pwc-archive/papers-with-abstracts huggingface dataset:
An Alternate Approach for Designing a Domain Specific Image Search Prototype Using Histogram
Authors: Sukanta Sinha, Rana Dattagupta, Debajyoti Mukhopadhyay
Date: 2013-11-28 00:00:00
Tasks: Image Retrieval

Everyone knows that thousand of words are represented by a single image. As a
result image search has become a very popular mechanism for the Web searchers.
Image search means, the search results are produced by the search engine should
be a set of images along with their Web page Unified Resource Locator. Now Web
searcher can perform two types of image search, they are Text to Image and
Image to Image search. In Text to Image search, search query should be a text.
Based on the input text data system will generate a set of images along with
their Web page URL as an output. On the other hand, in Image to Image search,
search query should be an image and based on this image system will generate a
set of images along with their Web page URL as an output. According to the
current scenarios, Text to Image search mechanism always not returns perfect
result. It matches the text data and then displays the corresponding images as
an output, which is not always perfect. To resolve this problem, Web
researchers have introduced the Image to Image search mechanism. In this paper,
we have also proposed an alternate approach of Image to Image search mechanism
using Histogram.
Learning Semantic Representations for the Phrase Translation Model
Authors: Jianfeng Gao, Xiaodong He, Wen-tau Yih, Li Deng
Date: 2013-11-28 00:00:00
Tasks: Learning Semantic Representations, Machine Translation, Translation

This paper presents a novel semantic-based phrase translation model. A pair
of source and target phrases are projected into continuous-valued vector
representations in a low-dimensional latent semantic space, where their
translation score is computed by the distance between the pair in this new
space. The projection is performed by a multi-layer neural network whose
weights are learned on parallel training data. The learning is aimed to
directly optimize the quality of end-to-end machine translation results.
Experimental evaluation has been performed on two Europarl translation tasks,
English-French and German-English. The results show that the new semantic-based
phrase translation model significantly improves the performance of a
state-of-the-art phrase-based statistical machine translation sys-tem, leading
to a gain of 0.7-1.0 BLEU points.
ADMM Algorithm for Graphical Lasso with an $\ell_{\infty}$ Element-wise Norm Constraint
Authors: Karthik Mohan
Date: 2013-11-28 00:00:00

We consider the problem of Graphical lasso with an additional $\ell_{\infty}$
element-wise norm constraint on the precision matrix. This problem has
applications in high-dimensional covariance decomposition such as in
\citep{Janzamin-12}. We propose an ADMM algorithm to solve this problem. We
also use a continuation strategy on the penalty parameter to have a fast
implemenation of the algorithm.
Real-time High Resolution Fusion of Depth Maps on GPU
Authors: Dmitry Trifonov
Date: 2013-11-28 00:00:00
Tasks: GPU, Pose Estimation, Surface Reconstruction, Vocal Bursts Intensity Prediction

A system for live high quality surface reconstruction using a single moving
depth camera on a commodity hardware is presented. High accuracy and real-time
frame rate is achieved by utilizing graphics hardware computing capabilities
via OpenCL and by using sparse data structure for volumetric surface
representation. Depth sensor pose is estimated by combining serial texture
registration algorithm with iterative closest points algorithm (ICP) aligning
obtained depth map to the estimated scene model. Aligned surface is then fused
into the scene. Kalman filter is used to improve fusion quality. Truncated
signed distance function (TSDF) stored as block-based sparse buffer is used to
represent surface. Use of sparse data structure greatly increases accuracy of
scanned surfaces and maximum scanning area. Traditional GPU implementation of
volumetric rendering and fusion algorithms were modified to exploit sparsity to
achieve desired performance. Incorporation of texture registration for sensor
pose estimation and Kalman filter for measurement integration improved accuracy
and robustness of scanning process.
A Novel Illumination-Invariant Loss for Monocular 3D Pose Estimation
Authors: Srimal Jayawardena, Marcus Hutter, Nathan Brewer
Date: 2013-11-28 00:00:00
Tasks: 3D Pose Estimation, Object, Pose Estimation

