pwc-archive/papers-with-abstracts huggingface dataset:
A Framework for Fast Face and Eye Detection
Authors: Anjith George, Anirban Dasgupta, Aurobinda Routray
Date: 2015-05-13 00:00:00
Tasks: Face Detection

Face detection is an essential step in many computer vision applications like
surveillance, tracking, medical analysis, facial expression analysis etc.
Several approaches have been made in the direction of face detection. Among
them, Haar-like features based method is a robust method. In spite of the
robustness, Haar - like features work with some limitations. However, with some
simple modifications in the algorithm, its performance can be made faster and
more robust. The present work refers to the increase in speed of operation of
the original algorithm by down sampling the frames and its analysis with
different scale factors. It also discusses the detection of tilted faces using
an affine transformation of the input image.
Optimal linear estimation under unknown nonlinear transform
Authors: Xinyang Yi, Zhaoran Wang, Constantine Caramanis, Han Liu
Date: 2015-05-13 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: compressed sensing

Linear regression studies the problem of estimating a model parameter
$\beta^* \in \mathbb{R}^p$, from $n$ observations
$\{(y_i,\mathbf{x}_i)\}_{i=1}^n$ from linear model $y_i = \langle
\mathbf{x}_i,\beta^* \rangle + \epsilon_i$. We consider a significant
generalization in which the relationship between $\langle \mathbf{x}_i,\beta^*
\rangle$ and $y_i$ is noisy, quantized to a single bit, potentially nonlinear,
noninvertible, as well as unknown. This model is known as the single-index
model in statistics, and, among other things, it represents a significant
generalization of one-bit compressed sensing. We propose a novel spectral-based
estimation procedure and show that we can recover $\beta^*$ in settings (i.e.,
classes of link function $f$) where previous algorithms fail. In general, our
algorithm requires only very mild restrictions on the (unknown) functional
relationship between $y_i$ and $\langle \mathbf{x}_i,\beta^* \rangle$. We also
consider the high dimensional setting where $\beta^*$ is sparse ,and introduce
a two-stage nonconvex framework that addresses estimation challenges in high
dimensional regimes where $p \gg n$. For a broad class of link functions
between $\langle \mathbf{x}_i,\beta^* \rangle$ and $y_i$, we establish minimax
lower bounds that demonstrate the optimality of our estimators in both the
classical and high dimensional regimes.
Feature selection using Fisher's ratio technique for automatic speech recognition
Authors: Sarika Hegde, K. K. Achary, Surendra Shetty
Date: 2015-05-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Classification, feature selection, General Classification, speech-recognition, Speech Recognition

Automatic Speech Recognition involves mainly two steps; feature extraction
and classification . Mel Frequency Cepstral Coefficient is used as one of the
prominent feature extraction techniques in ASR. Usually, the set of all 12 MFCC
coefficients is used as the feature vector in the classification step. But the
question is whether the same or improved classification accuracy can be
achieved by using a subset of 12 MFCC as feature vector. In this paper,
Fisher's ratio technique is used for selecting a subset of 12 MFCC coefficients
that contribute more in discriminating a pattern. The selected coefficients are
used in classification with Hidden Markov Model algorithm. The classification
accuracies that we get by using 12 coefficients and by using the selected
coefficients are compared.
Hybrid data clustering approach using K-Means and Flower Pollination Algorithm
Authors: R. Jensi, G. Wiselin Jiji
Date: 2015-05-13 00:00:00
Tasks: Clustering, global-optimization

Data clustering is a technique for clustering set of objects into known
number of groups. Several approaches are widely applied to data clustering so
that objects within the clusters are similar and objects in different clusters
are far away from each other. K-Means, is one of the familiar center based
clustering algorithms since implementation is very easy and fast convergence.
However, K-Means algorithm suffers from initialization, hence trapped in local
optima. Flower Pollination Algorithm (FPA) is the global optimization
technique, which avoids trapping in local optimum solution. In this paper, a
novel hybrid data clustering approach using Flower Pollination Algorithm and
K-Means (FPAKM) is proposed. The proposed algorithm results are compared with
K-Means and FPA on eight datasets. From the experimental results, FPAKM is
better than FPA and K-Means.
APAC: Augmented PAttern Classification with Neural Networks
Authors: Ikuro Sato, Hiroki Nishimura, Kensuke Yokoi
Date: 2015-05-13 00:00:00
Tasks: Classification, Data Augmentation, General Classification, Image Classification

