report our evaluation and pilot studies on the
effectiveness of the method and its application to optimizing needle biopsy
strategies.
Learning $k$-Modal Distributions via Testing
Authors: Constantinos Daskalakis, Ilias Diakonikolas, Rocco A. Servedio
Date: 2011-07-13 00:00:00
Tasks: Density Estimation

A $k$-modal probability distribution over the discrete domain $\{1,...,n\}$
is one whose histogram has at most $k$ "peaks" and "valleys." Such
distributions are natural generalizations of monotone ($k=0$) and unimodal
($k=1$) probability distributions, which have been intensively studied in
probability theory and statistics.
  In this paper we consider the problem of \emph{learning} (i.e., performing
density estimation of) an unknown $k$-modal distribution with respect to the
$L_1$ distance. The learning algorithm is given access to independent samples
drawn from an unknown $k$-modal distribution $p$, and it must output a
hypothesis distribution $\widehat{p}$ such that with high probability the total
variation distance between $p$ and $\widehat{p}$ is at most $\epsilon.$ Our
main goal is to obtain \emph{computationally efficient} algorithms for this
problem that use (close to) an information-theoretically optimal number of
samples.
  We give an efficient algorithm for this problem that runs in time
$\mathrm{poly}(k,\log(n),1/\epsilon)$. For $k \leq \tilde{O}(\log n)$, the
number of samples used by our algorithm is very close (within an
$\tilde{O}(\log(1/\epsilon))$ factor) to being information-theoretically
optimal. Prior to this work computationally efficient algorithms were known
only for the cases $k=0,1$ \cite{Birge:87b,Birge:97}.
  A novel feature of our approach is that our learning algorithm crucially uses
a new algorithm for \emph{property testing of probability distributions} as a
key subroutine. The learning algorithm uses the property tester to efficiently
decompose the $k$-modal distribution into $k$ (near-)monotone distributions,
which are easier to learn.
Cavlectometry: Towards Holistic Reconstruction of Large Mirror Objects
Authors: Jonathan Balzer, Daniel Acevedo-Feliz, Stefano Soatto, Sebastian Höfer, Markus Hadwiger, Jürgen Beyerer
Date: 2014-09-14 00:00:00
Tasks: Surface Reconstruction

We introduce a method based on the deflectometry principle for the
reconstruction of specular objects exhibiting significant size and geometric
complexity. A key feature of our approach is the deployment of an Automatic
Virtual Environment (CAVE) as pattern generator. To unfold the full power of
this extraordinary experimental setup, an optical encoding scheme is developed
which accounts for the distinctive topology of the CAVE. Furthermore, we devise
an algorithm for detecting the object of interest in raw deflectometric images.
The segmented foreground is used for single-view reconstruction, the background
for estimation of the camera pose, necessary for calibrating the sensor system.
Experiments suggest a significant gain of coverage in single measurements
compared to previous methods. To facilitate research on specular surface
reconstruction, we will make our data set publicly available.
A New Framework for Retinex based Color Image Enhancement using Particle Swarm Optimization
Authors: M. C. Hanumantharaju, M. Ravishankar, D. R. Rameshbabu, V. N Manjunath Aradhya
Date: 2014-09-14 00:00:00
Tasks: Image Enhancement

A new approach for tuning the parameters of MultiScale Retinex (MSR) based
color image enhancement algorithm using a popular optimization method, namely,
Particle Swarm Optimization (PSO) is presented in this paper. The image
enhancement using MSR scheme heavily depends on parameters such as Gaussian
surround space constant, number of scales, gain and offset etc. Selection of
these parameters, empirically and its application to MSR scheme to produce
inevitable results are the major blemishes. The method presented here results
in huge savings of computation time as well as improvement in the visual
quality of an image, since the PSO exploited maximizes the MSR parameters. The
objective of PSO is to validate the visual quality of the enhanced image
iteratively using an effective objective criterion based on entropy and edge
information of an image. The PSO method of parameter optimization of MSR scheme
achieves a very good quality of reconstructed images, far better than that
possible with the other existing methods. Finally, the quality of the enhanced
color images obtained by the proposed method are evaluated using novel metric,
namely, Wavelet Energy (WE). The experimental results presented show that color
images enhanced using the proposed scheme are clearer, more vivid and
efficient.
A new approach in machine learning
Authors: Alain Tapp
Date: 2014-09-14 00:00:00
Tasks: BIG-bench Machine Learning

