pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised feature learning with discriminative encoder
Authors: Gaurav Pandey, Ambedkar Dukkipati
Date: 2017-09-03 00:00:00

In recent years, deep discriminative models have achieved extraordinary
performance on supervised learning tasks, significantly outperforming their
generative counterparts. However, their success relies on the presence of a
large amount of labeled data. How can one use the same discriminative models
for learning useful features in the absence of labels? We address this question
in this paper, by jointly modeling the distribution of data and latent features
in a manner that explicitly assigns zero probability to unobserved data. Rather
than maximizing the marginal probability of observed data, we maximize the
joint probability of the data and the latent features using a two step EM-like
procedure. To prevent the model from overfitting to our initial selection of
latent features, we use adversarial regularization. Depending on the task, we
allow the latent features to be one-hot or real-valued vectors and define a
suitable prior on the features. For instance, one-hot features correspond to
class labels and are directly used for the unsupervised and semi-supervised
classification task, whereas real-valued feature vectors are fed as input to
simple classifiers for auxiliary supervised discrimination tasks. The proposed
model, which we dub discriminative encoder (or DisCoder), is flexible in the
type of latent features that it can capture. The proposed model achieves
state-of-the-art performance on several challenging tasks.
Difficulty-level Modeling of Ontology-based Factual Questions
Authors: Vinu E. V, P. Sreenivasa Kumar
Date: 2017-09-03 00:00:00

Semantics based knowledge representations such as ontologies are found to be
very useful in automatically generating meaningful factual questions.
Determining the difficulty level of these system generated questions is helpful
to effectively utilize them in various educational and professional
applications. The existing approaches for finding the difficulty level of
factual questions are very simple and are limited to a few basic principles. We
propose a new methodology for this problem by considering an educational theory
called Item Response Theory (IRT). In the IRT, knowledge proficiency of end
users (learners) are considered for assigning difficulty levels, because of the
assumptions that a given question is perceived differently by learners of
various proficiencies. We have done a detailed study on the features (factors)
of a question statement which could possibly determine its difficulty level for
three learner categories (experts, intermediates and beginners). We formulate
ontology based metrics for the same. We then train three logistic regression
models to predict the difficulty level corresponding to the three learner
categories.
A Semi-Supervised Approach to Detecting Stance in Tweets
Authors: Amita Misra, Brian Ecker, Theodore Handleman, Nicolas Hahn, Marilyn Walker
Date: 2017-09-03 00:00:00
Tasks: Stance Classification

Stance classification aims to identify, for a particular issue under
discussion, whether the speaker or author of a conversational turn has Pro
(Favor) or Con (Against) stance on the issue. Detecting stance in tweets is a
new task proposed for SemEval-2016 Task6, involving predicting stance for a
dataset of tweets on the topics of abortion, atheism, climate change, feminism
and Hillary Clinton. Given the small size of the dataset, our team created our
own topic-specific training corpus by developing a set of high precision
hashtags for each topic that were used to query the twitter API, with the aim
of developing a large training corpus without additional human labeling of
tweets for stance. The hashtags selected for each topic were predicted to be
stance-bearing on their own. Experimental results demonstrate good performance
for our features for opinion-target pairs based on generalizing dependency
features using sentiment lexicons.
Using Summarization to Discover Argument Facets in Online Ideological Dialog
Authors: Amita Misra, Pranav Anand, Jean E. Fox Tree, Marilyn Walker
Date: 2017-09-03 00:00:00
Tasks: Semantic Textual Similarity

