pwc-archive/papers-with-abstracts huggingface dataset:
A Neuro-Fuzzy Method to Improving Backfiring Conversion Ratios
Authors: Justin Wong, Danny Ho, Luiz Fernando Capretz
Date: 2015-08-25 00:00:00

Software project estimation is crucial aspect in delivering software on time
and on budget. Software size is an important metric in determining the effort,
cost, and productivity. Today, source lines of code and function point are the
most used sizing metrics. Backfiring is a well-known technique for converting
between function points and source lines of code. However when backfiring is
used, there is a high margin of error. This study introduces a method to
improve the accuracy of backfiring. Intelligent systems have been used in
software prediction models to improve performance over traditional techniques.
For this reason, a hybrid Neuro-Fuzzy is used because it takes advantages of
the neural networks learning and fuzzy logic human-like reasoning. This paper
describes an improved backfiring technique which uses Neuro-Fuzzy and compares
the new method against the default conversion ratios currently used by software
practitioners.
Sparse 3D convolutional neural networks
Authors: Ben Graham
Date: 2015-05-12 00:00:00
Tasks: 3D Object Recognition, Object Recognition, Temporal Action Localization

We have implemented a convolutional neural network designed for processing
sparse three-dimensional input data. The world we live in is three dimensional
so there are a large number of potential applications including 3D object
recognition and analysis of space-time objects. In the quest for efficiency, we
experiment with CNNs on the 2D triangular-lattice and 3D tetrahedral-lattice.
BREN: Body Reflection Essence-Neuter Model for Separation of Reflection Components
Authors: Changsoo Je, Hyung-Min Park
Date: 2015-08-25 00:00:00

We propose a novel reflection color model consisting of body essence and
(mixed) neuter, and present an effective method for separating dichromatic
reflection components using a single image. Body essence is an entity invariant
to interface reflection, and has two degrees of freedom unlike hue and maximum
chromaticity. As a result, the proposed method is insensitive to noise and
proper for colors around CMY (cyan, magenta, and yellow) as well as RGB (red,
green, and blue), contrary to the maximum chromaticity-based methods. Interface
reflection is separated by using a Gaussian function, which removes a critical
thresholding problem. Furthermore, the method does not require any region
segmentation. Experimental results show the efficacy of the proposed model and
method.
Robot Language Learning, Generation, and Comprehension
Authors: Daniel Paul Barrett, Scott Alan Bronikowski, Haonan Yu, Jeffrey Mark Siskind
Date: 2015-08-25 00:00:00

We present a unified framework which supports grounding natural-language
semantics in robotic driving. This framework supports acquisition (learning
grounded meanings of nouns and prepositions from human annotation of robotic
driving paths), generation (using such acquired meanings to generate sentential
description of new robotic driving paths), and comprehension (using such
acquired meanings to support automated driving to accomplish navigational goals
specified in natural language). We evaluate the performance of these three
tasks by having independent human judges rate the semantic fidelity of the
sentences associated with paths, achieving overall average correctness of 94.6%
and overall average completeness of 85.6%.
A Practical Guide to CNNs and Fisher Vectors for Image Instance Retrieval
Authors: Vijay Chandrasekhar, Jie Lin, Olivier Morère, Hanlin Goh, Antoine Veillard
Date: 2015-08-11 00:00:00
Tasks: image-classification, Image Classification, Image Instance Retrieval, Image Retrieval, Retrieval