Deep neural networks have been exhibiting splendid accuracies in many of
visual pattern classification problems. Many of the state-of-the-art methods
employ a technique known as data augmentation at the training stage. This paper
addresses an issue of decision rule for classifiers trained with augmented
data. Our method is named as APAC: the Augmented PAttern Classification, which
is a way of classification using the optimal decision rule for augmented data
learning. Discussion of methods of data augmentation is not our primary focus.
We show clear evidences that APAC gives far better generalization performance
than the traditional way of class prediction in several experiments. Our
convolutional neural network model with APAC achieved a state-of-the-art
accuracy on the MNIST dataset among non-ensemble classifiers. Even our
multilayer perceptron model beats some of the convolutional models with
recently invented stochastic regularization techniques on the CIFAR-10 dataset.
PISA: Pixelwise Image Saliency by Aggregating Complementary Appearance Contrast Measures with Edge-Preserving Coherence
Authors: Keze Wang, Liang Lin, Jiangbo Lu, Chenglong Li, Keyang Shi
Date: 2015-05-13 00:00:00
Proceeding: CVPR 2013
Tasks: Image Segmentation, Object Recognition, Saliency Detection, Semantic Segmentation

Driven by recent vision and graphics applications such as image segmentation
and object recognition, computing pixel-accurate saliency values to uniformly
highlight foreground objects becomes increasingly important. In this paper, we
propose a unified framework called PISA, which stands for Pixelwise Image
Saliency Aggregating various bottom-up cues and priors. It generates spatially
coherent yet detail-preserving, pixel-accurate and fine-grained saliency, and
overcomes the limitations of previous methods which use homogeneous
superpixel-based and color only treatment. PISA aggregates multiple saliency
cues in a global context such as complementary color and structure contrast
measures with their spatial priors in the image domain. The saliency confidence
is further jointly modeled with a neighborhood consistence constraint into an
energy minimization formulation, in which each pixel will be evaluated with
multiple hypothetical saliency levels. Instead of using global discrete
optimization methods, we employ the cost-volume filtering technique to solve
our formulation, assigning the saliency levels smoothly while preserving the
edge-aware structure details. In addition, a faster version of PISA is
developed using a gradient-driven image sub-sampling strategy to greatly
improve the runtime efficiency while keeping comparable detection accuracy.
Extensive experiments on a number of public datasets suggest that PISA
convincingly outperforms other state-of-the-art approaches. In addition, with
this work we also create a new dataset containing $800$ commodity images for
evaluating saliency detection. The dataset and source code of PISA can be
downloaded at http://vision.sysu.edu.cn/project/PISA/
Exemplar Dynamics and Sound Merger in Language
Authors: P. F. Tupper
Date: 2014-12-02 00:00:00

We develop a model of phonological contrast in natural language.
Specifically, the model describes the maintenance of contrast between different
words in a language, and the elimination of such contrast when sounds in the
words merge. An example of such a contrast is that provided by the two vowel
sounds 'i' and 'e', which distinguish pairs of words such as 'pin' and 'pen' in
most dialects of English. We model language users' knowledge of the
pronunciation of a word as consisting of collections of labeled exemplars
stored in memory. Each exemplar is a detailed memory of a particular utterance
of the word in question. In our model an exemplar is represented by one or two
phonetic variables along with a weight indicating how strong the memory of the
utterance is. Starting from an exemplar-level model we derive
integro-differential equations for the evolution of exemplar density fields in
phonetic space. Using these latter equations we investigate under what
conditions two sounds merge, thus eliminating the contrast. Our main conclusion
is that for the preservation of phonological contrast, it is necessary that
anomalous utterances of a given word are discarded, and not merely stored in
memory as an exemplar of another word.
Equitability, interval estimation, and statistical power
Authors: Yakir A. Reshef, David N. Reshef, Pardis C. Sabeti, Michael M. Mitzenmacher
Date: 2015-05-09 00:00:00
Tasks: Two-sample testing