More and more of the information available on the web is dialogic, and a
significant portion of it takes place in online forum conversations about
current social and political topics. We aim to develop tools to summarize what
these conversations are about. What are the CENTRAL PROPOSITIONS associated
with different stances on an issue, what are the abstract objects under
discussion that are central to a speaker's argument? How can we recognize that
two CENTRAL PROPOSITIONS realize the same FACET of the argument? We hypothesize
that the CENTRAL PROPOSITIONS are exactly those arguments that people find most
salient, and use human summarization as a probe for discovering them. We
describe our corpus of human summaries of opinionated dialogs, then show how we
can identify similar repeated arguments, and group them into FACETS across many
discussions of a topic. We define a new task, ARGUMENT FACET SIMILARITY (AFS),
and show that we can predict AFS with a .54 correlation score, versus an ngram
system baseline of .39 and a semantic textual similarity system baseline of
.45.
Topic Independent Identification of Agreement and Disagreement in Social Media Dialogue
Authors: Amita Misra, Marilyn Walker
Date: 2017-09-03 00:00:00
Proceeding: WS 2013 8

Research on the structure of dialogue has been hampered for years because
large dialogue corpora have not been available. This has impacted the dialogue
research community's ability to develop better theories, as well as good off
the shelf tools for dialogue processing. Happily, an increasing amount of
information and opinion exchange occur in natural dialogue in online forums,
where people share their opinions about a vast range of topics. In particular
we are interested in rejection in dialogue, also called disagreement and
denial, where the size of available dialogue corpora, for the first time,
offers an opportunity to empirically test theoretical accounts of the
expression and inference of rejection in dialogue. In this paper, we test
whether topic-independent features motivated by theoretical predictions can be
used to recognize rejection in online forums in a topic independent way. Our
results show that our theoretically motivated features achieve 66% accuracy, an
improvement over a unigram baseline of an absolute 6%.
Detection of Moving Object in Dynamic Background Using Gaussian Max-Pooling and Segmentation Constrained RPCA
Authors: Yang Li, Guangcan Liu, Sheng-Yong Chen
Date: 2017-09-03 00:00:00
Tasks: Moving Object Detection, object-detection, Object Detection

Due to its efficiency and stability, Robust Principal Component Analysis
(RPCA) has been emerging as a promising tool for moving object detection.
Unfortunately, existing RPCA based methods assume static or quasi-static
background, and thereby they may have trouble in coping with the background
scenes that exhibit a persistent dynamic behavior. In this work, we shall
introduce two techniques to fill in the gap. First, instead of using the raw
pixel-value as features that are brittle in the presence of dynamic background,
we devise a so-called Gaussian max-pooling operator to estimate a
"stable-value" for each pixel. Those stable-values are robust to various
background changes and can therefore distinguish effectively the foreground
objects from the background. Then, to obtain more accurate results, we further
propose a Segmentation Constrained RPCA (SC-RPCA) model, which incorporates the
temporal and spatial continuity in images into RPCA. The inference process of
SC-RPCA is a group sparsity constrained nuclear norm minimization problem,
which is convex and easy to solve. Experimental results on seven videos from
the CDCNET 2014 database show the superior performance of the proposed method.
Fake News Detection on Social Media: A Data Mining Perspective
Authors: Kai Shu, Amy Sliva, Suhang Wang, Jiliang Tang, Huan Liu
Date: 2017-08-07 00:00:00
Tasks: Fake News Detection

Social media for news consumption is a double-edged sword. On the one hand,
its low cost, easy access, and rapid dissemination of information lead people
to seek out and consume news from social media. On the other hand, it enables
the wide spread of "fake news", i.e., low quality news with intentionally false
information. The extensive spread of fake news has the potential for extremely
negative impacts on individuals and society. Therefore, fake news detection on
social media has recently become an emerging research that is attracting
tremendous attention. Fake news detection on social media presents unique
characteristics and challenges that make existing detection algorithms from
traditional news media ineffective or not applicable. First, fake news is
intentionally written to mislead readers to believe false information, which
makes it difficult and nontrivial to detect based on news content; therefore,
we need to include auxiliary information, such as user social engagements on
social media, to help make a determination. Second, exploiting this auxiliary
information is challenging in and of itself as users' social engagements with
fake news produce data that is big, incomplete, unstructured, and noisy.
Because the issue of fake news detection on social media is both challenging
and relevant, we conducted this survey to further facilitate research on the
problem. In this survey, we present a comprehensive review of detecting fake
news on social media, including fake news characterizations on psychology and
social theories, existing algorithms from a data mining perspective, evaluation
metrics and representative datasets. We also discuss related research areas,
open problems, and future research directions for fake news detection on social
media.
From Query-By-Keyword to Query-By-Example: LinkedIn Talent Search Approach
Authors: Viet Ha-Thuc, Yan Yan, Xianren Wu, Vijay Dialani, Abhishek Gupta, Shakti Sinha
Date: 2017-09-03 00:00:00
Tasks: Position

