pwc-archive/papers-with-abstracts huggingface dataset:
Modification of Question Writing Style Influences Content Popularity in a Social Q&A System
Authors: Igor A. Podgorny
Date: 2016-01-15 00:00:00

TurboTax AnswerXchange is a social Q&A system supporting users working on
federal and state tax returns. Using 2015 data, we demonstrate that content
popularity (or number of views per AnswerXchange question) can be predicted
with reasonable accuracy based on attributes of the question alone. We also
employ probabilistic topic analysis and uplift modeling to identify question
features with the highest impact on popularity. We demonstrate that content
popularity is driven by behavioral attributes of AnswerXchange users and
depends on complex interactions between search ranking algorithms,
psycholinguistic factors and question writing style. Our findings provide a
rationale for employing popularity predictions to guide the users into
formulating better questions and editing the existing ones. For example,
starting question title with a question word or adding details to the question
increase number of views per question. Similar approach can be applied to
promoting AnswerXchange content indexed by Google to drive organic traffic to
TurboTax.
Maximum Entropy Kernels for System Identification
Authors: Francesca Paola Carli, Tianshi Chen, Lennart Ljung
Date: 2014-11-20 00:00:00
Tasks: Matrix Completion

A new nonparametric approach for system identification has been recently
proposed where the impulse response is modeled as the realization of a
zero-mean Gaussian process whose covariance (kernel) has to be estimated from
data. In this scheme, quality of the estimates crucially depends on the
parametrization of the covariance of the Gaussian process. A family of kernels
that have been shown to be particularly effective in the system identification
framework is the family of Diagonal/Correlated (DC) kernels. Maximum entropy
properties of a related family of kernels, the Tuned/Correlated (TC) kernels,
have been recently pointed out in the literature. In this paper we show that
maximum entropy properties indeed extend to the whole family of DC kernels. The
maximum entropy interpretation can be exploited in conjunction with results on
matrix completion problems in the graphical models literature to shed light on
the structure of the DC kernel. In particular, we prove that the DC kernel
admits a closed-form factorization, inverse and determinant. These results can
be exploited both to improve the numerical stability and to reduce the
computational complexity associated with the computation of the DC estimator.
It's about time: Online Macrotask Sequencing in Expert Crowdsourcing
Authors: Heinz Schmitz, Ioanna Lykourentzou
Date: 2016-01-15 00:00:00

We introduce the problem of Task Assignment and Sequencing (TAS), which adds
the timeline perspective to expert crowdsourcing optimization. Expert
crowdsourcing involves macrotasks, like document writing, product design, or
web development, which take more time than typical binary microtasks, require
expert skills, assume varying degrees of knowledge over a topic, and require
crowd workers to build on each other's contributions. Current works usually
assume offline optimization models, which consider worker and task arrivals
known and do not take into account the element of time. Realistically however,
time is critical: tasks have deadlines, expert workers are available only at
specific time slots, and worker/task arrivals are not known a-priori. Our work
is the first to address the problem of optimal task sequencing for online,
heterogeneous, time-constrained macrotasks. We propose tas-online, an online
algorithm that aims to complete as many tasks as possible within budget,
required quality and a given timeline, without future input information
regarding job release dates or worker availabilities. Results, comparing
tas-online to four typical benchmarks, show that it achieves more completed
jobs, lower flow times and higher job quality. This work has practical
implications for improving the Quality of Service of current crowdsourcing
platforms, allowing them to offer cost, quality and time improvements for
expert tasks.
Faster Asynchronous SGD
Authors: Augustus Odena
Date: 2016-01-15 00:00:00