For analysis of a high-dimensional dataset, a common approach is to test a
null hypothesis of statistical independence on all variable pairs using a
non-parametric measure of dependence. However, because this approach attempts
to identify any non-trivial relationship no matter how weak, it often
identifies too many relationships to be useful. What is needed is a way of
identifying a smaller set of relationships that merit detailed further
analysis.
  Here we formally present and characterize equitability, a property of
measures of dependence that aims to overcome this challenge. Notionally, an
equitable statistic is a statistic that, given some measure of noise, assigns
similar scores to equally noisy relationships of different types [Reshef et al.
2011]. We begin by formalizing this idea via a new object called the
interpretable interval, which functions as an interval estimate of the amount
of noise in a relationship of unknown type. We define an equitable statistic as
one with small interpretable intervals.
  We then draw on the equivalence of interval estimation and hypothesis testing
to show that under moderate assumptions an equitable statistic is one that
yields well powered tests for distinguishing not only between trivial and
non-trivial relationships of all kinds but also between non-trivial
relationships of different strengths. This means that equitability allows us to
specify a threshold relationship strength $x_0$ and to search for relationships
of all kinds with strength greater than $x_0$. Thus, equitability can be
thought of as a strengthening of power against independence that enables
fruitful analysis of data sets with a small number of strong, interesting
relationships and a large number of weaker ones. We conclude with a
demonstration of how our two equivalent characterizations of equitability can
be used to evaluate the equitability of a statistic in practice.
Theoretical Foundations of Equitability and the Maximal Information Coefficient
Authors: Yakir A. Reshef, David N. Reshef, Pardis C. Sabeti, Michael Mitzenmacher
Date: 2014-08-21 00:00:00

The maximal information coefficient (MIC) is a tool for finding the strongest
pairwise relationships in a data set with many variables (Reshef et al., 2011).
MIC is useful because it gives similar scores to equally noisy relationships of
different types. This property, called {\em equitability}, is important for
analyzing high-dimensional data sets.
  Here we formalize the theory behind both equitability and MIC in the language
of estimation theory. This formalization has a number of advantages. First, it
allows us to show that equitability is a generalization of power against
statistical independence. Second, it allows us to compute and discuss the
population value of MIC, which we call MIC_*. In doing so we generalize and
strengthen the mathematical results proven in Reshef et al. (2011) and clarify
the relationship between MIC and mutual information. Introducing MIC_* also
enables us to reason about the properties of MIC more abstractly: for instance,
we show that MIC_* is continuous and that there is a sense in which it is a
canonical "smoothing" of mutual information. We also prove an alternate,
equivalent characterization of MIC_* that we use to state new estimators of it
as well as an algorithm for explicitly computing it when the joint probability
density function of a pair of random variables is known. Our hope is that this
paper provides a richer theoretical foundation for MIC and equitability going
forward.
  This paper will be accompanied by a forthcoming companion paper that performs
extensive empirical analysis and comparison to other methods and discusses the
practical aspects of both equitability and the use of MIC and its related
statistics.
An Empirical Study of Leading Measures of Dependence
Authors: David N. Reshef, Yakir A. Reshef, Pardis C. Sabeti, Michael M. Mitzenmacher
Date: 2015-05-09 00:00:00
Tasks: Mutual Information Estimation