The problem of identifying the 3D pose of a known object from a given 2D
image has important applications in Computer Vision. Our proposed method of
registering a 3D model of a known object on a given 2D photo of the object has
numerous advantages over existing methods. It does not require prior training,
knowledge of the camera parameters, explicit point correspondences or matching
features between the image and model. Unlike techniques that estimate a partial
3D pose (as in an overhead view of traffic or machine parts on a conveyor
belt), our method estimates the complete 3D pose of the object. It works on a
single static image from a given view under varying and unknown lighting
conditions. For this purpose we derive a novel illumination-invariant distance
measure between the 2D photo and projected 3D model, which is then minimised to
find the best pose parameters. Results for vehicle pose detection in real
photographs are presented.
Using Multiple Samples to Learn Mixture Models
Authors: Jason D. Lee, Ran Gilad-Bachrach, Rich Caruana
Date: 2013-11-28 00:00:00
Proceeding: NeurIPS 2013 12

In the mixture models problem it is assumed that there are $K$ distributions
$\theta_{1},\ldots,\theta_{K}$ and one gets to observe a sample from a mixture
of these distributions with unknown coefficients. The goal is to associate
instances with their generating distributions, or to identify the parameters of
the hidden distributions. In this work we make the assumption that we have
access to several samples drawn from the same $K$ underlying distributions, but
with different mixing weights. As with topic modeling, having multiple samples
is often a reasonable assumption. Instead of pooling the data into one sample,
we prove that it is possible to use the differences between the samples to
better recover the underlying structure. We present algorithms that recover the
underlying structure under milder assumptions than the current state of art
when either the dimensionality or the separation is high. The methods, when
applied to topic modeling, allow generalization to words not present in the
training data.
Cross-Domain Sparse Coding
Authors: Jim Jing-Yan Wang
Date: 2013-11-27 00:00:00
Tasks: image-classification, Image Classification, Spam detection

Sparse coding has shown its power as an effective data representation method.
However, up to now, all the sparse coding approaches are limited within the
single domain learning problem. In this paper, we extend the sparse coding to
cross domain learning problem, which tries to learn from a source domain to a
target domain with significant different distribution. We impose the Maximum
Mean Discrepancy (MMD) criterion to reduce the cross-domain distribution
difference of sparse codes, and also regularize the sparse codes by the class
labels of the samples from both domains to increase the discriminative ability.
The encouraging experiment results of the proposed cross-domain sparse coding
algorithm on two challenging tasks --- image classification of photograph and
oil painting domains, and multiple user spam detection --- show the advantage
of the proposed method over other cross-domain data representation methods.
Introduction to Neutrosophic Measure, Neutrosophic Integral, and Neutrosophic Probability
Authors: Florentin Smarandache
Date: 2013-11-27 00:00:00

In this paper, we introduce for the first time the notions of neutrosophic
measure and neutrosophic integral, and we develop the 1995 notion of
neutrosophic probability. We present many practical examples. It is possible to
define the neutrosophic measure and consequently the neutrosophic integral and
neutrosophic probability in many ways, because there are various types of
indeterminacies, depending on the problem we need to solve. Neutrosophics study
the indeterminacy. Indeterminacy is different from randomness. It can be caused
by physical space materials and type of construction, by items involved in the
space, etc.
Image forgery detection based on the fusion of machine learning and block-matching methods
Authors: Davide Cozzolino, Diego Gragnaniello, Luisa Verdoliva
Date: 2013-11-27 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Image Forgery Detection, Steganalysis

Dense local descriptors and machine learning have been used with success in
several applications, like classification of textures, steganalysis, and
forgery detection. We develop a new image forgery detector building upon some
descriptors recently proposed in the steganalysis field suitably merging some
of such descriptors, and optimizing a SVM classifier on the available training
set. Despite the very good performance, very small forgeries are hardly ever
detected because they contribute very little to the descriptors. Therefore we
also develop a simple, but extremely specific, copy-move detector based on
region matching and fuse decisions so as to reduce the missing detection rate.
Overall results appear to be extremely encouraging.
A novel framework for image forgery localization
Authors: Davide Cozzolino, Diego Gragnaniello, Luisa Verdoliva
Date: 2013-11-27 00:00:00
Tasks: BIG-bench Machine Learning, Patch Matching