Asynchronous distributed stochastic gradient descent methods have trouble
converging because of stale gradients. A gradient update sent to a parameter
server by a client is stale if the parameters used to calculate that gradient
have since been updated on the server. Approaches have been proposed to
circumvent this problem that quantify staleness in terms of the number of
elapsed updates. In this work, we propose a novel method that quantifies
staleness in terms of moving averages of gradient statistics. We show that this
method outperforms previous methods with respect to convergence speed and
scalability to many clients. We also discuss how an extension to this method
can be used to dramatically reduce bandwidth costs in a distributed training
context. In particular, our method allows reduction of total bandwidth usage by
a factor of 5 with little impact on cost convergence. We also describe (and
link to) a software library that we have used to simulate these algorithms
deterministically on a single machine.
Detecting and Extracting Events from Text Documents
Authors: Jugal Kalita
Date: 2016-01-15 00:00:00
Tasks: Articles, Event Detection, Philosophy

Events of various kinds are mentioned and discussed in text documents,
whether they are books, news articles, blogs or microblog feeds. The paper
starts by giving an overview of how events are treated in linguistics and
philosophy. We follow this discussion by surveying how events and associated
information are handled in computationally. In particular, we look at how
textual documents can be mined to extract events and ancillary information.
These days, it is mostly through the application of various machine learning
techniques. We also discuss applications of event detection and extraction
systems, particularly in summarization, in the medical domain and in the
context of Twitter posts. We end the paper with a discussion of challenges and
future directions.
Improved graph-based SFA: Information preservation complements the slowness principle
Authors: Alberto N. Escalante-B., Laurenz Wiskott
Date: 2016-01-15 00:00:00
Tasks: MORPH, Time Series, Time Series Analysis

Slow feature analysis (SFA) is an unsupervised-learning algorithm that
extracts slowly varying features from a multi-dimensional time series. A
supervised extension to SFA for classification and regression is graph-based
SFA (GSFA). GSFA is based on the preservation of similarities, which are
specified by a graph structure derived from the labels. It has been shown that
hierarchical GSFA (HGSFA) allows learning from images and other
high-dimensional data. The feature space spanned by HGSFA is complex due to the
composition of the nonlinearities of the nodes in the network. However, we show
that the network discards useful information prematurely before it reaches
higher nodes, resulting in suboptimal global slowness and an under-exploited
feature space.
  To counteract these problems, we propose an extension called hierarchical
information-preserving GSFA (HiGSFA), where information preservation
complements the slowness-maximization goal. We build a 10-layer HiGSFA network
to estimate human age from facial photographs of the MORPH-II database,
achieving a mean absolute error of 3.50 years, improving the state-of-the-art
performance. HiGSFA and HGSFA support multiple-labels and offer a rich feature
space, feed-forward training, and linear complexity in the number of samples
and dimensions. Furthermore, HiGSFA outperforms HGSFA in terms of feature
slowness, estimation accuracy and input reconstruction, giving rise to a
promising hierarchical supervised-learning approach.
A Relative Exponential Weighing Algorithm for Adversarial Utility-based Dueling Bandits
Authors: Pratik Gajane, Tanguy Urvoy, Fabrice Clérot
Date: 2016-01-15 00:00:00
Tasks: Information Retrieval, Retrieval

We study the K-armed dueling bandit problem which is a variation of the
classical Multi-Armed Bandit (MAB) problem in which the learner receives only
relative feedback about the selected pairs of arms. We propose a new algorithm
called Relative Exponential-weight algorithm for Exploration and Exploitation
(REX3) to handle the adversarial utility-based formulation of this problem.
This algorithm is a non-trivial extension of the Exponential-weight algorithm
for Exploration and Exploitation (EXP3) algorithm. We prove a finite time
expected regret upper bound of order O(sqrt(K ln(K)T)) for this algorithm and a
general lower bound of order omega(sqrt(KT)). At the end, we provide
experimental results using real data from information retrieval applications.
Invariant backpropagation: how to train a transformation-invariant neural network
Authors: Sergey Demyanov, James Bailey, Ramamohanarao Kotagiri, Christopher Leckie
Date: 2015-02-16 00:00:00
Tasks: Classification, Data Augmentation, General Classification, image-classification, Image Classification, Translation