With deep learning becoming the dominant approach in computer vision, the use
of representations extracted from Convolutional Neural Nets (CNNs) is quickly
gaining ground on Fisher Vectors (FVs) as favoured state-of-the-art global
image descriptors for image instance retrieval. While the good performance of
CNNs for image classification are unambiguously recognised, which of the two
has the upper hand in the image retrieval context is not entirely clear yet. In
this work, we propose a comprehensive study that systematically evaluates FVs
and CNNs for image retrieval. The first part compares the performances of FVs
and CNNs on multiple publicly available data sets. We investigate a number of
details specific to each method. For FVs, we compare sparse descriptors based
on interest point detectors with dense single-scale and multi-scale variants.
For CNNs, we focus on understanding the impact of depth, architecture and
training data on retrieval results. Our study shows that no descriptor is
systematically better than the other and that performance gains can usually be
obtained by using both types together. The second part of the study focuses on
the impact of geometrical transformations such as rotations and scale changes.
FVs based on interest point detectors are intrinsically resilient to such
transformations while CNNs do not have a built-in mechanism to ensure such
invariance. We show that performance of CNNs can quickly degrade in presence of
rotations while they are far less affected by changes in scale. We then propose
a number of ways to incorporate the required invariances in the CNN pipeline.
Overall, our work is intended as a reference guide offering practically useful
and simply implementable guidelines to anyone looking for state-of-the-art
global descriptors best suited to their specific image instance retrieval
problem.
Unsatisfiable Cores and Lower Bounding for Constraint Programming
Authors: Nicholas Downing, Thibaut Feydy, Peter J. Stuckey
Date: 2015-08-25 00:00:00

Constraint Programming (CP) solvers typically tackle optimization problems by
repeatedly finding solutions to a problem while placing tighter and tighter
bounds on the solution cost. This approach is somewhat naive, especially for
soft-constraint optimization problems in which the soft constraints are mostly
satisfied. Unsatisfiable-core approaches to solving soft constraint problems in
SAT (e.g. MAXSAT) force all soft constraints to be hard initially. When solving
fails they return an unsatisfiable core, as a set of soft constraints that
cannot hold simultaneously. These are reverted to soft and solving continues.
Since lazy clause generation solvers can also return unsatisfiable cores we can
adapt this approach to constraint programming. We adapt the original MAXSAT
unsatisfiable core solving approach to be usable for constraint programming and
define a number of extensions. Experimental results show that our methods are
beneficial on a broad class of CP-optimization benchmarks involving soft
constraints, cardinality or preferences.
OCReP: An Optimally Conditioned Regularization for Pseudoinversion Based Neural Training
Authors: Rossella Cancelliere, Mario Gai, Patrick Gallinari, Luca Rubini
Date: 2015-08-25 00:00:00
Tasks: Diagnostic

In this paper we consider the training of single hidden layer neural networks
by pseudoinversion, which, in spite of its popularity, is sometimes affected by
numerical instability issues. Regularization is known to be effective in such
cases, so that we introduce, in the framework of Tikhonov regularization, a
matricial reformulation of the problem which allows us to use the condition
number as a diagnostic tool for identification of instability. By imposing
well-conditioning requirements on the relevant matrices, our theoretical
analysis allows the identification of an optimal value for the regularization
parameter from the standpoint of stability. We compare with the value derived
by cross-validation for overfitting control and optimisation of the
generalization performance. We test our method for both regression and
classification tasks. The proposed method is quite effective in terms of
predictivity, often with some improvement on performance with respect to the
reference cases considered. This approach, due to analytical determination of
the regularization parameter, dramatically reduces the computational load
required by many other techniques.
An analysis of numerical issues in neural training by pseudoinversion
Authors: R. Cancelliere, R. Deluca, M. Gai, P. Gallinari, L. Rubini
Date: 2015-08-25 00:00:00

Some novel strategies have recently been proposed for single hidden layer
neural network training that set randomly the weights from input to hidden
layer, while weights from hidden to output layer are analytically determined by
pseudoinversion. These techniques are gaining popularity in spite of their
known numerical issues when singular and/or almost singular matrices are
involved. In this paper we discuss a critical use of Singular Value Analysis
for identification of these drawbacks and we propose an original use of
regularisation to determine the output weights, based on the concept of
critical hidden layer size. This approach also allows to limit the training
computational effort. Besides, we introduce a novel technique which relies an
effective determination of input weights to the hidden layer dimension. This
approach is tested for both regression and classification tasks, resulting in a
significant performance improvement with respect to alternative methods.
AUC Optimisation and Collaborative Filtering
Authors: Charanpal Dhanjal, Romaric Gaudel, Stephan Clemencon
Date: 2015-08-25 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

