pwc-archive/papers-with-abstracts huggingface dataset:
Determining Points on Handwritten Mathematical Symbols
Authors: Rui Hu, Stephen M. Watt
Date: 2013-06-20 00:00:00

In a variety of applications, such as handwritten mathematics and diagram
labelling, it is common to have symbols of many different sizes in use and for
the writing not to follow simple baselines. In order to understand the scale
and relative positioning of individual characters, it is necessary to identify
the location of certain expected features. These are typically identified by
particular points in the symbols, for example, the baseline of a lower case "p"
would be identified by the lowest part of the bowl, ignoring the descender. We
investigate how to find these special points automatically so they may be used
in a number of problems, such as improving two-dimensional mathematical
recognition and in handwriting neatening, while preserving the original style.
Failure of Calibration is Typical
Authors: Gordon Belot
Date: 2013-06-20 00:00:00

Schervish (1985b) showed that every forecasting system is noncalibrated for
uncountably many data sequences that it might see. This result is strengthened
here: from a topological point of view, failure of calibration is typical and
calibration rare. Meanwhile, Bayesian forecasters are certain that they are
calibrated---this invites worries about the connection between Bayesianism and
rationality.
A Multi-Engine Approach to Answer Set Programming
Authors: Marco Maratea, Luca Pulina, Francesco Ricca
Date: 2013-06-20 00:00:00
Tasks: BIG-bench Machine Learning

Answer Set Programming (ASP) is a truly-declarative programming paradigm
proposed in the area of non-monotonic reasoning and logic programming, that has
been recently employed in many applications. The development of efficient ASP
systems is, thus, crucial. Having in mind the task of improving the solving
methods for ASP, there are two usual ways to reach this goal: $(i)$ extending
state-of-the-art techniques and ASP solvers, or $(ii)$ designing a new ASP
solver from scratch. An alternative to these trends is to build on top of
state-of-the-art solvers, and to apply machine learning techniques for choosing
automatically the "best" available solver on a per-instance basis.
  In this paper we pursue this latter direction. We first define a set of
cheap-to-compute syntactic features that characterize several aspects of ASP
programs. Then, we apply classification methods that, given the features of the
instances in a {\sl training} set and the solvers' performance on these
instances, inductively learn algorithm selection strategies to be applied to a
{\sl test} set. We report the results of a number of experiments considering
solvers and different training and test sets of instances taken from the ones
submitted to the "System Track" of the 3rd ASP Competition. Our analysis shows
that, by applying machine learning techniques to ASP solving, it is possible to
obtain very robust performance: our approach can solve more instances compared
with any solver that entered the 3rd ASP Competition. (To appear in Theory and
Practice of Logic Programming (TPLP).)
Recognition of Named-Event Passages in News Articles
Authors: Luis Marujo, Wang Ling, Anatole Gershman, Jaime Carbonell, João P. Neto, David Matos
Date: 2013-06-20 00:00:00
Proceeding: COLING 2012 12
Tasks: Articles

We extend the concept of Named Entities to Named Events - commonly occurring
events such as battles and earthquakes. We propose a method for finding
specific passages in news articles that contain information about such events
and report our preliminary evaluation results. Collecting "Gold Standard" data
presents many problems, both practical and conceptual. We present a method for
obtaining such data using the Amazon Mechanical Turk service.
From-Below Approximations in Boolean Matrix Factorization: Geometry and New Algorithm
Authors: Radim Belohlavek, Martin Trnecka
Date: 2013-06-20 00:00:00

We present new results on Boolean matrix factorization and a new algorithm
based on these results. The results emphasize the significance of
factorizations that provide from-below approximations of the input matrix.
While the previously proposed algorithms do not consider the possibly different
significance of different matrix entries, our results help measure such
significance and suggest where to focus when computing factors. An experimental
evaluation of the new algorithm on both synthetic and real data demonstrates
its good performance in terms of good coverage by the first k factors as well
as a small number of factors needed for exact decomposition and indicates that
the algorithm outperforms the available ones in these terms. We also propose
future research topics.
Key Phrase Extraction of Lightly Filtered Broadcast News
Authors: Luis Marujo, Ricardo Ribeiro, David Martins de Matos, João P. Neto, Anatole Gershman, Jaime Carbonell
Date: 2013-06-20 00:00:00

