pwc-archive/papers-with-abstracts huggingface dataset:
Applications of repeat degree on coverings of neighborhoods
Authors: Hua Yao, William Zhu
Date: 2013-07-10 00:00:00

In covering based rough sets, the neighborhood of an element is the
intersection of all the covering blocks containing the element. All the
neighborhoods form a new covering called a covering of neighborhoods. In the
course of studying under what condition a covering of neighborhoods is a
partition, the concept of repeat degree is proposed, with the help of which the
issue is addressed. This paper studies further the application of repeat degree
on coverings of neighborhoods. First, we investigate under what condition a
covering of neighborhoods is the reduct of the covering inducing it. As a
preparation for addressing this issue, we give a necessary and sufficient
condition for a subset of a set family to be the reduct of the set family. Then
we study under what condition two coverings induce a same relation and a same
covering of neighborhoods. Finally, we give the method of calculating the
covering according to repeat degree.
Error Rate Bounds in Crowdsourcing Models
Authors: Hongwei Li, Bin Yu, Dengyong Zhou
Date: 2013-07-10 00:00:00

Crowdsourcing is an effective tool for human-powered computation on many
tasks challenging for computers. In this paper, we provide finite-sample
exponential bounds on the error rate (in probability and in expectation) of
hyperplane binary labeling rules under the Dawid-Skene crowdsourcing model. The
bounds can be applied to analyze many common prediction methods, including the
majority voting and weighted majority voting. These bound results could be
useful for controlling the error rate and designing better algorithms. We show
that the oracle Maximum A Posterior (MAP) rule approximately optimizes our
upper bound on the mean error rate for any hyperplane binary labeling rule, and
propose a simple data-driven weighted majority voting (WMV) rule (called
one-step WMV) that attempts to approximate the oracle MAP and has a provable
theoretical guarantee on the error rate. Moreover, we use simulated and real
data to demonstrate that the data-driven EM-MAP rule is a good approximation to
the oracle MAP rule, and to demonstrate that the mean error rate of the
data-driven EM-MAP rule is also bounded by the mean error rate bound of the
oracle MAP rule with estimated parameters plugging into the bound.
Selection Mammogram Texture Descriptors Based on Statistics Properties Backpropagation Structure
Authors: Shofwatul 'Uyun, Sri Hartati, Agus Harjoko, Subanar
Date: 2013-07-10 00:00:00

Computer Aided Diagnosis (CAD) system has been developed for the early
detection of breast cancer, one of the most deadly cancer for women. The benign
of mammogram has different texture from malignant. There are fifty mammogram
images used in this work which are divided for training and testing. Therefore,
the selection of the right texture to determine the level of accuracy of CAD
system is important. The first and second order statistics are the texture
feature extraction methods which can be used on a mammogram. This work
classifies texture descriptor into nine groups where the extraction of features
is classified using backpropagation learning with two types of multi-layer
perceptron (MLP). The best texture descriptor as selected when the value of
regression 1 appears in both the MLP-1 and the MLP-2 with the number of epoches
less than 1000. The results of testing show that the best selected texture
descriptor is the second order (combination) using all direction (0, 45, 90 and
135) that have twenty four descriptors.
Controlling the Precision-Recall Tradeoff in Differential Dependency Network Analysis
Authors: Diane Oyen, Alexandru Niculescu-Mizil, Rachel Ostroff, Alex Stewart, Vincent P. Clark
Date: 2013-07-09 00:00:00
Tasks: Transfer Learning

Graphical models have gained a lot of attention recently as a tool for
learning and representing dependencies among variables in multivariate data.
Often, domain scientists are looking specifically for differences among the
dependency networks of different conditions or populations (e.g. differences
between regulatory networks of different species, or differences between
dependency networks of diseased versus healthy populations). The standard
method for finding these differences is to learn the dependency networks for
each condition independently and compare them. We show that this approach is
prone to high false discovery rates (low precision) that can render the
analysis useless. We then show that by imposing a bias towards learning similar
dependency networks for each condition the false discovery rates can be reduced
to acceptable levels, at the cost of finding a reduced number of differences.
Algorithms developed in the transfer learning literature can be used to vary
the strength of the imposed similarity bias and provide a natural mechanism to
smoothly adjust this differential precision-recall tradeoff to cater to the
requirements of the analysis conducted. We present real case studies
(oncological and neurological) where domain experts use the proposed technique
to extract useful differential networks that shed light on the biological
processes involved in cancer and brain function.
Cups Products in Z2-Cohomology of 3D Polyhedral Complexes
Authors: Rocio Gonalez-Diaz, Javier Lamar, Ronald Umble
Date: 2012-07-10 00:00:00

Let $I=(\mathbb{Z}^3,26,6,B)$ be a 3D digital image, let $Q(I)$ be the
associated cubical complex and let $\partial Q(I)$ be the subcomplex of $Q(I)$
whose maximal cells are the quadrangles of $Q(I)$ shared by a voxel of $B$ in
the foreground -- the object under study -- and by a voxel of
$\mathbb{Z}^3\smallsetminus B$ in the background -- the ambient space. We show
how to simplify the combinatorial structure of $\partial Q(I)$ and obtain a 3D
polyhedral complex $P(I)$ homeomorphic to $\partial Q(I)$ but with fewer cells.
We introduce an algorithm that computes cup products on
$H^*(P(I);\mathbb{Z}_2)$ directly from the combinatorics. The computational
method introduced here can be effectively applied to any polyhedral complex
embedded in $\mathbb{R}^3$.
Tuned Models of Peer Assessment in MOOCs
Authors: Chris Piech, Jonathan Huang, Zhenghao Chen, Chuong Do, Andrew Ng, Daphne Koller
Date: 2013-07-09 00:00:00