In recommendation systems, one is interested in the ranking of the predicted
items as opposed to other losses such as the mean squared error. Although a
variety of ways to evaluate rankings exist in the literature, here we focus on
the Area Under the ROC Curve (AUC) as it widely used and has a strong
theoretical underpinning. In practical recommendation, only items at the top of
the ranked list are presented to the users. With this in mind, we propose a
class of objective functions over matrix factorisations which primarily
represent a smooth surrogate for the real AUC, and in a special case we show
how to prioritise the top of the list. The objectives are differentiable and
optimised through a carefully designed stochastic gradient-descent-based
algorithm which scales linearly with the size of the data. In the special case
of square loss we show how to improve computational complexity by leveraging
previously computed measures. To understand theoretically the underlying matrix
factorisation approaches we study both the consistency of the loss functions
with respect to AUC, and generalisation using Rademacher theory. The resulting
generalisation analysis gives strong motivation for the optimisation under
study. Finally, we provide computation results as to the efficacy of the
proposed method using synthetic and real data.
Visualizing NLP annotations for Crowdsourcing
Authors: Hanchuan Li, Haichen Shen, Shengliang Xu, Congle Zhang
Date: 2015-08-25 00:00:00
Tasks: Clustering

Visualizing NLP annotation is useful for the collection of training data for
the statistical NLP approaches. Existing toolkits either provide limited visual
aid, or introduce comprehensive operators to realize sophisticated linguistic
rules. Workers must be well trained to use them. Their audience thus can hardly
be scaled to large amounts of non-expert crowdsourced workers. In this paper,
we present CROWDANNO, a visualization toolkit to allow crowd-sourced workers to
annotate two general categories of NLP problems: clustering and parsing.
Workers can finish the tasks with simplified operators in an interactive
interface, and fix errors conveniently. User studies show our toolkit is very
friendly to NLP non-experts, and allow them to produce high quality labels for
several sophisticated problems. We release our source code and toolkit to spur
future research.
Better Summarization Evaluation with Word Embeddings for ROUGE
Authors: Jun-Ping Ng, Viktoria Abrecht
Date: 2015-08-25 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Abstractive Text Summarization, Semantic Similarity, Semantic Textual Similarity, Text Summarization, Word Embeddings

ROUGE is a widely adopted, automatic evaluation measure for text
summarization. While it has been shown to correlate well with human judgements,
it is biased towards surface lexical similarities. This makes it unsuitable for
the evaluation of abstractive summarization, or summaries with substantial
paraphrasing. We study the effectiveness of word embeddings to overcome this
disadvantage of ROUGE. Specifically, instead of measuring lexical overlaps,
word embeddings are used to compute the semantic similarity of the words used
in summaries instead. Our experimental results show that our proposal is able
to achieve better correlations with human judgements when measured with the
Spearman and Kendall rank coefficients.
Depth-Gated LSTM
Authors: Kaisheng Yao, Trevor Cohn, Katerina Vylomova, Kevin Duh, Chris Dyer
Date: 2015-08-16 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

In this short note, we present an extension of long short-term memory (LSTM)
neural networks to using a depth gate to connect memory cells of adjacent
layers. Doing so introduces a linear dependence between lower and upper layer
recurrent units. Importantly, the linear dependence is gated through a gating
function, which we call depth gate. This gate is a function of the lower layer
memory cell, the input to and the past memory cell of this layer. We conducted
experiments and verified that this new architecture of LSTMs was able to
improve machine translation and language modeling performances.
ERBlox: Combining Matching Dependencies with Machine Learning for Entity Resolution
Authors: Zeinab Bahmani, Leopoldo Bertossi, Nikolaos Vasiloglou
Date: 2015-08-25 00:00:00
Tasks: Attribute, BIG-bench Machine Learning, Blocking, Entity Resolution

Entity resolution (ER), an important and common data cleaning problem, is
about detecting data duplicate representations for the same external entities,
and merging them into single representations. Relatively recently, declarative
rules called matching dependencies (MDs) have been proposed for specifying
similarity conditions under which attribute values in database records are
merged. In this work we show the process and the benefits of integrating three
components of ER: (a) Classifiers for duplicate/non-duplicate record pairs
built using machine learning (ML) techniques, (b) MDs for supporting both the
blocking phase of ML and the merge itself; and (c) The use of the declarative
language LogiQL -an extended form of Datalog supported by the LogicBlox
platform- for data processing, and the specification and enforcement of MDs.
Wavelet subspace decomposition of thermal infrared images for defect detection in artworks
Authors: Muhammad Zubair Ahmad, Amir Ali Khan, Sihem Mezghani, Eric Perrin, Kamel Mouhoubi, Jean-Luc Bodnar, Valeriu Vrabie
Date: 2015-08-25 00:00:00
Tasks: Defect Detection, Fault Detection

