pwc-archive/papers-with-abstracts huggingface dataset:
Low-Rank Matrix Recovery from Row-and-Column Affine Measurements
Authors: Avishai Wagner, Or Zuk
Date: 2015-05-23 00:00:00

We propose and study a row-and-column affine measurement scheme for low-rank
matrix recovery. Each measurement is a linear combination of elements in one
row or one column of a matrix $X$. This setting arises naturally in
applications from different domains. However, current algorithms developed for
standard matrix recovery problems do not perform well in our case, hence the
need for developing new algorithms and theory for our problem. We propose a
simple algorithm for the problem based on Singular Value Decomposition ($SVD$)
and least-squares ($LS$), which we term \alg. We prove that (a simplified
version of) our algorithm can recover $X$ exactly with the minimum possible
number of measurements in the noiseless case. In the general noisy case, we
prove performance guarantees on the reconstruction accuracy under the Frobenius
norm. In simulations, our row-and-column design and \alg algorithm show
improved speed, and comparable and in some cases better accuracy compared to
standard measurements designs and algorithms. Our theoretical and experimental
results suggest that the proposed row-and-column affine measurements scheme,
together with our recovery algorithm, may provide a powerful framework for
affine matrix reconstruction.
Exposing ambiguities in a relation-extraction gold standard with crowdsourcing
Authors: Tong Shu Li, Benjamin M. Good, Andrew I. Su
Date: 2015-05-23 00:00:00
Tasks: Relation, Relation Extraction

Semantic relation extraction is one of the frontiers of biomedical natural
language processing research. Gold standards are key tools for advancing this
research. It is challenging to generate these standards because of the high
cost of expert time and the difficulty in establishing agreement between
annotators. We implemented and evaluated a microtask crowdsourcing approach
that can produce a gold standard for extracting drug-disease relations. The
aggregated crowd judgment agreed with expert annotations from a pre-existing
corpus on 43 of 60 sentences tested. The levels of crowd agreement varied in a
similar manner to the levels of agreement among the original expert annotators.
This work rein-forces the power of crowdsourcing in the process of assembling
gold standards for relation extraction. Further, it high-lights the importance
of exposing the levels of agreement between human annotators, expert or crowd,
in gold standard corpora as these are reproducible signals indicating
ambiguities in the data or in the annotation guidelines.
Efficient Large Scale Video Classification
Authors: Balakrishnan Varadarajan, George Toderici, Sudheendra Vijayanarasimhan, Apostol Natsev
Date: 2015-05-22 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Mixture-of-Experts, Video Classification

Video classification has advanced tremendously over the recent years. A large
part of the improvements in video classification had to do with the work done
by the image classification community and the use of deep convolutional
networks (CNNs) which produce competitive results with hand- crafted motion
features. These networks were adapted to use video frames in various ways and
have yielded state of the art classification results. We present two methods
that build on this work, and scale it up to work with millions of videos and
hundreds of thousands of classes while maintaining a low computational cost. In
the context of large scale video processing, training CNNs on video frames is
extremely time consuming, due to the large number of frames involved. We
propose to avoid this problem by training CNNs on either YouTube thumbnails or
Flickr images, and then using these networks' outputs as features for other
higher level classifiers. We discuss the challenges of achieving this and
propose two models for frame-level and video-level classification. The first is
a highly efficient mixture of experts while the latter is based on long short
term memory neural networks. We present results on the Sports-1M video dataset
(1 million videos, 487 classes) and on a new dataset which has 12 million
videos and 150,000 labels.
Greedy Biomarker Discovery in the Genome with Applications to Antimicrobial Resistance
Authors: Alexandre Drouin, Sébastien Giguère, Maxime Déraspe, François Laviolette, Mario Marchand, Jacques Corbeil
Date: 2015-05-22 00:00:00