In many classification problems a classifier should be robust to small
variations in the input vector. This is a desired property not only for
particular transformations, such as translation and rotation in image
classification problems, but also for all others for which the change is small
enough to retain the object perceptually indistinguishable. We propose two
extensions of the backpropagation algorithm that train a neural network to be
robust to variations in the feature vector. While the first of them enforces
robustness of the loss function to all variations, the second method trains the
predictions to be robust to a particular variation which changes the loss
function the most. The second methods demonstrates better results, but is
slightly slower. We analytically compare the proposed algorithm with two the
most similar approaches (Tangent BP and Adversarial Training), and propose
their fast versions. In the experimental part we perform comparison of all
algorithms in terms of classification accuracy and robustness to noise on MNIST
and CIFAR-10 datasets. Additionally we analyze how the performance of the
proposed algorithm depends on the dataset size and data augmentation.
ActiveClean: Interactive Data Cleaning While Learning Convex Loss Models
Authors: Sanjay Krishnan, Jiannan Wang, Eugene Wu, Michael J. Franklin, Ken Goldberg
Date: 2016-01-15 00:00:00
Tasks: Active Learning, EEG, Electroencephalogram (EEG), regression

Data cleaning is often an important step to ensure that predictive models,
such as regression and classification, are not affected by systematic errors
such as inconsistent, out-of-date, or outlier data. Identifying dirty data is
often a manual and iterative process, and can be challenging on large datasets.
However, many data cleaning workflows can introduce subtle biases into the
training processes due to violation of independence assumptions. We propose
ActiveClean, a progressive cleaning approach where the model is updated
incrementally instead of re-training and can guarantee accuracy on partially
cleaned data. ActiveClean supports a popular class of models called convex loss
models (e.g., linear regression and SVMs). ActiveClean also leverages the
structure of a user's model to prioritize cleaning those records likely to
affect the results. We evaluate ActiveClean on five real-world datasets UCI
Adult, UCI EEG, MNIST, Dollars For Docs, and WorldBank with both real and
synthetic errors. Our results suggest that our proposed optimizations can
improve model accuracy by up-to 2.5x for the same amount of data cleaned.
Furthermore for a fixed cleaning budget and on all real dirty datasets,
ActiveClean returns more accurate models than uniform sampling and Active
Learning.
A Method for Image Reduction Based on a Generalization of Ordered Weighted Averaging Functions
Authors: A. Diego S. Farias, Valdigleis S. Costa, Luiz Ranyer A. Lopes, Benjamín Bedregal, Regivan Santiago
Date: 2016-01-15 00:00:00

In this paper we propose a special type of aggregation function which
generalizes the notion of Ordered Weighted Averaging Function - OWA. The
resulting functions are called Dynamic Ordered Weighted Averaging Functions ---
DYOWAs. This generalization will be developed in such way that the weight
vectors are variables depending on the input vector. Particularly, this
operators generalize the aggregation functions: Minimum, Maximum, Arithmetic
Mean, Median, etc, which are extensively used in image processing. In this
field of research two problems are considered: The determination of methods to
reduce images and the construction of techniques which provide noise reduction.
The operators described here are able to be used in both cases. In terms of
image reduction we apply the methodology provided by Patermain et al. We use
the noise reduction operators obtained here to treat the images obtained in the
first part of the paper, thus obtaining images with better quality.
Towards Turkish ASR: Anatomy of a rule-based Turkish g2p
Authors: Duygu Altinok
Date: 2016-01-15 00:00:00
Tasks: Anatomy, Morphological Analysis

This paper describes the architecture and implementation of a rule-based
grapheme to phoneme converter for Turkish. The system accepts surface form as
input, outputs SAMPA mapping of the all parallel pronounciations according to
the morphological analysis together with stress positions. The system has been
implemented in Python
Categorization Axioms for Clustering Results
Authors: Jian Yu, Zongben Xu
Date: 2014-03-09 00:00:00
Tasks: Clustering, General Classification