In this technical report we presented a novel approach to machine learning.
Once the new framework is presented, we will provide a simple and yet very
powerful learning algorithm which will be benchmark on various dataset.
  The framework we proposed is based on booleen circuits; more specifically the
classifier produced by our algorithm have that form. Using bits and boolean
gates instead of real numbers and multiplication enable the the learning
algorithm and classifier to use very efficient boolean vector operations. This
enable both the learning algorithm and classifier to be extremely efficient.
The accuracy of the classifier we obtain with our framework compares very
favorably those produced by conventional techniques, both in terms of
efficiency and accuracy.
Design of Novel Algorithm and Architecture for Gaussian Based Color Image Enhancement System for Real Time Applications
Authors: M. C. Hanumantharaju, M. Ravishankar, D. R. Rameshbabu
Date: 2014-09-14 00:00:00
Tasks: Color Constancy, Image Enhancement

This paper presents the development of a new algorithm for Gaussian based
color image enhancement system. The algorithm has been designed into
architecture suitable for FPGA/ASIC implementation. The color image enhancement
is achieved by first convolving an original image with a Gaussian kernel since
Gaussian distribution is a point spread function which smoothen the image.
Further, logarithm-domain processing and gain/offset corrections are employed
in order to enhance and translate pixels into the display range of 0 to 255.
The proposed algorithm not only provides better dynamic range compression and
color rendition effect but also achieves color constancy in an image. The
design exploits high degrees of pipelining and parallel processing to achieve
real time performance. The design has been realized by RTL compliant Verilog
coding and fits into a single FPGA with a gate count utilization of 321,804.
The proposed method is implemented using Xilinx Virtex-II Pro XC2VP40-7FF1148
FPGA device and is capable of processing high resolution color motion pictures
of sizes of up to 1600x1200 pixels at the real time video rate of 116 frames
per second. This shows that the proposed design would work for not only still
images but also for high resolution video sequences.
EquiNMF: Graph Regularized Multiview Nonnegative Matrix Factorization
Authors: Daniel Hidru, Anna Goldenberg
Date: 2014-09-14 00:00:00
Tasks: Clustering, Data Integration

Nonnegative matrix factorization (NMF) methods have proved to be powerful
across a wide range of real-world clustering applications. Integrating multiple
types of measurements for the same objects/subjects allows us to gain a deeper
understanding of the data and refine the clustering. We have developed a novel
Graph-reguarized multiview NMF-based method for data integration called
EquiNMF. The parameters for our method are set in a completely automated
data-specific unsupervised fashion, a highly desirable property in real-world
applications. We performed extensive and comprehensive experiments on multiview
imaging data. We show that EquiNMF consistently outperforms other single-view
NMF methods used on concatenated data and multi-view NMF methods with different
types of regularizations.
Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation
Authors: Pichao Wang, Wanqing Li, Philip Ogunbona, Zhimin Gao, Hanling Zhang
Date: 2014-09-14 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Temporal Action Localization