Image forgery localization is a very active and open research field for the
difficulty to handle the large variety of manipulations a malicious user can
perform by means of more and more sophisticated image editing tools. Here, we
propose a localization framework based on the fusion of three very different
tools, based, respectively, on sensor noise, patch-matching, and machine
learning. The binary masks provided by these tools are finally fused based on
some suitable reliability indexes. According to preliminary experiments on the
training set, the proposed framework provides often a very good localization
accuracy and sometimes valuable clues for visual scrutiny.
A Constraint Programming Approach for Mining Sequential Patterns in a Sequence Database
Authors: Jean-Philippe Métivier, Samir Loudni, Thierry Charnois
Date: 2013-11-27 00:00:00
Tasks: Sequential Pattern Mining

Constraint-based pattern discovery is at the core of numerous data mining
tasks. Patterns are extracted with respect to a given set of constraints
(frequency, closedness, size, etc). In the context of sequential pattern
mining, a large number of devoted techniques have been developed for solving
particular classes of constraints. The aim of this paper is to investigate the
use of Constraint Programming (CP) to model and mine sequential patterns in a
sequence database. Our CP approach offers a natural way to simultaneously
combine in a same framework a large set of constraints coming from various
origins. Experiments show the feasibility and the interest of our approach.
On Approximate Inference for Generalized Gaussian Process Models
Authors: Lifeng Shang, Antoni B. Chan
Date: 2013-11-25 00:00:00
Tasks: Form

A generalized Gaussian process model (GGPM) is a unifying framework that
encompasses many existing Gaussian process (GP) models, such as GP regression,
classification, and counting. In the GGPM framework, the observation likelihood
of the GP model is itself parameterized using the exponential family
distribution (EFD). In this paper, we consider efficient algorithms for
approximate inference on GGPMs using the general form of the EFD. A particular
GP model and its associated inference algorithms can then be formed by changing
the parameters of the EFD, thus greatly simplifying its creation for
task-specific output domains. We demonstrate the efficacy of this framework by
creating several new GP models for regressing to non-negative reals and to real
intervals. We also consider a closed-form Taylor approximation for efficient
inference on GGPMs, and elaborate on its connections with other model-specific
heuristic closed-form approximations. Finally, we present a comprehensive set
of experiments to compare approximate inference algorithms on a wide variety of
GGPMs.
Color and Shape Content Based Image Classification using RBF Network and PSO Technique: A Survey
Authors: Abhishek Pandey, Anjna Jayant Deen, Rajeev Pandey
Date: 2013-11-27 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Retrieval

The improvement of the accuracy of image query retrieval used image
classification technique. Image classification is well known technique of
supervised learning. The improved method of image classification increases the
working efficiency of image query retrieval. For the improvements of
classification technique we used RBF neural network function for better
prediction of feature used in image retrieval.Colour content is represented by
pixel values in image classification using radial base function(RBF) technique.
This approach provides better result compare to SVM technique in image
representation.Image is represented by matrix though RBF using pixel values of
colour intensity of image. Firstly we using RGB colour model. In this colour
model we use red, green and blue colour intensity values in matrix.SVM with
partical swarm optimization for image classification is implemented in content
of images which provide better Results based on the proposed approach are found
encouraging in terms of color image classification accuracy.
Want a Good Answer? Ask a Good Question First!
Authors: Yuan Yao, Hanghang Tong, Tao Xie, Leman Akoglu, Feng Xu, Jian Lu
Date: 2013-11-27 00:00:00
Tasks: Community Question Answering, Question Answering

Community Question Answering (CQA) websites have become valuable repositories
which host a massive volume of human knowledge. To maximize the utility of such
knowledge, it is essential to evaluate the quality of an existing question or
answer, especially soon after it is posted on the CQA website.
  In this paper, we study the problem of inferring the quality of questions and
answers through a case study of a software CQA (Stack Overflow). Our key
finding is that the quality of an answer is strongly positively correlated with
that of its question. Armed with this observation, we propose a family of
algorithms to jointly predict the quality of questions and answers, for both
quantifying numerical quality scores and differentiating the high-quality
questions/answers from those of low quality. We conduct extensive experimental
evaluations to demonstrate the effectiveness and efficiency of our methods.
Automatic Road Lighting System (ARLS) Model Based on Image Processing of Moving Object
Authors: Suprijadi, Thomas Muliawan, Sparisoma Viridi
Date: 2011-07-05 00:00:00
Tasks: Position