This paper explores the impact of light filtering on automatic key phrase
extraction (AKE) applied to Broadcast News (BN). Key phrases are words and
expressions that best characterize the content of a document. Key phrases are
often used to index the document or as features in further processing. This
makes improvements in AKE accuracy particularly important. We hypothesized that
filtering out marginally relevant sentences from a document would improve AKE
accuracy. Our experiments confirmed this hypothesis. Elimination of as little
as 10% of the document sentences lead to a 2% improvement in AKE precision and
recall. AKE is built over MAUI toolkit that follows a supervised learning
approach. We trained and tested our AKE method on a gold standard made of 8 BN
programs containing 110 manually annotated news stories. The experiments were
conducted within a Multimedia Monitoring Solution (MMS) system for TV and radio
news/programs, running daily, and monitoring 12 TV and 4 radio channels.
Regularization and nonlinearities for neural language models: when are they needed?
Authors: Marius Pachitariu, Maneesh Sahani
Date: 2013-01-23 00:00:00
Tasks: Sentence, Sentence Completion

Neural language models (LMs) based on recurrent neural networks (RNN) are
some of the most successful word and character-level LMs. Why do they work so
well, in particular better than linear neural LMs? Possible explanations are
that RNNs have an implicitly better regularization or that RNNs have a higher
capacity for storing patterns due to their nonlinearities or both. Here we
argue for the first explanation in the limit of little training data and the
second explanation for large amounts of text data. We show state-of-the-art
performance on the popular and small Penn dataset when RNN LMs are regularized
with random dropout. Nonetheless, we show even better performance from a
simplified, much less expressive linear RNN model without off-diagonal entries
in the recurrent matrix. We call this model an impulse-response LM (IRLM).
Using random dropout, column normalization and annealed learning rates, IRLMs
develop neurons that keep a memory of up to 50 words in the past and achieve a
perplexity of 102.5 on the Penn dataset. On two large datasets however, the
same regularization methods are unsuccessful for both models and the RNN's
expressivity allows it to overtake the IRLM by 10 and 20 percent perplexity,
respectively. Despite the perplexity gap, IRLMs still outperform RNNs on the
Microsoft Research Sentence Completion (MRSC) task. We develop a slightly
modified IRLM that separates long-context units (LCUs) from short-context units
and show that the LCUs alone achieve a state-of-the-art performance on the MRSC
task of 60.8%. Our analysis indicates that a fruitful direction of research for
neural LMs lies in developing more accessible internal representations, and
suggests an optimization regime of very high momentum terms for effectively
training such models.
Supervised Topical Key Phrase Extraction of News Stories using Crowdsourcing, Light Filtering and Co-reference Normalization
Authors: Luis Marujo, Anatole Gershman, Jaime Carbonell, Robert Frederking, João P. Neto
Date: 2013-06-20 00:00:00
Proceeding: LREC 2012 5

Fast and effective automated indexing is critical for search and personalized
services. Key phrases that consist of one or more words and represent the main
concepts of the document are often used for the purpose of indexing. In this
paper, we investigate the use of additional semantic features and
pre-processing steps to improve automatic key phrase extraction. These features
include the use of signal words and freebase categories. Some of these features
lead to significant improvements in the accuracy of the results. We also
experimented with 2 forms of document pre-processing that we call light
filtering and co-reference normalization. Light filtering removes sentences
from the document, which are judged peripheral to its main content.
Co-reference normalization unifies several written forms of the same named
entity into a unique form. We also needed a "Gold Standard" - a set of labeled
documents for training and evaluation. While the subjective nature of key
phrase selection precludes a true "Gold Standard", we used Amazon's Mechanical
Turk service to obtain a useful approximation. Our data indicates that the
biggest improvements in performance were due to shallow semantic features, news
categories, and rhetorical signals (nDCG 78.47% vs. 68.93%). The inclusion of
deeper semantic features such as Freebase sub-categories was not beneficial by
itself, but in combination with pre-processing, did cause slight improvements
in the nDCG scores.
Iterative Grassmannian Optimization for Robust Image Alignment
Authors: Jun He, Dejiao Zhang, Laura Balzano, Tao Tao
Date: 2013-06-03 00:00:00
Tasks: Face Recognition