One key challenge in talent search is to translate complex criteria of a
hiring position into a search query, while it is relatively easy for a searcher
to list examples of suitable candidates for a given position. To improve search
efficiency, we propose the next generation of talent search at LinkedIn, also
referred to as Search By Ideal Candidates. In this system, a searcher provides
one or several ideal candidates as the input to hire for a given position. The
system then generates a query based on the ideal candidates and uses it to
retrieve and rank results. Shifting from the traditional Query-By-Keyword to
this new Query-By-Example system poses a number of challenges: How to generate
a query that best describes the candidates? When moving to a completely
different paradigm, how does one leverage previous product logs to learn
ranking models and/or evaluate the new system with no existing usage logs?
Finally, given the different nature between the two search paradigms, the
ranking features typically used for Query-By-Keyword systems might not be
optimal for Query-By-Example. This paper describes our approach to solving
these challenges. We present experimental results confirming the effectiveness
of the proposed solution, particularly on query building and search ranking
tasks. As of writing this paper, the new system has been available to all
LinkedIn members.
Simulated Annealing for JPEG Quantization
Authors: Max Hopkins, Michael Mitzenmacher, Sebastian Wagner-Carena
Date: 2017-09-03 00:00:00
Tasks: Quantization

JPEG is one of the most widely used image formats, but in some ways remains
surprisingly unoptimized, perhaps because some natural optimizations would go
outside the standard that defines JPEG. We show how to improve JPEG compression
in a standard-compliant, backward-compatible manner, by finding improved
default quantization tables. We describe a simulated annealing technique that
has allowed us to find several quantization tables that perform better than the
industry standard, in terms of both compressed size and image fidelity.
Specifically, we derive tables that reduce the FSIM error by over 10% while
improving compression by over 20% at quality level 95 in our tests; we also
provide similar results for other quality levels. While we acknowledge our
approach can in some images lead to visible artifacts under large
magnification, we believe use of these quantization tables, or additional
tables that could be found using our methodology, would significantly reduce
JPEG file sizes with improved overall image quality.
Fast Image Processing with Fully-Convolutional Networks
Authors: Qifeng Chen, Jia Xu, Vladlen Koltun
Date: 2017-09-02 00:00:00
Proceeding: ICCV 2017 10
Tasks: Style Transfer

We present an approach to accelerating a wide variety of image processing
operators. Our approach uses a fully-convolutional network that is trained on
input-output pairs that demonstrate the operator's action. After training, the
original operator need not be run at all. The trained network operates at full
resolution and runs in constant time. We investigate the effect of network
architecture on approximation accuracy, runtime, and memory footprint, and
identify a specific architecture that balances these considerations. We
evaluate the presented approach on ten advanced image processing operators,
including multiple variational models, multiscale tone and detail manipulation,
photographic style transfer, nonlocal dehazing, and nonphotorealistic
stylization. All operators are approximated by the same model. Experiments
demonstrate that the presented approach is significantly more accurate than
prior approximation schemes. It increases approximation accuracy as measured by
PSNR across the evaluated operators by 8.5 dB on the MIT-Adobe dataset (from
27.5 to 36 dB) and reduces DSSIM by a multiplicative factor of 3 compared to
the most accurate prior approximation scheme, while being the fastest. We show
that our models generalize across datasets and across resolutions, and
investigate a number of extensions of the presented approach. The results are
shown in the supplementary video at https://youtu.be/eQyfHgLx8Dc
When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, $\ell_2$-consistency and Neuroscience Applications
Authors: Hao Henry Zhou, Yilin Zhang, Vamsi K. Ithapu, Sterling C. Johnson, Grace Wahba, Vikas Singh
Date: 2017-09-02 00:00:00
Proceeding: ICML 2017 8