Recently, mid-level features have shown promising performance in computer
vision. Mid-level features learned by incorporating class-level information are
potentially more discriminative than traditional low-level local features. In
this paper, an effective method is proposed to extract mid-level features from
Kinect skeletons for 3D human action recognition. Firstly, the orientations of
limbs connected by two skeleton joints are computed and each orientation is
encoded into one of the 27 states indicating the spatial relationship of the
joints. Secondly, limbs are combined into parts and the limb's states are
mapped into part states. Finally, frequent pattern mining is employed to mine
the most frequent and relevant (discriminative, representative and
non-redundant) states of parts in continuous several frames. These parts are
referred to as Frequent Local Parts or FLPs. The FLPs allow us to build
powerful bag-of-FLP-based action representation. This new representation yields
state-of-the-art results on MSR DailyActivity3D and MSR ActionPairs3D.
Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces
Authors: Kevin Swersky, David Duvenaud, Jasper Snoek, Frank Hutter, Michael A. Osborne
Date: 2014-09-14 00:00:00
Tasks: Bayesian Optimization

In practical Bayesian optimization, we must often search over structures with
differing numbers of parameters. For instance, we may wish to search over
neural network architectures with an unknown number of layers. To relate
performance data gathered for different architectures, we define a new kernel
for conditional parameter spaces that explicitly includes information about
which parameters are relevant in a given structure. We show that this kernel
improves model quality and Bayesian optimization results over several simpler
baseline kernels.
Sparse Estimation with Strongly Correlated Variables using Ordered Weighted L1 Regularization
Authors: Mario A. T. Figueiredo, Robert D. Nowak
Date: 2014-09-14 00:00:00
Tasks: Clustering

This paper studies ordered weighted L1 (OWL) norm regularization for sparse
estimation problems with strongly correlated variables. We prove sufficient
conditions for clustering based on the correlation/colinearity of variables
using the OWL norm, of which the so-called OSCAR is a particular case. Our
results extend previous ones for OSCAR in several ways: for the squared error
loss, our conditions hold for the more general OWL norm and under weaker
assumptions; we also establish clustering conditions for the absolute error
loss, which is, as far as we know, a novel result. Furthermore, we characterize
the statistical performance of OWL norm regularization for generative models in
which certain clusters of regression variables are strongly (even perfectly)
correlated, but variables in different clusters are uncorrelated. We show that
if the true p-dimensional signal generating the data involves only s of the
clusters, then O(s log p) samples suffice to accurately estimate the signal,
regardless of the number of coefficients within the clusters. The estimation of
s-sparse signals with completely independent variables requires just as many
measurements. In other words, using the OWL we pay no price (in terms of the
number of measurements) for the presence of strongly correlated variables.
Hybrid Systems Knowledge Representation Using Modelling Environment System Techniques Artificial Intelligence
Authors: Kamran Latif
Date: 2014-09-03 00:00:00

Knowledge-based or Artificial Intelligence techniques are used increasingly
as alternatives to more classical techniques to model ENVIRONMENTAL SYSTEMS.
Use of Artificial Intelligence (AI) in environmental modelling has increased
with recognition of its potential. In this paper we examine the DIFFERENT
TECHNIQUES of Artificial intelligence with profound examples of human
perception, learning and reasoning to solve complex problems. However with the
increase of complexity better methods are required. Keeping in view of the
above some researchers introduced the idea of hybrid mechanism in which two or
more methods can be combined which seems to be a positive effort for creating a
more complex; advanced and intelligent system which has the capability to in-
cooperate human decisions thus driving the landscape changes.
Polarity detection movie reviews in hindi language
Authors: Richa Sharma, Shweta Nigam, Rekha Jain
Date: 2014-09-13 00:00:00
Tasks: Negation, Opinion Mining, Sentiment Analysis

Nowadays peoples are actively involved in giving comments and reviews on
social networking websites and other websites like shopping websites, news
websites etc. large number of people everyday share their opinion on the web,
results is a large number of user data is collected .users also find it trivial
task to read all the reviews and then reached into the decision. It would be
better if these reviews are classified into some category so that the user
finds it easier to read. Opinion Mining or Sentiment Analysis is a natural
language processing task that mines information from various text forms such as
reviews, news, and blogs and classify them on the basis of their polarity as
positive, negative or neutral. But, from the last few years, user content in
Hindi language is also increasing at a rapid rate on the Web. So it is very
important to perform opinion mining in Hindi language as well. In this paper a
Hindi language opinion mining system is proposed. The system classifies the
reviews as positive, negative and neutral for Hindi language. Negation is also
handled in the proposed system. Experimental results using reviews of movies
show the effectiveness of the system
A study on effectiveness of extreme learning machine
Authors: Yuguang Wang, Feilong Cao, Yubo Yuan
Date: 2014-09-13 00:00:00