Robust high-dimensional data processing has witnessed an exciting development
in recent years, as theoretical results have shown that it is possible using
convex programming to optimize data fit to a low-rank component plus a sparse
outlier component. This problem is also known as Robust PCA, and it has found
application in many areas of computer vision. In image and video processing and
face recognition, the opportunity to process massive image databases is
emerging as people upload photo and video data online in unprecedented volumes.
However, data quality and consistency is not controlled in any way, and the
massiveness of the data poses a serious computational challenge. In this paper
we present t-GRASTA, or "Transformed GRASTA (Grassmannian Robust Adaptive
Subspace Tracking Algorithm)". t-GRASTA iteratively performs incremental
gradient descent constrained to the Grassmann manifold of subspaces in order to
simultaneously estimate a decomposition of a collection of images into a
low-rank subspace, a sparse part of occlusions and foreground objects, and a
transformation such as rotation or translation of the image. We show that
t-GRASTA is 4 $\times$ faster than state-of-the-art algorithms, has half the
memory requirement, and can achieve alignment for face images as well as
jittered camera surveillance images.
Off-Policy Actor-Critic
Authors: Thomas Degris, Martha White, Richard S. Sutton
Date: 2012-05-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents the first actor-critic algorithm for off-policy
reinforcement learning. Our algorithm is online and incremental, and its
per-time-step complexity scales linearly with the number of learned weights.
Previous work on actor-critic algorithms is limited to the on-policy setting
and does not take advantage of the recent advances in off-policy gradient
temporal-difference learning. Off-policy techniques, such as Greedy-GQ, enable
a target policy to be learned while following and obtaining data from another
(behavior) policy. For many problems, however, actor-critic methods are more
practical than action value methods (like Greedy-GQ) because they explicitly
represent the policy; consequently, the policy can be stochastic and utilize a
large action space. In this paper, we illustrate how to practically combine the
generality and learning potential of off-policy learning with the flexibility
in action selection given by actor-critic methods. We derive an incremental,
linear time and space complexity algorithm that includes eligibility traces,
prove convergence under assumptions similar to previous off-policy algorithms,
and empirically show better or comparable performance to existing algorithms on
standard reinforcement-learning benchmark problems.
Quantifying Morphological Computation
Authors: Keyan Zahedi, Nihat Ay
Date: 2013-01-29 00:00:00

The field of embodied intelligence emphasises the importance of the
morphology and environment with respect to the behaviour of a cognitive system.
The contribution of the morphology to the behaviour, commonly known as
morphological computation, is well-recognised in this community. We believe
that the field would benefit from a formalisation of this concept as we would
like to ask how much the morphology and the environment contribute to an
embodied agent's behaviour, or how an embodied agent can maximise the
exploitation of its morphology within its environment. In this work we derive
two concepts of measuring morphological computation, and we discuss their
relation to the Information Bottleneck Method. The first concepts asks how much
the world contributes to the overall behaviour and the second concept asks how
much the agent's action contributes to a behaviour. Various measures are
derived from the concepts and validated in two experiments which highlight
their strengths and weaknesses.
Evolving Boolean Regulatory Networks with Epigenetic Control
Authors: Larry Bull
Date: 2013-06-20 00:00:00

The significant role of epigenetic mechanisms within natural systems has
become increasingly clear. This paper uses a recently presented abstract,
tunable Boolean genetic regulatory network model to explore aspects of
epigenetics. It is shown how dynamically controlling transcription via a DNA
methylation-inspired mechanism can be selected for by simulated evolution under
various single and multiple cell scenarios. Further, it is shown that the
effects of such control can be inherited without detriment to fitness.
Analysing Word Importance for Image Annotation
Authors: Payal Gulati, A. K. Sharma
Date: 2013-06-20 00:00:00
Tasks: Image Retrieval, Retrieval

Image annotation provides several keywords automatically for a given image
based on various tags to describe its contents which is useful in Image
retrieval. Various researchers are working on text based and content based
image annotations [7,9]. It is seen, in traditional Image annotation
approaches, annotation words are treated equally without considering the
importance of each word in real world. In context of this, in this work, images
are annotated with keywords based on their frequency count and word
correlation. Moreover this work proposes an approach to compute importance
score of candidate keywords, having same frequency count.
Gene selection with guided regularized random forest
Authors: Houtao Deng, George Runger
Date: 2012-09-28 00:00:00
Tasks: feature selection

The regularized random forest (RRF) was recently proposed for feature
selection by building only one ensemble. In RRF the features are evaluated on a
part of the training data at each tree node. We derive an upper bound for the
number of distinct Gini information gain values in a node, and show that many
features can share the same information gain at a node with a small number of
instances and a large number of features. Therefore, in a node with a small
number of instances, RRF is likely to select a feature not strongly relevant.
Here an enhanced RRF, referred to as the guided RRF (GRRF), is proposed. In
GRRF, the importance scores from an ordinary random forest (RF) are used to
guide the feature selection process in RRF. Experiments on 10 gene data sets
show that the accuracy performance of GRRF is, in general, more robust than RRF
when their parameters change. GRRF is computationally efficient, can select
compact feature subsets, and has competitive accuracy performance, compared to
RRF, varSelRF and LASSO logistic regression (with evaluations from an RF
classifier). Also, RF applied to the features selected by RRF with the minimal
regularization outperforms RF applied to all the features for most of the data
sets considered here. Therefore, if accuracy is considered more important than
the size of the feature subset, RRF with the minimal regularization may be
considered. We use the accuracy performance of RF, a strong classifier, to
evaluate feature selection methods, and illustrate that weak classifiers are
less capable of capturing the information contained in a feature subset. Both
RRF and GRRF were implemented in the "RRF" R package available at CRAN, the
official R package archive.
Galerkin Methods for Complementarity Problems and Variational Inequalities
Authors: Geoffrey J. Gordon
Date: 2013-06-20 00:00:00