Monitoring the health of ancient artworks requires adequate prudence because
of the sensitive nature of these materials. Classical techniques for
identifying the development of faults rely on acoustic testing. These
techniques, being invasive, may result in causing permanent damage to the
material, especially if the material is inspected periodically. Non destructive
testing has been carried out for different materials since long. In this
regard, non-invasive systems were developed based on infrared thermometry
principle to identify the faults in artworks. The test artwork is heated and
the thermal response of the different layers is captured with the help of a
thermal infrared camera. However, prolonged heating risks overheating and thus
causing damage to artworks and an alternate approach is to use pseudo-random
binary sequence excitations. The faults in the artwork, though, cannot be
detected on the captured images, especially if their strength is weak. The
weaker faults are either masked by the stronger ones, by the pictorial layer of
the artwork or by the non-uniform heating. This work addresses the detection
and localization of the faults through a wavelet based subspace decomposition
scheme. The proposed scheme, on one hand, allows to remove the background
while, on the other hand, removes the undesired high frequency noise. It is
shown that the detection parameter is proportional to the diameter and the
depth of the fault. A criterion is proposed to select the optimal wavelet basis
along with suitable level selection for wavelet decomposition and
reconstruction. The proposed approach is tested on a laboratory developed test
sample with known fault locations and dimensions as well as real artworks. A
comparison with a previously reported method demonstrates the efficacy of the
proposed approach for fault detection in artworks.
An algorithm for Left Atrial Thrombi detection using Transesophageal Echocardiography
Authors: Jianrui Ding, Min Xian, H. D. Cheng, Yang Li, Fei Xu, Yingtao Zhang
Date: 2015-08-24 00:00:00
Tasks: Multiple Instance Learning

Transesophageal echocardiography (TEE) is widely used to detect left atrium
(LA)/left atrial appendage (LAA) thrombi. In this paper, the local binary
pattern variance (LBPV) features are extracted from region of interest (ROI).
And the dynamic features are formed by using the information of its neighbor
frames in the sequence. The sequence is viewed as a bag, and the images in the
sequence are considered as the instances. Multiple-instance learning (MIL)
method is employed to solve the LAA thrombi detection. The experimental results
show that the proposed method can achieve better performance than that by using
other methods.
Eliciting Disease Data from Wikipedia Articles
Authors: Geoffrey Fairchild, Lalindra De Silva, Sara Y. Del Valle, Alberto M. Segre
Date: 2015-04-02 00:00:00
Tasks: Articles, TAG, Time Series, Time Series Analysis

Traditional disease surveillance systems suffer from several disadvantages,
including reporting lags and antiquated technology, that have caused a movement
towards internet-based disease surveillance systems. Internet systems are
particularly attractive for disease outbreaks because they can provide data in
near real-time and can be verified by individuals around the globe. However,
most existing systems have focused on disease monitoring and do not provide a
data repository for policy makers or researchers. In order to fill this gap, we
analyzed Wikipedia article content.
  We demonstrate how a named-entity recognizer can be trained to tag case
counts, death counts, and hospitalization counts in the article narrative that
achieves an F1 score of 0.753. We also show, using the 2014 West African Ebola
virus disease epidemic article as a case study, that there are detailed time
series data that are consistently updated that closely align with ground truth
data.
  We argue that Wikipedia can be used to create the first community-driven
open-source emerging disease detection, monitoring, and repository system.
A Smoothed Dual Approach for Variational Wasserstein Problems
Authors: Marco Cuturi, Gabriel Peyré
Date: 2015-03-09 00:00:00

