pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Generation of Typographic Font from a Small Font Subset
Authors: Tomo Miyazaki, Tatsunori Tsuchiya, Yoshihiro Sugaya, Shinichiro Omachi, Masakazu Iwamura, Seiichi Uchida, Koichi Kise
Date: 2017-01-20 00:00:00
Tasks: Font Generation

This paper addresses the automatic generation of a typographic font from a
subset of characters. Specifically, we use a subset of a typographic font to
extrapolate additional characters. Consequently, we obtain a complete font
containing a number of characters sufficient for daily use. The automated
generation of Japanese fonts is in high demand because a Japanese font requires
over 1,000 characters. Unfortunately, professional typographers create most
fonts, resulting in significant financial and time investments for font
generation. The proposed method can be a great aid for font creation because
designers do not need to create the majority of the characters for a new font.
The proposed method uses strokes from given samples for font generation. The
strokes, from which we construct characters, are extracted by exploiting a
character skeleton dataset. This study makes three main contributions: a novel
method of extracting strokes from characters, which is applicable to both
standard fonts and their variations; a fully automated approach for
constructing characters; and a selection method for sample characters. We
demonstrate our proposed method by generating 2,965 characters in 47 fonts.
Objective and subjective evaluations verify that the generated characters are
similar to handmade characters.
Efficient Feature Matching by Progressive Candidate Search
Authors: Sehyung Lee, Jongwoo Lim, Il Hong Suh
Date: 2017-01-20 00:00:00

We present a novel feature matching algorithm that systematically utilizes
the geometric properties of features such as position, scale, and orientation,
in addition to the conventional descriptor vectors. In challenging scenes with
the presence of repetitive patterns or with a large viewpoint change, it is
hard to find the correct correspondences using feature descriptors only, since
the descriptor distances of the correct matches may not be the least among the
candidates due to appearance changes. Assuming that the layout of the nearby
features does not changed much, we propose the bidirectional transfer measure
to gauge the geometric consistency of a pair of feature correspondences. The
feature matching problem is formulated as a Markov random field (MRF) which
uses descriptor distances and relative geometric similarities together. The
unmatched features are explicitly modeled in the MRF to minimize its negative
impact. For speed and stability, instead of solving the MRF on the entire
features at once, we start with a small set of confident feature matches, and
then progressively search the candidates in nearby features and expand the MRF
with them. Experimental comparisons show that the proposed algorithm finds
better feature correspondences, i.e. more matches with higher inlier ratio, in
many challenging scenes with much lower computational cost than the
state-of-the-art algorithms.
Stability Enhanced Large-Margin Classifier Selection
Authors: Will Wei Sun, Guang Cheng, Yufeng Liu
Date: 2017-01-20 00:00:00
Tasks: Classification, General Classification

Stability is an important aspect of a classification procedure because
unstable predictions can potentially reduce users' trust in a classification
system and also harm the reproducibility of scientific conclusions. The major
goal of our work is to introduce a novel concept of classification instability,
i.e., decision boundary instability (DBI), and incorporate it with the
generalization error (GE) as a standard for selecting the most accurate and
stable classifier. Specifically, we implement a two-stage algorithm: (i)
initially select a subset of classifiers whose estimated GEs are not
significantly different from the minimal estimated GE among all the candidate
classifiers; (ii) the optimal classifier is chosen as the one achieving the
minimal DBI among the subset selected in stage (i). This general selection
principle applies to both linear and nonlinear classifiers. Large-margin
classifiers are used as a prototypical example to illustrate the above idea.
Our selection method is shown to be consistent in the sense that the optimal
classifier simultaneously achieves the minimal GE and the minimal DBI. Various
simulations and real examples further demonstrate the advantage of our method
over several alternative approaches.
Statistics of Visual Responses to Object Stimuli from Primate AIT Neurons to DNN Neurons
Authors: Qiulei Dong, Zhanyi Hu
Date: 2016-12-12 00:00:00
Tasks: Object, Object Recognition