Cluster analysis has attracted more and more attention in the field of
machine learning and data mining. Numerous clustering algorithms have been
proposed and are being developed due to diverse theories and various
requirements of emerging applications. Therefore, it is very worth establishing
an unified axiomatic framework for data clustering. In the literature, it is an
open problem and has been proved very challenging. In this paper, clustering
results are axiomatized by assuming that an proper clustering result should
satisfy categorization axioms. The proposed axioms not only introduce
classification of clustering results and inequalities of clustering results,
but also are consistent with prototype theory and exemplar theory of
categorization models in cognitive science. Moreover, the proposed axioms lead
to three principles of designing clustering algorithm and cluster validity
index, which follow many popular clustering algorithms and cluster validity
indices.
A Blind Adaptive CDMA Receiver Based on State Space Structures
Authors: Zaid Albataineh, Fathi M. Salem
Date: 2014-08-01 00:00:00

Code Division Multiple Access (CDMA) is a channel access method, based on
spread-spectrum technology, used by various radio technologies world-wide. In
general, CDMA is used as an access method in many mobile standards such as
CDMA2000 and WCDMA. We address the problem of blind multiuser equalization in
the wideband CDMA system, in the noisy multipath propagation environment.
Herein, we propose three new blind receiver schemes, which are based on state
space structures and Independent Component Analysis (ICA). These blind
state-space receivers (BSSR) do not require knowledge of the propagation
parameters or spreading code sequences of the users they primarily exploit the
natural assumption of statistical independence among the source signals. We
also develop three semi blind adaptive detectors by incorporating the new
adaptive methods into the standard RAKE receiver structure. Extensive
comparative case study, based on Bit error rate (BER) performance of these
methods, is carried out for different number of users, symbols per user, and
signal to noise ratio (SNR) in comparison with conventional detectors,
including the Blind Multiuser Detectors (BMUD) and Linear Minimum mean squared
error (LMMSE). The results show that the proposed methods outperform the other
detectors in estimating the symbol signals from the received mixed CDMA
signals. Moreover, the new blind detectors mitigate the multi access
interference (MAI) in CDMA.
Generation of a Supervised Classification Algorithm for Time-Series Variable Stars with an Application to the LINEAR Dataset
Authors: Kyle B Johnston, Hakeem M Oluseyi
Date: 2016-01-14 00:00:00
Tasks: Astronomy, General Classification, Retrieval, Time Series, Time Series Analysis

With the advent of digital astronomy, new benefits and new problems have been
presented to the modern day astronomer. While data can be captured in a more
efficient and accurate manor using digital means, the efficiency of data
retrieval has led to an overload of scientific data for processing and storage.
This paper will focus on the construction and application of a supervised
pattern classification algorithm for the identification of variable stars.
Given the reduction of a survey of stars into a standard feature space, the
problem of using prior patterns to identify new observed patterns can be
reduced to time tested classification methodologies and algorithms. Such
supervised methods, so called because the user trains the algorithms prior to
application using patterns with known classes or labels, provide a means to
probabilistically determine the estimated class type of new observations. This
paper will demonstrate the construction and application of a supervised
classification algorithm on variable star data. The classifier is applied to a
set of 192,744 LINEAR data points. Of the original samples, 34,451 unique stars
were classified with high confidence (high level of probability of being the
true class).
Dual-tree $k$-means with bounded iteration runtime
Authors: Ryan R. Curtin
Date: 2016-01-14 00:00:00
Tasks: Clustering

k-means is a widely used clustering algorithm, but for $k$ clusters and a
dataset size of $N$, each iteration of Lloyd's algorithm costs $O(kN)$ time.
Although there are existing techniques to accelerate single Lloyd iterations,
none of these are tailored to the case of large $k$, which is increasingly
common as dataset sizes grow. We propose a dual-tree algorithm that gives the
exact same results as standard $k$-means; when using cover trees, we use
adaptive analysis techniques to, under some assumptions, bound the
single-iteration runtime of the algorithm as $O(N + k log k)$. To our knowledge
these are the first sub-$O(kN)$ bounds for exact Lloyd iterations. We then show
that this theoretically favorable algorithm performs competitively in practice,
especially for large $N$ and $k$ in low dimensions. Further, the algorithm is
tree-independent, so any type of tree may be used.
Monitoring Potential Drug Interactions and Reactions via Network Analysis of Instagram User Timelines
Authors: Rion Brattig Correia, Lang Li, Luis M. Rocha
Date: 2015-10-05 00:00:00