Variational problems that involve Wasserstein distances have been recently
proposed to summarize and learn from probability measures. Despite being
conceptually simple, such problems are computationally challenging because they
involve minimizing over quantities (Wasserstein distances) that are themselves
hard to compute. We show that the dual formulation of Wasserstein variational
problems introduced recently by Carlier et al. (2014) can be regularized using
an entropic smoothing, which leads to smooth, differentiable, convex
optimization problems that are simpler to implement and numerically more
stable. We illustrate the versatility of this approach by applying it to the
computation of Wasserstein barycenters and gradient flows of spacial
regularization functionals.
Label optimal regret bounds for online local learning
Authors: Pranjal Awasthi, Moses Charikar, Kevin A. Lai, Andrej Risteski
Date: 2015-03-07 00:00:00
Tasks: Collaborative Filtering, Open-Ended Question Answering

We resolve an open question from (Christiano, 2014b) posed in COLT'14
regarding the optimal dependency of the regret achievable for online local
learning on the size of the label set. In this framework the algorithm is shown
a pair of items at each step, chosen from a set of $n$ items. The learner then
predicts a label for each item, from a label set of size $L$ and receives a
real valued payoff. This is a natural framework which captures many interesting
scenarios such as collaborative filtering, online gambling, and online max cut
among others. (Christiano, 2014a) designed an efficient online learning
algorithm for this problem achieving a regret of $O(\sqrt{nL^3T})$, where $T$
is the number of rounds. Information theoretically, one can achieve a regret of
$O(\sqrt{n \log L T})$. One of the main open questions left in this framework
concerns closing the above gap.
  In this work, we provide a complete answer to the question above via two main
results. We show, via a tighter analysis, that the semi-definite programming
based algorithm of (Christiano, 2014a), in fact achieves a regret of
$O(\sqrt{nLT})$. Second, we show a matching computational lower bound. Namely,
we show that a polynomial time algorithm for online local learning with lower
regret would imply a polynomial time algorithm for the planted clique problem
which is widely believed to be hard. We prove a similar hardness result under a
related conjecture concerning planted dense subgraphs that we put forth. Unlike
planted clique, the planted dense subgraph problem does not have any known
quasi-polynomial time algorithms.
  Computational lower bounds for online learning are relatively rare, and we
hope that the ideas developed in this work will lead to lower bounds for other
online learning scenarios as well.
Another Look at DWD: Thrifty Algorithm and Bayes Risk Consistency in RKHS
Authors: Boxiang Wang, Hui Zou
Date: 2015-08-24 00:00:00
Tasks: Learning Theory

Distance weighted discrimination (DWD) is a margin-based classifier with an
interesting geometric motivation. DWD was originally proposed as a superior
alternative to the support vector machine (SVM), however DWD is yet to be
popular compared with the SVM. The main reasons are twofold. First, the
state-of-the-art algorithm for solving DWD is based on the second-order-cone
programming (SOCP), while the SVM is a quadratic programming problem which is
much more efficient to solve. Second, the current statistical theory of DWD
mainly focuses on the linear DWD for the high-dimension-low-sample-size setting
and data-piling, while the learning theory for the SVM mainly focuses on the
Bayes risk consistency of the kernel SVM. In fact, the Bayes risk consistency
of DWD is presented as an open problem in the original DWD paper. In this work,
we advance the current understanding of DWD from both computational and
theoretical perspectives. We propose a novel efficient algorithm for solving
DWD, and our algorithm can be several hundred times faster than the existing
state-of-the-art algorithm based on the SOCP. In addition, our algorithm can
handle the generalized DWD, while the SOCP algorithm only works well for a
special DWD but not the generalized DWD. Furthermore, we consider a natural
kernel DWD in a reproducing kernel Hilbert space and then establish the Bayes
risk consistency of the kernel DWD. We compare DWD and the SVM on several
benchmark data sets and show that the two have comparable classification
accuracy, but DWD equipped with our new algorithm can be much faster to compute
than the SVM.
A Framework for Comparing Groups of Documents
Authors: Arun S. Maiya
Date: 2015-08-24 00:00:00

We present a general framework for comparing multiple groups of documents. A
bipartite graph model is proposed where document groups are represented as one
node set and the comparison criteria are represented as the other node set.
Using this model, we present basic algorithms to extract insights into
similarities and differences among the document groups. Finally, we demonstrate
the versatility of our framework through an analysis of NSF funding programs
for basic research.