Extreme learning machine (ELM), proposed by Huang et al., has been shown a
promising learning algorithm for single-hidden layer feedforward neural
networks (SLFNs). Nevertheless, because of the random choice of input weights
and biases, the ELM algorithm sometimes makes the hidden layer output matrix H
of SLFN not full column rank, which lowers the effectiveness of ELM. This paper
discusses the effectiveness of ELM and proposes an improved algorithm called
EELM that makes a proper selection of the input weights and bias before
calculating the output weights, which ensures the full column rank of H in
theory. This improves to some extend the learning rate (testing accuracy,
prediction accuracy, learning time) and the robustness property of the
networks. The experimental results based on both the benchmark function
approximation and real-world problems including classification and regression
applications show the good performances of EELM.
Concurrent Tracking of Inliers and Outliers
Authors: Jae-Yeong Lee, Wonpil Yu
Date: 2014-09-13 00:00:00
Tasks: Motion Estimation, Object Tracking

In object tracking, outlier is one of primary factors which degrade
performance of image-based tracking algorithms. In this respect, therefore,
most of the existing methods simply discard detected outliers and pay little or
no attention to employing them as an important source of information for motion
estimation. We consider outliers as important as inliers for object tracking
and propose a motion estimation algorithm based on concurrent tracking of
inliers and outliers. Our tracker makes use of pyramidal implementation of the
Lucas-Kanade tracker to estimate motion flows of inliers and outliers and final
target motion is estimated robustly based on both of these information.
Experimental results from challenging benchmark video sequences confirm
enhanced tracking performance, showing highly stable target tracking under
severe occlusion compared with state-of-the-art algorithms. The proposed
algorithm runs at more than 100 frames per second even without using a hardware
accelerator, which makes the proposed method more practical and portable.
Parallel Distributed Block Coordinate Descent Methods based on Pairwise Comparison Oracle
Authors: Kota Matsui, Wataru Kumagai, Takafumi Kanamori
Date: 2014-09-13 00:00:00

This paper provides a block coordinate descent algorithm to solve
unconstrained optimization problems. In our algorithm, computation of function
values or gradients is not required. Instead, pairwise comparison of function
values is used. Our algorithm consists of two steps; one is the direction
estimate step and the other is the search step. Both steps require only
pairwise comparison of function values, which tells us only the order of
function values over two points. In the direction estimate step, a Newton type
search direction is estimated. A computation method like block coordinate
descent methods is used with the pairwise comparison. In the search step, a
numerical solution is updated along the estimated direction. The computation in
the direction estimate step can be easily parallelized, and thus, the algorithm
works efficiently to find the minimizer of the objective function. Also, we
show an upper bound of the convergence rate. In numerical experiments, we show
that our method efficiently finds the optimal solution compared to some
existing methods based on the pairwise comparison.
Approximating rational Bezier curves by constrained Bezier curves of arbitrary degree
Authors: Mao Shi, Jiansong Deng
Date: 2012-12-14 00:00:00

In this paper, we propose a method to obtain a constrained approximation of a
rational B\'{e}zier curve by a polynomial B\'{e}zier curve. This problem is
reformulated as an approximation problem between two polynomial B\'{e}zier
curves based on weighted least-squares method, where weight functions
$\rho(t)=\omega(t)$ and $\rho(t)=\omega(t)^{2}$ are studied respectively. The
efficiency of the proposed method is tested using some examples.