Much recent research aims to identify evidence for Drug-Drug Interactions
(DDI) and Adverse Drug reactions (ADR) from the biomedical scientific
literature. In addition to this "Bibliome", the universe of social media
provides a very promising source of large-scale data that can help identify DDI
and ADR in ways that have not been hitherto possible. Given the large number of
users, analysis of social media data may be useful to identify under-reported,
population-level pathology associated with DDI, thus further contributing to
improvements in population health. Moreover, tapping into this data allows us
to infer drug interactions with natural products--including cannabis--which
constitute an array of DDI very poorly explored by biomedical research thus
far. Our goal is to determine the potential of Instagram for public health
monitoring and surveillance for DDI, ADR, and behavioral pathology at large.
Using drug, symptom, and natural product dictionaries for identification of the
various types of DDI and ADR evidence, we have collected ~7000 timelines. We
report on 1) the development of a monitoring tool to easily observe user-level
timelines associated with drug and symptom terms of interest, and 2)
population-level behavior via the analysis of co-occurrence networks computed
from user timelines at three different scales: monthly, weekly, and daily
occurrences. Analysis of these networks further reveals 3) drug and symptom
direct and indirect associations with greater support in user timelines, as
well as 4) clusters of symptoms and drugs revealed by the collective behavior
of the observed population. This demonstrates that Instagram contains much
drug- and pathology specific data for public health monitoring of DDI and ADR,
and that complex network analysis provides an important toolbox to extract
health-related associations and their support from large-scale social media
data.
Inference of hidden structures in complex physical systems by multi-scale clustering
Authors: Z. Nussinov, P. Ronhovde, Dandan Hu, S. Chakrabarty, M. Sahu, Bo Sun, N. A. Mauro, K. K. Sahu
Date: 2015-03-05 00:00:00
Tasks: Clustering, Combinatorial Optimization, Community Detection, Image Segmentation, Semantic Segmentation

We survey the application of a relatively new branch of statistical
physics--"community detection"-- to data mining. In particular, we focus on the
diagnosis of materials and automated image segmentation. Community detection
describes the quest of partitioning a complex system involving many elements
into optimally decoupled subsets or communities of such elements. We review a
multiresolution variant which is used to ascertain structures at different
spatial and temporal scales. Significant patterns are obtained by examining the
correlations between different independent solvers. Similar to other
combinatorial optimization problems in the NP complexity class, community
detection exhibits several phases. Typically, illuminating orders are revealed
by choosing parameters that lead to extremal information theory correlations.
Dynamic Concept Composition for Zero-Example Event Detection
Authors: Xiaojun Chang, Yi Yang, Guodong Long, Chengqi Zhang, Alexander G. Hauptmann
Date: 2016-01-14 00:00:00
Tasks: Event Detection, Zero-Shot Learning

In this paper, we focus on automatically detecting events in unconstrained
videos without the use of any visual training exemplars. In principle,
zero-shot learning makes it possible to train an event detection model based on
the assumption that events (e.g. \emph{birthday party}) can be described by
multiple mid-level semantic concepts (e.g. "blowing candle", "birthday cake").
Towards this goal, we first pre-train a bundle of concept classifiers using
data from other sources. Then we evaluate the semantic correlation of each
concept \wrt the event of interest and pick up the relevant concept
classifiers, which are applied on all test videos to get multiple prediction
score vectors. While most existing systems combine the predictions of the
concept classifiers with fixed weights, we propose to learn the optimal weights
of the concept classifiers for each testing video by exploring a set of online
available videos with free-form text descriptions of their content. To validate
the effectiveness of the proposed approach, we have conducted extensive
experiments on the latest TRECVID MEDTest 2014, MEDTest 2013 and CCV dataset.
The experimental results confirm the superiority of the proposed approach.