The Set Covering Machine (SCM) is a greedy learning algorithm that produces
sparse classifiers. We extend the SCM for datasets that contain a huge number
of features. The whole genetic material of living organisms is an example of
such a case, where the number of feature exceeds 10^7. Three human pathogens
were used to evaluate the performance of the SCM at predicting antimicrobial
resistance. Our results show that the SCM compares favorably in terms of
sparsity and accuracy against L1 and L2 regularized Support Vector Machines and
CART decision trees. Moreover, the SCM was the only algorithm that could
consider the full feature space. For all other algorithms, the latter had to be
filtered as a preprocessing step.
Tunnel Surface 3D Reconstruction from Unoriented Image Sequences
Authors: Arnold Bauer, Karlheinz Gutjahr, Gerhard Paar, Heiner Kontrus, Robert Glatzl
Date: 2015-05-22 00:00:00
Tasks: 3D Reconstruction

The 3D documentation of the tunnel surface during construction requires fast
and robust measurement systems. In the solution proposed in this paper, during
tunnel advance a single camera is taking pictures of the tunnel surface from
several positions. The recorded images are automatically processed to gain a 3D
tunnel surface model. Image acquisition is realized by the
tunneling/advance/driving personnel close to the tunnel face (= the front end
of the advance). Based on the following fully automatic analysis/evaluation, a
decision on the quality of the outbreak can be made within a few minutes. This
paper describes the image recording system and conditions as well as the
stereo-photogrammetry based workflow for the continuously merged dense 3D
reconstruction of the entire advance region. Geo-reference is realized by means
of signalized targets that are automatically detected in the images. We report
on the results of recent testing under real construction conditions, and
conclude with prospects for further development in terms of on-site
performance.
Keyphrase Based Evaluation of Automatic Text Summarization
Authors: Fatma El-Ghannam, Tarek El-Shishtawy
Date: 2015-05-22 00:00:00
Tasks: LEMMA, Text Summarization

The development of methods to deal with the informative contents of the text
units in the matching process is a major challenge in automatic summary
evaluation systems that use fixed n-gram matching. The limitation causes
inaccurate matching between units in a peer and reference summaries. The
present study introduces a new Keyphrase based Summary Evaluator KpEval for
evaluating automatic summaries. The KpEval relies on the keyphrases since they
convey the most important concepts of a text. In the evaluation process, the
keyphrases are used in their lemma form as the matching text unit. The system
was applied to evaluate different summaries of Arabic multi-document data set
presented at TAC2011. The results showed that the new evaluation technique
correlates well with the known evaluation systems: Rouge1, Rouge2, RougeSU4,
and AutoSummENG MeMoG. KpEval has the strongest correlation with AutoSummENG
MeMoG, Pearson and spearman correlation coefficient measures are 0.8840, 0.9667
respectively.
Learning Dynamic Feature Selection for Fast Sequential Prediction
Authors: Emma Strubell, Luke Vilnis, Kate Silverstein, Andrew McCallum
Date: 2015-05-22 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Benchmarking, Dependency Parsing, feature selection, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, parameter estimation, Part-Of-Speech Tagging, POS, POS Tagging, Prediction, Transition-Based Dependency Parsing

We present paired learning and inference algorithms for significantly
reducing computation and increasing speed of the vector dot products in the
classifiers that are at the heart of many NLP components. This is accomplished
by partitioning the features into a sequence of templates which are ordered
such that high confidence can often be reached using only a small fraction of
all features. Parameter estimation is arranged to maximize accuracy and early
confidence in this sequence. Our approach is simpler and better suited to NLP
than other related cascade methods. We present experiments in left-to-right
part-of-speech tagging, named entity recognition, and transition-based
dependency parsing. On the typical benchmarking datasets we can preserve POS
tagging accuracy above 97% and parsing LAS above 88.5% both with over a
five-fold reduction in run-time, and NER F1 above 88 with more than 2x increase
in speed.
Design and Implementation of Real-time Algorithms for Eye Tracking and PERCLOS Measurement for on board Estimation of Alertness of Drivers
Authors: Anjith George, Aurobinda Routray
Date: 2015-05-22 00:00:00