In exploratory data analysis, we are often interested in identifying
promising pairwise associations for further analysis while filtering out
weaker, less interesting ones. This can be accomplished by computing a measure
of dependence on all variable pairs and examining the highest-scoring pairs,
provided the measure of dependence used assigns similar scores to equally noisy
relationships of different types. This property, called equitability, is
formalized in Reshef et al. [2015b]. In addition to equitability, measures of
dependence can also be assessed by the power of their corresponding
independence tests as well as their runtime.
  Here we present extensive empirical evaluation of the equitability, power
against independence, and runtime of several leading measures of dependence.
These include two statistics introduced in Reshef et al. [2015a]: MICe, which
has equitability as its primary goal, and TICe, which has power against
independence as its goal. Regarding equitability, our analysis finds that MICe
is the most equitable method on functional relationships in most of the
settings we considered, although mutual information estimation proves the most
equitable at large sample sizes in some specific settings. Regarding power
against independence, we find that TICe, along with Heller and Gorfine's S^DDP,
is the state of the art on the relationships we tested. Our analyses also show
a trade-off between power against independence and equitability consistent with
the theory in Reshef et al. [2015b]. In terms of runtime, MICe and TICe are
significantly faster than many other measures of dependence tested, and
computing either one makes computing the other trivial. This suggests that a
fast and useful strategy for achieving a combination of power against
independence and equitability may be to filter relationships by TICe and then
to examine the MICe of only the significant ones.
Correlation Clustering with Noisy Partial Information
Authors: Konstantin Makarychev, Yury Makarychev, Aravindan Vijayaraghavan
Date: 2014-06-22 00:00:00
Tasks: Clustering, General Classification

In this paper, we propose and study a semi-random model for the Correlation
Clustering problem on arbitrary graphs G. We give two approximation algorithms
for Correlation Clustering instances from this model. The first algorithm finds
a solution of value $(1+ \delta) optcost + O_{\delta}(n\log^3 n)$ with high
probability, where $optcost$ is the value of the optimal solution (for every
$\delta > 0$). The second algorithm finds the ground truth clustering with an
arbitrarily small classification error $\eta$ (under some additional
assumptions on the instance).
Event Retrieval Using Motion Barcodes
Authors: Gil Ben-Artzi, Michael Werman, Shmuel Peleg
Date: 2014-12-03 00:00:00
Tasks: Retrieval

We introduce a simple and effective method for retrieval of videos showing a
specific event, even when the videos of that event were captured from
significantly different viewpoints. Appearance-based methods fail in such
cases, as appearances change with large changes of viewpoints.
  Our method is based on a pixel-based feature, "motion barcode", which records
the existence/non-existence of motion as a function of time. While appearance,
motion magnitude, and motion direction can vary greatly between disparate
viewpoints, the existence of motion is viewpoint invariant. Based on the motion
barcode, a similarity measure is developed for videos of the same event taken
from very different viewpoints. This measure is robust to occlusions common
under different viewpoints, and can be computed efficiently.
  Event retrieval is demonstrated using challenging videos from stationary and
hand held cameras.
Sentiment Analysis For Modern Standard Arabic And Colloquial
Authors: Hossam S. Ibrahim, Sherif M. Abdou, Mervat Gheith
Date: 2015-05-12 00:00:00
Tasks: Arabic Sentiment Analysis, Negation, Sentence, Sentiment Analysis, Sentiment Classification

The rise of social media such as blogs and social networks has fueled
interest in sentiment analysis. With the proliferation of reviews, ratings,
recommendations and other forms of online expression, online opinion has turned
into a kind of virtual currency for businesses looking to market their
products, identify new opportunities and manage their reputations, therefore
many are now looking to the field of sentiment analysis. In this paper, we
present a feature-based sentence level approach for Arabic sentiment analysis.
Our approach is using Arabic idioms/saying phrases lexicon as a key importance
for improving the detection of the sentiment polarity in Arabic sentences as
well as a number of novels and rich set of linguistically motivated features
contextual Intensifiers, contextual Shifter and negation handling), syntactic
features for conflicting phrases which enhance the sentiment classification
accuracy. Furthermore, we introduce an automatic expandable wide coverage
polarity lexicon of Arabic sentiment words. The lexicon is built with
gold-standard sentiment words as a seed which is manually collected and
annotated and it expands and detects the sentiment orientation automatically of
new sentiment words using synset aggregation technique and free online Arabic
lexicons and thesauruses. Our data focus on modern standard Arabic (MSA) and
Egyptian dialectal Arabic tweets and microblogs (hotel reservation, product
reviews, etc.). The experimental results using our resources and techniques
with SVM classifier indicate high performance levels, with accuracies of over
95%.
A new Level-set based Protocol for Accurate Bone Segmentation from CT Imaging
Authors: Manuel Pinheiro, J. L. Alves
Date: 2015-05-12 00:00:00
Tasks: Image Cropping, Image Deconvolution, Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation

In this work it is proposed a medical image segmentation pipeline for
accurate bone segmentation from CT imaging. It is a two-step methodology, with
a pre-segmentation step and a segmentation refinement step. First, the user
performs a rough segmenting of the desired region of interest. Next, a fully
automatic refinement step is applied to the pre-segmented data. The automatic
segmentation refinement is composed by several sub-stpng, namely image
deconvolution, image cropping and interpolation. The user-defined
pre-segmentation is then refined over the deconvolved, cropped, and up-sampled
version of the image. The algorithm is applied in the segmentation of CT images
of a composite femur bone, reconstructed with different reconstruction
protocols. Segmentation outcomes are validated against a gold standard model
obtained with coordinate measuring machine Nikon Metris LK V20 with a digital
line scanner LC60-D that guarantees an accuracy of 28 $\mu m$. High sub-pixel
accuracy models were obtained for all tested Datasets. The algorithm is able to
produce high quality segmentation of the composite femur regardless of the
surface meshing strategy used.
Indonesian Social Media Sentiment Analysis With Sarcasm Detection
Authors: Edwin Lunando, Ayu Purwarianti
Date: 2015-05-12 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Sarcasm Detection, Sentiment Analysis, Sentiment Classification

Sarcasm is considered one of the most difficult problem in sentiment
analysis. In our ob-servation on Indonesian social media, for cer-tain topics,
people tend to criticize something using sarcasm. Here, we proposed two
additional features to detect sarcasm after a common sentiment analysis is
conducted. The features are the negativity information and the number of
interjection words. We also employed translated SentiWordNet in the sentiment
classification. All the classifications were conducted with machine learning
algorithms. The experimental results showed that the additional features are
quite effective in the sarcasm detection.
A Survey of Arabic Dialogues Understanding for Spontaneous Dialogues and Instant Message
Authors: AbdelRahim A. Elmadany, Sherif M. Abdou, Mervat Gheith
Date: 2015-05-12 00:00:00
Tasks: Classification, General Classification

Building dialogues systems interaction has recently gained considerable
attention, but most of the resources and systems built so far are tailored to
English and other Indo-European languages. The need for designing systems for
other languages is increasing such as Arabic language. For this reasons, there
are more interest for Arabic dialogue acts classification task because it a key
player in Arabic language understanding to building this systems. This paper
surveys different techniques for dialogue acts classification for Arabic. We
describe the main existing techniques for utterances segmentations and
classification, annotation schemas, and test corpora for Arabic dialogues
understanding that have introduced in the literature
Turn Segmentation into Utterances for Arabic Spontaneous Dialogues and Instance Messages
Authors: AbdelRahim A. Elmadany, Sherif M. Abdou, Mervat Gheith
Date: 2015-05-12 00:00:00
Tasks: Dialogue Understanding, Segmentation, Text Segmentation, Text Summarization, Translation

Text segmentation task is an essential processing task for many of Natural
Language Processing (NLP) such as text summarization, text translation,
dialogue language understanding, among others. Turns segmentation considered
the key player in dialogue understanding task for building automatic
Human-Computer systems. In this paper, we introduce a novel approach to turn
segmentation into utterances for Egyptian spontaneous dialogues and Instance
Messages (IM) using Machine Learning (ML) approach as a part of automatic
understanding Egyptian spontaneous dialogues and IM task. Due to the lack of
Egyptian dialect dialogue corpus the system evaluated by our corpus includes
3001 turns, which are collected, segmented, and annotated manually from
Egyptian call-centers. The system achieves F1 scores of 90.74% and accuracy of
95.98%.
Removing systematic errors for exoplanet search via latent causes
Authors: Bernhard Schölkopf, David W. Hogg, Dun Wang, Daniel Foreman-Mackey, Dominik Janzing, Carl-Johann Simon-Gabriel, Jonas Peters
Date: 2015-05-12 00:00:00
Tasks: Astronomy, Causal Inference, regression

We describe a method for removing the effect of confounders in order to
reconstruct a latent quantity of interest. The method, referred to as
half-sibling regression, is inspired by recent work in causal inference using
additive noise models. We provide a theoretical justification and illustrate
the potential of the method in a challenging astronomy application.