Cadieu et al. (Cadieu,2014) reported that deep neural networks(DNNs) could
rival the representation of primate inferotemporal cortex for object
recognition. Lehky et al. (Lehky,2011) provided a statistical analysis on
neural responses to object stimuli in primate AIT cortex. They found the
intrinsic dimensionality of object representations in AIT cortex is around 100
(Lehky,2014). Considering the outstanding performance of DNNs in object
recognition, it is worthwhile investigating whether the responses of DNN
neurons have similar response statistics to those of AIT neurons. Following
Lehky et al.'s works, we analyze the response statistics to image stimuli and
the intrinsic dimensionality of object representations of DNN neurons. Our
findings show in terms of kurtosis and Pareto tail index, the response
statistics on single-neuron selectivity and population sparseness of DNN
neurons are fundamentally different from those of IT neurons except some
special cases. By increasing the number of neurons and stimuli, the conclusions
could alter substantially. In addition, with the ascendancy of the
convolutional layers of DNNs, the single-neuron selectivity and population
sparseness of DNN neurons increase, indicating the last convolutional layer is
to learn features for object representations, while the following
fully-connected layers are to learn categorization features. It is also found
that a sufficiently large number of stimuli and neurons are necessary for
obtaining a stable dimensionality. To our knowledge, this is the first work to
analyze the response statistics of DNN neurons comparing with AIT neurons, and
our results provide not only some insights into the discrepancy of DNN neurons
with respect to IT neurons in object representation, but also shed some light
on possible outcomes of IT neurons when the number of recorded neurons and
stimuli is beyond the level in (Lehky,2011,2014).
Random Forest Missing Data Algorithms
Authors: Fei Tang, Hemant Ishwaran
Date: 2017-01-19 00:00:00
Tasks: Imputation

Random forest (RF) missing data algorithms are an attractive approach for
dealing with missing data. They have the desirable properties of being able to
handle mixed types of missing data, they are adaptive to interactions and
nonlinearity, and they have the potential to scale to big data settings.
Currently there are many different RF imputation algorithms but relatively
little guidance about their efficacy, which motivated us to study their
performance. Using a large, diverse collection of data sets, performance of
various RF algorithms was assessed under different missing data mechanisms.
Algorithms included proximity imputation, on the fly imputation, and imputation
utilizing multivariate unsupervised and supervised splitting---the latter class
representing a generalization of a new promising imputation algorithm called
missForest. Performance of algorithms was assessed by ability to impute data
accurately. Our findings reveal RF imputation to be generally robust with
performance improving with increasing correlation. Performance was good under
moderate to high missingness, and even (in certain cases) when data was missing
not at random.
Rare Disease Physician Targeting: A Factor Graph Approach
Authors: Yong Cai, Yunlong Wang, Dong Dai
Date: 2017-01-19 00:00:00

In rare disease physician targeting, a major challenge is how to identify
physicians who are treating diagnosed or underdiagnosed rare diseases patients.
Rare diseases have extremely low incidence rate. For a specified rare disease,
only a small number of patients are affected and a fractional of physicians are
involved. The existing targeting methodologies, such as segmentation and
profiling, are developed under mass market assumption. They are not suitable
for rare disease market where the target classes are extremely imbalanced. The
authors propose a graphical model approach to predict targets by jointly
modeling physician and patient features from different data spaces and
utilizing the extra relational information. Through an empirical example with
medical claim and prescription data, the proposed approach demonstrates better
accuracy in finding target physicians. The graph representation also provides
visual interpretability of relationship among physicians and patients. The
model can be extended to incorporate more complex dependency structures. This
article contributes to the literature of exploring the benefit of utilizing
relational dependencies among entities in healthcare industry.
The Internet as Quantitative Social Science Platform: Insights from a Trillion Observations
Authors: Klaus Ackermann, Simon D Angus, Paul A Raschky
Date: 2017-01-19 00:00:00