The alertness level of drivers can be estimated with the use of computer
vision based methods. The level of fatigue can be found from the value of
PERCLOS. It is the ratio of closed eye frames to the total frames processed.
The main objective of the thesis is the design and implementation of real-time
algorithms for measurement of PERCLOS. In this work we have developed a
real-time system which is able to process the video onboard and to alarm the
driver in case the driver is in alert. For accurate estimation of PERCLOS the
frame rate should be greater than 4 and accuracy should be greater than 90%.
For eye detection we have used mainly two approaches Haar classifier based
method and Principal Component Analysis (PCA) based method for day time. During
night time active Near Infra Red (NIR) illumination is used. Local Binary
Pattern (LBP) histogram based method is used for the detection of eyes at night
time. The accuracy rate of the algorithms was found to be more than 90% at
frame rates more than 5 fps which was suitable for the application.
Probabilistic Belief Embedding for Knowledge Base Completion
Authors: Miao Fan, Qiang Zhou, Andrew Abel, Thomas Fang Zheng, Ralph Grishman
Date: 2015-05-10 00:00:00
Tasks: Knowledge Base Completion, Relation, Relation Prediction, Triplet

This paper contributes a novel embedding model which measures the probability
of each belief $\langle h,r,t,m\rangle$ in a large-scale knowledge repository
via simultaneously learning distributed representations for entities ($h$ and
$t$), relations ($r$), and the words in relation mentions ($m$). It facilitates
knowledge completion by means of simple vector operations to discover new
beliefs. Given an imperfect belief, we can not only infer the missing entities,
predict the unknown relations, but also tell the plausibility of the belief,
just leveraging the learnt embeddings of remaining evidences. To demonstrate
the scalability and the effectiveness of our model, we conduct experiments on
several large-scale repositories which contain millions of beliefs from
WordNet, Freebase and NELL, and compare it with other cutting-edge approaches
via competing the performances assessed by the tasks of entity inference,
relation prediction and triplet classification with respective metrics.
Extensive experimental results show that the proposed model outperforms the
state-of-the-arts with significant improvements.
Correlation Clustering with Constrained Cluster Sizes and Extended Weights Bounds
Authors: Gregory J. Puleo, Olgica Milenkovic
Date: 2014-11-03 00:00:00
Tasks: Clustering

We consider the problem of correlation clustering on graphs with constraints
on both the cluster sizes and the positive and negative weights of edges. Our
contributions are twofold: First, we introduce the problem of correlation
clustering with bounded cluster sizes. Second, we extend the regime of weight
values for which the clustering may be performed with constant approximation
guarantees in polynomial time and apply the results to the bounded cluster size
problem.
Statistical Estimation and Clustering of Group-invariant Orientation Parameters
Authors: Yu-Hui Chen, Dennis Wei, Gregory Newstadt, Marc DeGraef, Jeffrey Simmons, Alfred Hero
Date: 2015-03-15 00:00:00
Tasks: Clustering

We treat the problem of estimation of orientation parameters whose values are
invariant to transformations from a spherical symmetry group. Previous work has
shown that any such group-invariant distribution must satisfy a restricted
finite mixture representation, which allows the orientation parameter to be
estimated using an Expectation Maximization (EM) maximum likelihood (ML)
estimation algorithm. In this paper, we introduce two parametric models for
this spherical symmetry group estimation problem: 1) the hyperbolic Von Mises
Fisher (VMF) mixture distribution and 2) the Watson mixture distribution. We
also introduce a new EM-ML algorithm for clustering samples that come from
mixtures of group-invariant distributions with different parameters. We apply
the models to the problem of mean crystal orientation estimation under the
spherically symmetric group associated with the crystal form, e.g., cubic or
octahedral or hexahedral. Simulations and experiments establish the advantages
of the extended EM-VMF and EM-Watson estimators for data acquired by Electron
Backscatter Diffraction (EBSD) microscopy of a polycrystalline Nickel alloy
sample.
Machine Learning for Indoor Localization Using Mobile Phone-Based Sensors
Authors: David Mascharka, Eric Manley
Date: 2015-05-22 00:00:00
Tasks: BIG-bench Machine Learning, Indoor Localization