Using a vehicle toy (in next future called vehicle) as a moving object an
automatic road lighting system (ARLS) model is constructed. A digital video
camera with 25 fps is used to capture the vehicle motion as it moves in the
test segment of the road. Captured images are then processed to calculate
vehicle speed. This information of the speed together with position of vehicle
is then used to control the lighting system along the path that passes by the
vehicle. Length of the road test segment is 1 m, the video camera is positioned
about 1.1 m above the test segment, and the vehicle toy dimension is 13 cm
\times 9.3 cm. In this model, the maximum speed that ARLS can handle is about
1.32 m/s, and the highest performance is obtained about 91% at speed 0.93 m/s.
Learning Prices for Repeated Auctions with Strategic Buyers
Authors: Kareem Amin, Afshin Rostamizadeh, Umar Syed
Date: 2013-11-26 00:00:00
Proceeding: NeurIPS 2013 12

Inspired by real-time ad exchanges for online display advertising, we
consider the problem of inferring a buyer's value distribution for a good when
the buyer is repeatedly interacting with a seller through a posted-price
mechanism. We model the buyer as a strategic agent, whose goal is to maximize
her long-term surplus, and we are interested in mechanisms that maximize the
seller's long-term revenue. We define the natural notion of strategic regret
--- the lost revenue as measured against a truthful (non-strategic) buyer. We
present seller algorithms that are no-(strategic)-regret when the buyer
discounts her future surplus --- i.e. the buyer prefers showing advertisements
to users sooner rather than later. We also give a lower bound on strategic
regret that increases as the buyer's discounting weakens and shows, in
particular, that any seller algorithm will suffer linear strategic regret if
there is no discounting.
Dynamic Pricing with Limited Supply
Authors: Moshe Babaioff, Shaddin Dughmi, Robert Kleinberg, Aleksandrs Slivkins
Date: 2011-08-20 00:00:00
Tasks: Multi-Armed Bandits

We consider the problem of dynamic pricing with limited supply. A seller has
$k$ identical items for sale and is facing $n$ potential buyers ("agents") that
are arriving sequentially. Each agent is interested in buying one item. Each
agent's value for an item is an IID sample from some fixed distribution with
support $[0,1]$. The seller offers a take-it-or-leave-it price to each arriving
agent (possibly different for different agents), and aims to maximize his
expected revenue.
  We focus on "prior-independent" mechanisms -- ones that do not use any
information about the distribution. They are desirable because knowing the
distribution is unrealistic in many practical scenarios. We study how the
revenue of such mechanisms compares to the revenue of the optimal offline
mechanism that knows the distribution ("offline benchmark").
  We present a prior-independent dynamic pricing mechanism whose revenue is at
most $O((k \log n)^{2/3})$ less than the offline benchmark, for every
distribution that is regular. In fact, this guarantee holds without *any*
assumptions if the benchmark is relaxed to fixed-price mechanisms. Further, we
prove a matching lower bound. The performance guarantee for the same mechanism
can be improved to $O(\sqrt{k} \log n)$, with a distribution-dependent
constant, if $k/n$ is sufficiently small. We show that, in the worst case over
all demand distributions, this is essentially the best rate that can be
obtained with a distribution-specific constant.
  On a technical level, we exploit the connection to multi-armed bandits (MAB).
While dynamic pricing with unlimited supply can easily be seen as an MAB
problem, the intuition behind MAB approaches breaks when applied to the setting
with limited supply. Our high-level conceptual contribution is that even the
limited supply setting can be fruitfully treated as a bandit problem.
Supervised Texture Classification Using a Novel Compression-Based Similarity Measure
Authors: Mehrdad J. Gangeh, Ali Ghodsi, Mohamed S. Kamel
Date: 2012-07-12 00:00:00
Tasks: Classification, General Classification, Texture Classification