Many studies in biomedical and health sciences involve small sample sizes due
to logistic or financial constraints. Often, identifying weak (but
scientifically interesting) associations between a set of predictors and a
response necessitates pooling datasets from multiple diverse labs or groups.
While there is a rich literature in statistical machine learning to address
distributional shifts and inference in multi-site datasets, it is less clear
${\it when}$ such pooling is guaranteed to help (and when it does not) --
independent of the inference algorithms we use. In this paper, we present a
hypothesis test to answer this question, both for classical and high
dimensional linear regression. We precisely identify regimes where pooling
datasets across multiple sites is sensible, and how such policy decisions can
be made via simple checks executable on each site before any data transfer ever
happens. With a focus on Alzheimer's disease studies, we present empirical
results showing that in regimes suggested by our analysis, pooling a local
dataset with data from an international study improves power.
Zero-Shot Activity Recognition with Verb Attribute Induction
Authors: Rowan Zellers, Yejin Choi
Date: 2017-07-29 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Activity Recognition, Attribute

In this paper, we investigate large-scale zero-shot activity recognition by
modeling the visual and linguistic attributes of action verbs. For example, the
verb "salute" has several properties, such as being a light movement, a social
act, and short in duration. We use these attributes as the internal mapping
between visual and textual representations to reason about a previously unseen
action. In contrast to much prior work that assumes access to gold standard
attributes for zero-shot classes and focuses primarily on object attributes,
our model uniquely learns to infer action attributes from dictionary
definitions and distributed word representations. Experimental results confirm
that action attributes inferred from language can provide a predictive signal
for zero-shot prediction of previously unseen activities.
Challenging Language-Dependent Segmentation for Arabic: An Application to Machine Translation and Part-of-Speech Tagging
Authors: Hassan Sajjad, Fahim Dalvi, Nadir Durrani, Ahmed Abdelali, Yonatan Belinkov, Stephan Vogel
Date: 2017-09-02 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, Part-Of-Speech Tagging, POS, POS Tagging, Segmentation, Translation, Word Embeddings

Word segmentation plays a pivotal role in improving any Arabic NLP
application. Therefore, a lot of research has been spent in improving its
accuracy. Off-the-shelf tools, however, are: i) complicated to use and ii)
domain/dialect dependent. We explore three language-independent alternatives to
morphological segmentation using: i) data-driven sub-word units, ii) characters
as a unit of learning, and iii) word embeddings learned using a character CNN
(Convolution Neural Network). On the tasks of Machine Translation and POS
tagging, we found these methods to achieve close to, and occasionally surpass
state-of-the-art performance. In our analysis, we show that a neural machine
translation system is sensitive to the ratio of source and target tokens, and a
ratio close to 1 or greater, gives optimal performance.
On Identifiability of Nonnegative Matrix Factorization
Authors: Xiao Fu, Kejun Huang, Nicholas D. Sidiropoulos
Date: 2017-09-02 00:00:00

In this letter, we propose a new identification criterion that guarantees the
recovery of the low-rank latent factors in the nonnegative matrix factorization
(NMF) model, under mild conditions. Specifically, using the proposed criterion,
it suffices to identify the latent factors if the rows of one factor are
\emph{sufficiently scattered} over the nonnegative orthant, while no structural
assumption is imposed on the other factor except being full-rank. This is by
far the mildest condition under which the latent factors are provably
identifiable from the NMF model.
FOCA: A Methodology for Ontology Evaluation
Authors: Judson Bandeira, Ig Ibert Bittencourt, Patricia Espinheira, Seiji Isotani
Date: 2016-12-10 00:00:00