In this paper we investigate the problem of localizing a mobile device based
on readings from its embedded sensors utilizing machine learning methodologies.
We consider a real-world environment, collect a large dataset of 3110
datapoints, and examine the performance of a substantial number of machine
learning algorithms in localizing a mobile device. We have found algorithms
that give a mean error as accurate as 0.76 meters, outperforming other indoor
localization systems reported in the literature. We also propose a hybrid
instance-based approach that results in a speed increase by a factor of ten
with no loss of accuracy in a live deployment over standard instance-based
methods, allowing for fast and accurate localization. Further, we determine how
smaller datasets collected with less density affect accuracy of localization,
important for use in real-world environments. Finally, we demonstrate that
these approaches are appropriate for real-world deployment by evaluating their
performance in an online, in-motion experiment.
A Mixture of Generalized Hyperbolic Factor Analyzers
Authors: Cristina Tortora, Paul D. McNicholas, Ryan P. Browne
Date: 2013-11-26 00:00:00
Tasks: Clustering

Model-based clustering imposes a finite mixture modelling structure on data
for clustering. Finite mixture models assume that the population is a convex
combination of a finite number of densities, the distribution within each
population is a basic assumption of each particular model. Among all
distributions that have been tried, the generalized hyperbolic distribution has
the advantage that is a generalization of several other methods, such as the
Gaussian distribution, the skew t-distribution, etc. With specific parameters,
it can represent either a symmetric or a skewed distribution. While its
inherent flexibility is an advantage in many ways, it means the estimation of
more parameters than its special and limiting cases. The aim of this work is to
propose a mixture of generalized hyperbolic factor analyzers to introduce
parsimony and extend the method to high dimensional data. This work can be seen
as an extension of the mixture of factor analyzers model to generalized
hyperbolic mixtures. The performance of our generalized hyperbolic factor
analyzers is illustrated on real data, where it performs favourably compared to
its Gaussian analogue.
A survey of SMS based Information Systems
Authors: Manish R. Joshi, Varsha M. Pathak
Date: 2015-05-22 00:00:00
Tasks: Image Retrieval, Information Retrieval, Management, Question Answering, Retrieval, Survey

Short Message Service (SMS) based Information Systems (SMSbIS) provide an
excellent alternative to a traditional approach of obtaining specific
information by direct (through phone) or indirect (IVRS, Web, Email) probing.
Information and communication technology and far reaching mobile penetration
has opened this new research trend Number of key players in Search industry
including Microsoft and Google are attracted by the expected increase in volume
of use of such applications. The wide range of applications and their public
acceptance has motivated researchers to work in this research domain. Several
applications such as SMS based information access using database management
services, SMS based information retrieval through internet (search engine), SMS
based information extraction, question answering, image retrieval etc. have
been emerged. With the aim to understand the functionality involved in these
systems, an extensive review of a few of these SMSbISs has been planned and
executed by us. These systems are classified into four categories based on the
objectives and domains of the applications. As a result of this study a well
structured functional model is presented here. The model is evaluated in
different dimensions, which is presented in this paper. In addition to this a
chronological progress with respect to research and development in this
upcoming field is compiled in this paper. Such an extensive review presented in
this paper would definitely help the researchers and developers to understand
the technical aspects of this field. The functional framework presented here
would be useful to the system designers to design and develop an SMS based
Information System of any specific domain.
GPTIPS 2: an open-source software platform for symbolic data mining
Authors: Dominic P. Searson
Date: 2014-12-15 00:00:00
Tasks: Model Discovery, Symbolic Regression