Supervised pixel-based texture classification is usually performed in the
feature space. We propose to perform this task in (dis)similarity space by
introducing a new compression-based (dis)similarity measure. The proposed
measure utilizes two dimensional MPEG-1 encoder, which takes into consideration
the spatial locality and connectivity of pixels in the images. The proposed
formulation has been carefully designed based on MPEG encoder functionality. To
this end, by design, it solely uses P-frame coding to find the (dis)similarity
among patches/images. We show that the proposed measure works properly on both
small and large patch sizes. Experimental results show that the proposed
approach significantly improves the performance of supervised pixel-based
texture classification on Brodatz and outdoor images compared to other
compression-based dissimilarity measures as well as approaches performed in
feature space. It also improves the computation speed by about 40% compared to
its rivals.
Kernelized Supervised Dictionary Learning
Authors: Mehrdad J. Gangeh, Ali Ghodsi, Mohamed S. Kamel
Date: 2012-07-10 00:00:00
Tasks: Dictionary Learning

In this paper, we propose supervised dictionary learning (SDL) by
incorporating information on class labels into the learning of the dictionary.
To this end, we propose to learn the dictionary in a space where the dependency
between the signals and their corresponding labels is maximized. To maximize
this dependency, the recently introduced Hilbert Schmidt independence criterion
(HSIC) is used. One of the main advantages of this novel approach for SDL is
that it can be easily kernelized by incorporating a kernel, particularly a
data-derived kernel such as normalized compression distance, into the
formulation. The learned dictionary is compact and the proposed approach is
fast. We show that it outperforms other unsupervised and supervised dictionary
learning approaches in the literature, using real-world data.
Statistical Inference in Dynamic Treatment Regimes
Authors: Eric B. Laber, Min Qian, Dan J. Lizotte, William E. Pelham, Susan A. Murphy
Date: 2010-06-30 00:00:00
Tasks: Decision Making

Dynamic treatment regimes are of growing interest across the clinical
sciences as these regimes provide one way to operationalize and thus inform
sequential personalized clinical decision making. A dynamic treatment regime is
a sequence of decision rules, with a decision rule per stage of clinical
intervention; each decision rule maps up-to-date patient information to a
recommended treatment. We briefly review a variety of approaches for using data
to construct the decision rules. We then review an interesting challenge, that
of nonregularity that often arises in this area. By nonregularity, we mean the
parameters indexing the optimal dynamic treatment regime are nonsmooth
functionals of the underlying generative distribution.
  A consequence is that no regular or asymptotically unbiased estimator of
these parameters exists. Nonregularity arises in inference for parameters in
the optimal dynamic treatment regime; we illustrate the effect of nonregularity
on asymptotic bias and via sensitivity of asymptotic, limiting, distributions
to local perturbations. We propose and evaluate a locally consistent Adaptive
Confidence Interval (ACI) for the parameters of the optimal dynamic treatment
regime. We use data from the Adaptive Interventions for Children with ADHD
study as an illustrative example. We conclude by highlighting and discussing
emerging theoretical problems in this area.
A Framework for Semi-automated Web Service Composition in Semantic Web
Authors: Debajyoti Mukhopadhyay, Archana Chougule
Date: 2013-11-26 00:00:00
Tasks: Service Composition

Number of web services available on Internet and its usage are increasing
very fast. In many cases, one service is not enough to complete the business
requirement; composition of web services is carried out. Autonomous composition
of web services to achieve new functionality is generating considerable
attention in semantic web domain. Development time and effort for new
applications can be reduced with service composition. Various approaches to
carry out automated composition of web services are discussed in literature.
Web service composition using ontologies is one of the effective approaches. In
this paper we demonstrate how the ontology based composition can be made faster
for each customer. We propose a framework to provide precomposed web services
to fulfil user requirements. We detail how ontology merging can be used for
composition which expedites the whole process. We discuss how framework
provides customer specific ontology merging and repository. We also elaborate
on how merging of ontologies is carried out.