Modeling an ontology is a hard and time-consuming task. Although
methodologies are useful for ontologists to create good ontologies, they do not
help with the task of evaluating the quality of the ontology to be reused. For
these reasons, it is imperative to evaluate the quality of the ontology after
constructing it or before reusing it. Few studies usually present only a set of
criteria and questions, but no guidelines to evaluate the ontology. The effort
to evaluate an ontology is very high as there is a huge dependence on the
evaluator's expertise to understand the criteria and questions in depth.
Moreover, the evaluation is still very subjective. This study presents a novel
methodology for ontology evaluation, taking into account three fundamental
principles: i) it is based on the Goal, Question, Metric approach for empirical
evaluation; ii) the goals of the methodologies are based on the roles of
knowledge representations combined with specific evaluation criteria; iii) each
ontology is evaluated according to the type of ontology. The methodology was
empirically evaluated using different ontologists and ontologies of the same
domain. The main contributions of this study are: i) defining a step-by-step
approach to evaluate the quality of an ontology; ii) proposing an evaluation
based on the roles of knowledge representations; iii) the explicit difference
of the evaluation according to the type of the ontology iii) a questionnaire to
evaluate the ontologies; iv) a statistical model that automatically calculates
the quality of the ontologies.
Artificial life, complex systems and cloud computing: a short review
Authors: Juan-Julián Merelo-Guervós
Date: 2017-09-02 00:00:00
Tasks: Artificial Life, Cloud Computing, Distributed Computing

Cloud computing is the prevailing mode of designing, creating and deploying
complex applications nowadays. Its underlying assumptions include distributed
computing, but also new concepts that need to be incorporated in the different
fields. In this short paper we will make a review of how the world of cloud
computing has intersected the complex systems and artificial life field, and
how it has been used as inspiration for new models or implementation of new and
powerful algorithms
Learning Multi-level Features For Sensor-based Human Action Recognition
Authors: Yan Xu, Zhengyang Shen, Xin Zhang, Yifan Gao, Shujian Deng, Yipei Wang, Yubo Fan, Eric I-Chao Chang
Date: 2016-11-22 00:00:00
Tasks: Action Recognition, Temporal Action Localization

This paper proposes a multi-level feature learning framework for human action
recognition using a single body-worn inertial sensor. The framework consists of
three phases, respectively designed to analyze signal-based (low-level),
components (mid-level) and semantic (high-level) information. Low-level
features capture the time and frequency domain property while mid-level
representations learn the composition of the action. The Max-margin Latent
Pattern Learning (MLPL) method is proposed to learn high-level semantic
descriptions of latent action patterns as the output of our framework. The
proposed method achieves the state-of-the-art performances, 88.7%, 98.8% and
72.6% (weighted F1 score) respectively, on Skoda, WISDM and OPP datasets.
Security Evaluation of Pattern Classifiers under Attack
Authors: Battista Biggio, Giorgio Fumera, Fabio Roli
Date: 2017-09-02 00:00:00
Tasks: Classification, General Classification, Intrusion Detection, Network Intrusion Detection

Pattern classification systems are commonly used in adversarial applications,
like biometric authentication, network intrusion detection, and spam filtering,
in which data can be purposely manipulated by humans to undermine their
operation. As this adversarial scenario is not taken into account by classical
design methods, pattern classification systems may exhibit vulnerabilities,
whose exploitation may severely affect their performance, and consequently
limit their practical utility. Extending pattern classification theory and
design methods to adversarial settings is thus a novel and very relevant
research direction, which has not yet been pursued in a systematic way. In this
paper, we address one of the main open issues: evaluating at design phase the
security of pattern classifiers, namely, the performance degradation under
potential attacks they may incur during operation. We propose a framework for
empirical evaluation of classifier security that formalizes and generalizes the
main ideas proposed in the literature, and give examples of its use in three
real applications. Reported results show that security evaluation can provide a
more complete understanding of the classifier's behavior in adversarial
environments, and lead to better design choices.