GPTIPS is a free, open source MATLAB based software platform for symbolic
data mining (SDM). It uses a multigene variant of the biologically inspired
machine learning method of genetic programming (MGGP) as the engine that drives
the automatic model discovery process. Symbolic data mining is the process of
extracting hidden, meaningful relationships from data in the form of symbolic
equations. In contrast to other data-mining methods, the structural
transparency of the generated predictive equations can give new insights into
the physical systems or processes that generated the data. Furthermore, this
transparency makes the models very easy to deploy outside of MATLAB. The
rationale behind GPTIPS is to reduce the technical barriers to using,
understanding, visualising and deploying GP based symbolic models of data,
whilst at the same time remaining highly customisable and delivering robust
numerical performance for power users. In this chapter, notable new features of
the latest version of the software are discussed with these aims in mind.
Additionally, a simplified variant of the MGGP high level gene crossover
mechanism is proposed. It is demonstrated that the new functionality of GPTIPS
2 (a) facilitates the discovery of compact symbolic relationships from data
using multiple approaches, e.g. using novel gene-centric visualisation analysis
to mitigate horizontal bloat and reduce complexity in multigene symbolic
regression models (b) provides numerous methods for visualising the properties
of symbolic models (c) emphasises the generation of graphically navigable
libraries of models that are optimal in terms of the Pareto trade off surface
of model performance and complexity and (d) expedites real world applications
by the simple, rapid and robust deployment of symbolic models outside the
software environment they were developed in.
Learning Program Embeddings to Propagate Feedback on Student Code
Authors: Chris Piech, Jonathan Huang, Andy Nguyen, Mike Phulsuksombati, Mehran Sahami, Leonidas Guibas
Date: 2015-05-22 00:00:00

Providing feedback, both assessing final work and giving hints to stuck
students, is difficult for open-ended assignments in massive online classes
which can range from thousands to millions of students. We introduce a neural
network method to encode programs as a linear mapping from an embedded
precondition space to an embedded postcondition space and propose an algorithm
for feedback at scale using these linear maps as features. We apply our
algorithm to assessments from the Code.org Hour of Code and Stanford
University's CS1 course, where we propagate human comments on student
assignments to orders of magnitude more submissions.
Distributed Gaussian Processes
Authors: Marc Peter Deisenroth, Jun Wei Ng
Date: 2015-02-10 00:00:00
Tasks: Form, Gaussian Processes, regression

To scale Gaussian processes (GPs) to large data sets we introduce the robust
Bayesian Committee Machine (rBCM), a practical and scalable product-of-experts
model for large-scale distributed GP regression. Unlike state-of-the-art sparse
GP approximations, the rBCM is conceptually simple and does not rely on
inducing or variational parameters. The key idea is to recursively distribute
computations to independent computational units and, subsequently, recombine
them to form an overall result. Efficient closed-form inference allows for
straightforward parallelisation and distributed computations with a small
memory footprint. The rBCM is independent of the computational graph and can be
used on heterogeneous computing infrastructures, ranging from laptops to
clusters. With sufficient computing resources our distributed GP model can
handle arbitrarily large data sets.
Joint Inference of Groups, Events and Human Roles in Aerial Videos
Authors: Tianmin Shu, Dan Xie, Brandon Rothrock, Sinisa Todorovic, Song-Chun Zhu
Date: 2015-05-22 00:00:00
Proceeding: CVPR 2015 6

With the advent of drones, aerial video analysis becomes increasingly
important; yet, it has received scant attention in the literature. This paper
addresses a new problem of parsing low-resolution aerial videos of large
spatial areas, in terms of 1) grouping, 2) recognizing events and 3) assigning
roles to people engaged in events. We propose a novel framework aimed at
conducting joint inference of the above tasks, as reasoning about each in
isolation typically fails in our setting. Given noisy tracklets of people and
detections of large objects and scene surfaces (e.g., building, grass), we use
a spatiotemporal AND-OR graph to drive our joint inference, using Markov Chain
Monte Carlo and dynamic programming. We also introduce a new formalism of
spatiotemporal templates characterizing latent sub-events. For evaluation, we
have collected and released a new aerial videos dataset using a hex-rotor
flying over picnic areas rich with group events. Our results demonstrate that
we successfully address above inference tasks under challenging conditions.