With the large-scale penetration of the internet, for the first time,
humanity has become linked by a single, open, communications platform.
Harnessing this fact, we report insights arising from a unified internet
activity and location dataset of an unparalleled scope and accuracy drawn from
over a trillion (1.5$\times 10^{12}$) observations of end-user internet
connections, with temporal resolution of just 15min over 2006-2012. We first
apply this dataset to the expansion of the internet itself over 1,647 urban
agglomerations globally. We find that unique IP per capita counts reach
saturation at approximately one IP per three people, and take, on average, 16.1
years to achieve; eclipsing the estimated 100- and 60- year saturation times
for steam-power and electrification respectively. Next, we use intra-diurnal
internet activity features to up-scale traditional over-night sleep
observations, producing the first global estimate of over-night sleep duration
in 645 cities over 7 years. We find statistically significant variation between
continental, national and regional sleep durations including some evidence of
global sleep duration convergence. Finally, we estimate the relationship
between internet concentration and economic outcomes in 411 OECD regions and
find that the internet's expansion is associated with negative or positive
productivity gains, depending strongly on sectoral considerations. To our
knowledge, our study is the first of its kind to use online/offline activity of
the entire internet to infer social science insights, demonstrating the
unparalleled potential of the internet as a social data-science platform.
CEVO: Comprehensive EVent Ontology Enhancing Cognitive Annotation
Authors: Saeedeh Shekarpour, Faisal Al-Shargi, Valerie Shalin, Krishnaprasad Thirunarayan, Amit P. Sheth
Date: 2017-01-19 00:00:00

While the general analysis of named entities has received substantial
research attention on unstructured as well as structured data, the analysis of
relations among named entities has received limited focus. In fact, a review of
the literature revealed a deficiency in research on the abstract
conceptualization required to organize relations. We believe that such an
abstract conceptualization can benefit various communities and applications
such as natural language processing, information extraction, machine learning,
and ontology engineering. In this paper, we present Comprehensive EVent
Ontology (CEVO), built on Levin's conceptual hierarchy of English verbs that
categorizes verbs with shared meaning, and syntactic behavior. We present the
fundamental concepts and requirements for this ontology. Furthermore, we
present three use cases employing the CEVO ontology on annotation tasks: (i)
annotating relations in plain text, (ii) annotating ontological properties, and
(iii) linking textual relations to ontological properties. These use-cases
demonstrate the benefits of using CEVO for annotation: (i) annotating English
verbs from an abstract conceptualization, (ii) playing the role of an upper
ontology for organizing ontological properties, and (iii) facilitating the
annotation of text relations using any underlying vocabulary. This resource is
available at https://shekarpour.github.io/cevo.io/ using https://w3id.org/cevo
namespace.
Holistic Interstitial Lung Disease Detection using Deep Convolutional Neural Networks: Multi-label Learning and Unordered Pooling
Authors: Mingchen Gao, Ziyue Xu, Le Lu, Adam P. Harrison, Ronald M. Summers, Daniel J. Mollura
Date: 2017-01-19 00:00:00
Tasks: Computed Tomography (CT), Multi-Label Learning, Texture Classification

Accurately predicting and detecting interstitial lung disease (ILD) patterns
given any computed tomography (CT) slice without any pre-processing
prerequisites, such as manually delineated regions of interest (ROIs), is a
clinically desirable, yet challenging goal. The majority of existing work
relies on manually-provided ILD ROIs to extract sampled 2D image patches from
CT slices and, from there, performs patch-based ILD categorization. Acquiring
manual ROIs is labor intensive and serves as a bottleneck towards
fully-automated CT imaging ILD screening over large-scale populations.
Furthermore, despite the considerable high frequency of more than one ILD
pattern on a single CT slice, previous works are only designed to detect one
ILD pattern per slice or patch.
  To tackle these two critical challenges, we present multi-label deep
convolutional neural networks (CNNs) for detecting ILDs from holistic CT slices
(instead of ROIs or sub-images). Conventional single-labeled CNN models can be
augmented to cope with the possible presence of multiple ILD pattern labels,
via 1) continuous-valued deep regression based robust norm loss functions or 2)
a categorical objective as the sum of element-wise binary logistic losses. Our
methods are evaluated and validated using a publicly available database of 658
patient CT scans under five-fold cross-validation, achieving promising
performance on detecting four major ILD patterns: Ground Glass, Reticular,
Honeycomb, and Emphysema. We also investigate the effectiveness of a CNN
activation-based deep-feature encoding scheme using Fisher vector encoding,
which treats ILD detection as spatially-unordered deep texture classification.
AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games
Authors: Neil Burch, Martin Schmid, Matej Moravčík, Michael Bowling
Date: 2016-12-20 00:00:00