Complementarity problems and variational inequalities arise in a wide variety
of areas, including machine learning, planning, game theory, and physical
simulation. In all of these areas, to handle large-scale problem instances, we
need fast approximate solution methods. One promising idea is Galerkin
approximation, in which we search for the best answer within the span of a
given set of basis functions. Bertsekas proposed one possible Galerkin method
for variational inequalities. However, this method can exhibit two problems in
practice: its approximation error is worse than might be expected based on the
ability of the basis to represent the desired solution, and each iteration
requires a projection step that is not always easy to implement efficiently.
So, in this paper, we present a new Galerkin method with improved behavior: our
new error bounds depend directly on the distance from the true solution to the
subspace spanned by our basis, and the only projections we require are onto the
feasible region or onto the span of our basis.
Felzenszwalb-Baum-Welch: Event Detection by Changing Appearance
Authors: Daniel Paul Barrett, Jeffrey Mark Siskind
Date: 2013-06-20 00:00:00
Tasks: Event Detection, Object, object-detection, Object Detection

We propose a method which can detect events in videos by modeling the change
in appearance of the event participants over time. This method makes it
possible to detect events which are characterized not by motion, but by the
changing state of the people or objects involved. This is accomplished by using
object detectors as output models for the states of a hidden Markov model
(HMM). The method allows an HMM to model the sequence of poses of the event
participants over time, and is effective for poses of humans and inanimate
objects. The ability to use existing object-detection methods as part of an
event model makes it possible to leverage ongoing work in the object-detection
community. A novel training method uses an EM loop to simultaneously learn the
temporal structure and object models automatically, without the need to specify
either the individual poses to be modeled or the frames in which they occur.
The E-step estimates the latent assignment of video frames to HMM states, while
the M-step estimates both the HMM transition probabilities and state output
models, including the object detectors, which are trained on the weighted
subset of frames assigned to their state. A new dataset was gathered because
little work has been done on events characterized by changing object pose, and
suitable datasets are not available. Our method produced results superior to
that of comparison systems on this dataset.
Computer simulation based parameter selection for resistance exercise
Authors: Ognjen Arandjelovic
Date: 2013-06-20 00:00:00

In contrast to most scientific disciplines, sports science research has been
characterized by comparatively little effort investment in the development of
relevant phenomenological models. Scarcer yet is the application of said models
in practice. We present a framework which allows resistance training
practitioners to employ a recently proposed neuromuscular model in actual
training program design. The first novelty concerns the monitoring aspect of
coaching. A method for extracting training performance characteristics from
loosely constrained video sequences, effortlessly and with minimal human input,
using computer vision is described. The extracted data is subsequently used to
fit the underlying neuromuscular model. This is achieved by solving an inverse
dynamics problem corresponding to a particular exercise. Lastly, a computer
simulation of hypothetical training bouts, using athlete-specific capability
parameters, is used to predict the effected adaptation and changes in
performance. The software described here allows the practitioner to manipulate
hypothetical training parameters and immediately see their effect on predicted
adaptation for a specific athlete. Thus, this work presents a holistic view of
the monitoring-assessment-adjustment loop.
Event-Object Reasoning with Curated Knowledge Bases: Deriving Missing Information
Authors: Chitta Baral, Nguyen H. Vo
Date: 2013-06-19 00:00:00

The broader goal of our research is to formulate answers to why and how
questions with respect to knowledge bases, such as AURA. One issue we face when
reasoning with many available knowledge bases is that at times needed
information is missing. Examples of this include partially missing information
about next sub-event, first sub-event, last sub-event, result of an event,
input to an event, destination of an event, and raw material involved in an
event. In many cases one can recover part of the missing knowledge through
reasoning. In this paper we give a formal definition about how such missing
information can be recovered and then give an ASP implementation of it. We then
discuss the implication of this with respect to answering why and how
questions.
Attack Planning in the Real World
Authors: Jorge Lucangeli Obes, Carlos Sarraute, Gerardo Richarte
Date: 2013-06-18 00:00:00

Assessing network security is a complex and difficult task. Attack graphs
have been proposed as a tool to help network administrators understand the
potential weaknesses of their network. However, a problem has not yet been
addressed by previous work on this subject; namely, how to actually execute and
validate the attack paths resulting from the analysis of the attack graph. In
this paper we present a complete PDDL representation of an attack model, and an
implementation that integrates a planner into a penetration testing tool. This
allows to automatically generate attack paths for penetration testing
scenarios, and to validate these attacks by executing the corresponding actions
-including exploits- against the real target network. We present an algorithm
for transforming the information present in the penetration testing tool to the
planning domain, and show how the scalability issues of attack graphs can be
solved using current planners. We include an analysis of the performance of our
solution, showing how our model scales to medium-sized networks and the number
of actions available in current penetration testing tools.