Evaluating agent performance when outcomes are stochastic and agents use
randomized strategies can be challenging when there is limited data available.
The variance of sampled outcomes may make the simple approach of Monte Carlo
sampling inadequate. This is the case for agents playing heads-up no-limit
Texas hold'em poker, where man-machine competitions have involved multiple days
of consistent play and still not resulted in statistically significant
conclusions even when the winner's margin is substantial. In this paper, we
introduce AIVAT, a low variance, provably unbiased value assessment tool that
uses an arbitrary heuristic estimate of state value, as well as the explicit
strategy of a subset of the agents. Unlike existing techniques which reduce the
variance from chance events, or only consider game ending actions, AIVAT
reduces the variance both from choices by nature and by players with a known
strategy. The resulting estimator in no-limit poker can reduce the number of
hands needed to draw statistical conclusions by more than a factor of 10.
Fast and Efficient Skin Detection for Facial Detection
Authors: Mohammad Reza Mahmoodi
Date: 2017-01-19 00:00:00

In this paper, an efficient skin detection system is proposed. The algorithm
is based on a very fast efficient pre-processing step utilizing the concept of
ternary conversion in order to identify candidate windows and subsequently, a
novel local two-stage diffusion method which has F-score accuracy of 0.5978 on
SDD dataset. The pre-processing step has been proven to be useful to boost the
speed of the system by eliminating 82% of an image in average. This is obtained
by keeping the true positive rate above 98%. In addition, a novel segmentation
algorithm is also designed to process candidate windows which is quantitatively
and qualitatively proven to be very efficient in term of accuracy. The
algorithm has been implemented in FPGA to obtain real-time processing speed.
The system is designed fully pipeline and the inherent parallel structure of
the algorithm is fully exploited to maximize the performance. The system is
implemented on a Spartan-6 LXT45 Xilinx FPGA and it is capable of processing 98
frames of 640*480 24-bit color images per second.
Parameter Selection Algorithm For Continuous Variables
Authors: Peyman Tavallali, Marianne Razavi, Sean Brady
Date: 2017-01-19 00:00:00
Tasks: Model Selection, regression

In this article, we propose a new algorithm for supervised learning methods,
by which one can both capture the non-linearity in data and also find the best
subset model. To produce an enhanced subset of the original variables, an ideal
selection method should have the potential of adding a supplementary level of
regression analysis that would capture complex relationships in the data via
mathematical transformation of the predictors and exploration of synergistic
effects of combined variables. The method that we present here has the
potential to produce an optimal subset of variables, rendering the overall
process of model selection to be more efficient. The core objective of this
paper is to introduce a new estimation technique for the classical least square
regression framework. This new automatic variable transformation and model
selection method could offer an optimal and stable model that minimizes the
mean square error and variability, while combining all possible subset
selection methodology and including variable transformations and interaction.
Moreover, this novel method controls multicollinearity, leading to an optimal
set of explanatory variables.
High Performance Novel Skin Segmentation Algorithm for Images With Complex Background
Authors: Mohammad Reza Mahmoodi
Date: 2017-01-19 00:00:00
Tasks: Face Detection, Face Recognition, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Segmentation, Vocal Bursts Intensity Prediction

Skin Segmentation is widely used in biometric applications such as face
detection, face recognition, face tracking, and hand gesture recognition.
However, several challenges such as nonlinear illumination, equipment effects,
personal interferences, ethnicity variations, etc., are involved in detection
process that result in the inefficiency of color based methods. Even though
many ideas have already been proposed, the problem has not been satisfactorily
solved yet. This paper introduces a technique that addresses some limitations
of the previous works. The proposed algorithm consists of three main steps
including initial seed generation of skin map, Otsu segmentation in color
images, and finally a two-stage diffusion. The initial seed of skin pixels is
provided based on the idea of ternary image as there are certain pixels in
images which are associated to human complexion with very high probability. The
Otsu segmentation is performed on several color channels in order to identify
homogeneous regions. The result accompanying with the edge map of the image is
utilized in two consecutive diffusion steps in order to annex initially
unidentified skin pixels to the seed. Both quantitative and qualitative results
demonstrate the effectiveness of the proposed system in compare with the
state-of-the-art works.
Leveraging Cognitive Features for Sentiment Analysis
Authors: Abhijit Mishra, Diptesh Kanojia, Seema Nagar, Kuntal Dey, Pushpak Bhattacharyya
Date: 2017-01-19 00:00:00
Proceeding: CONLL 2016 8
Tasks: General Classification, Sarcasm Detection, Sentiment Analysis

Sentiments expressed in user-generated short text and sentences are nuanced
by subtleties at lexical, syntactic, semantic and pragmatic levels. To address
this, we propose to augment traditional features used for sentiment analysis
and sarcasm detection, with cognitive features derived from the eye-movement
patterns of readers. Statistical classification using our enhanced feature set
improves the performance (F-score) of polarity detection by a maximum of 3.7%
and 9.3% on two datasets, over the systems that use only traditional features.
We perform feature significance analysis, and experiment on a held-out dataset,
showing that cognitive features indeed empower sentiment analyzers to handle
complex constructs.
Harnessing Cognitive Features for Sarcasm Detection
Authors: Abhijit Mishra, Diptesh Kanojia, Seema Nagar, Kuntal Dey, Pushpak Bhattacharyya
Date: 2017-01-19 00:00:00
Proceeding: ACL 2016 8
Tasks: Sarcasm Detection, Sentence, Sentiment Analysis

In this paper, we propose a novel mechanism for enriching the feature vector,
for the task of sarcasm detection, with cognitive features extracted from
eye-movement patterns of human readers. Sarcasm detection has been a
challenging research problem, and its importance for NLP applications such as
review summarization, dialog systems and sentiment analysis is well recognized.
Sarcasm can often be traced to incongruity that becomes apparent as the full
sentence unfolds. This presence of incongruity- implicit or explicit- affects
the way readers eyes move through the text. We observe the difference in the
behaviour of the eye, while reading sarcastic and non sarcastic sentences.
Motivated by his observation, we augment traditional linguistic and stylistic
features for sarcasm detection with the cognitive features obtained from
readers eye movement data. We perform statistical classification using the
enhanced feature set so obtained. The augmented cognitive features improve
sarcasm detection by 3.7% (in terms of F-score), over the performance of the
best reported system.
Poisson--Gamma Dynamical Systems
Authors: Aaron Schein, Mingyuan Zhou, Hanna Wallach
Date: 2017-01-19 00:00:00
Tasks: Inductive Bias

We introduce a new dynamical system for sequentially observed multivariate
count data. This model is based on the gamma--Poisson construction---a natural
choice for count data---and relies on a novel Bayesian nonparametric prior that
ties and shrinks the model parameters, thus avoiding overfitting. We present an
efficient MCMC inference algorithm that advances recent work on augmentation
schemes for inference in negative binomial models. Finally, we demonstrate the
model's inductive bias using a variety of real-world data sets, showing that it
exhibits superior predictive performance over other models and infers highly
interpretable latent structure.