In massive open online courses (MOOCs), peer grading serves as a critical
tool for scaling the grading of complex, open-ended assignments to courses with
tens or hundreds of thousands of students. But despite promising initial
trials, it does not always deliver accurate results compared to human experts.
In this paper, we develop algorithms for estimating and correcting for grader
biases and reliabilities, showing significant improvement in peer grading
accuracy on real data with 63,199 peer grades from Coursera's HCI course
offerings --- the largest peer grading networks analysed to date. We relate
grader biases and reliabilities to other student factors such as student
engagement, performance as well as commenting style. We also show that our
model can lead to more intelligent assignment of graders to gradees.
Image Fusion Technologies In Commercial Remote Sensing Packages
Authors: Firouz Abdullah Al-Wassai, N. V. Kalyankar
Date: 2013-07-09 00:00:00
Tasks: General Classification

Several remote sensing software packages are used to the explicit purpose of
analyzing and visualizing remotely sensed data, with the developing of remote
sensing sensor technologies from last ten years. Accord-ing to literature, the
remote sensing is still the lack of software tools for effective information
extraction from remote sensing data. So, this paper provides a state-of-art of
multi-sensor image fusion technologies as well as review on the quality
evaluation of the single image or fused images in the commercial remote sensing
pack-ages. It also introduces program (ALwassaiProcess) developed for image
fusion and classification.
Major Limitations of Satellite images
Authors: Firouz A. Al-Wassai, N. V. Kalyankar
Date: 2013-07-09 00:00:00

Remote sensing has proven to be a powerful tool for the monitoring of the
Earth surface to improve our perception of our surroundings has led to
unprecedented developments in sensor and information technologies. However,
technologies for effective use of the data and for extracting useful
information from the data of Remote sensing are still very limited since no
single sensor combines the optimal spectral, spatial and temporal resolution.
This paper briefly reviews the limitations of satellite remote sensing. Also,
reviews on the problems of image fusion techniques. The conclusion of this,
According to literature, the remote sensing is still the lack of software tools
for effective information extraction from remote sensing data. The trade-off in
spectral and spatial resolution will remain and new advanced data fusion
approaches are needed to make optimal use of remote sensors for extract the
most useful information.
The price of bandit information in multiclass online classification
Authors: Amit Daniely, Tom Helbertal
Date: 2013-02-05 00:00:00
Tasks: Classification, General Classification, Open-Ended Question Answering

We consider two scenarios of multiclass online learning of a hypothesis class
$H\subseteq Y^X$. In the {\em full information} scenario, the learner is
exposed to instances together with their labels. In the {\em bandit} scenario,
the true label is not exposed, but rather an indication whether the learner's
prediction is correct or not. We show that the ratio between the error rates in
the two scenarios is at most $8\cdot|Y|\cdot \log(|Y|)$ in the realizable case,
and $\tilde{O}(\sqrt{|Y|})$ in the agnostic case. The results are tight up to a
logarithmic factor and essentially answer an open question from (Daniely et.
al. - Multiclass learnability and the erm principle).
  We apply these results to the class of $\gamma$-margin multiclass linear
classifiers in $\reals^d$. We show that the bandit error rate of this class is
$\tilde{\Theta}(\frac{|Y|}{\gamma^2})$ in the realizable case and
$\tilde{\Theta}(\frac{1}{\gamma}\sqrt{|Y|T})$ in the agnostic case. This
resolves an open question from (Kakade et. al. - Efficient bandit algorithms
for online multiclass prediction).
Approximate dynamic programming using fluid and diffusion approximations with applications to power management
Authors: Wei Chen, Dayu Huang, Ankur A. Kulkarni, Jayakrishnan Unnikrishnan, Quanyan Zhu, Prashant Mehta, Sean Meyn, Adam Wierman
Date: 2013-07-06 00:00:00
Tasks: Management

Neuro-dynamic programming is a class of powerful techniques for approximating
the solution to dynamic programming equations. In their most computationally
attractive formulations, these techniques provide the approximate solution only
within a prescribed finite-dimensional function class. Thus, the question that
always arises is how should the function class be chosen? The goal of this
paper is to propose an approach using the solutions to associated fluid and
diffusion approximations. In order to illustrate this approach, the paper
focuses on an application to dynamic speed scaling for power management in
computer processors.
Bayesian Discovery of Multiple Bayesian Networks via Transfer Learning
Authors: Diane Oyen, Terran Lane
Date: 2013-07-09 00:00:00
Tasks: Transfer Learning

Bayesian network structure learning algorithms with limited data are being
used in domains such as systems biology and neuroscience to gain insight into
the underlying processes that produce observed data. Learning reliable networks
from limited data is difficult, therefore transfer learning can improve the
robustness of learned networks by leveraging data from related tasks. Existing
transfer learning algorithms for Bayesian network structure learning give a
single maximum a posteriori estimate of network models. Yet, many other models
may be equally likely, and so a more informative result is provided by Bayesian
structure discovery. Bayesian structure discovery algorithms estimate posterior
probabilities of structural features, such as edges. We present transfer
learning for Bayesian structure discovery which allows us to explore the shared
and unique structural features among related tasks. Efficient computation
requires that our transfer learning objective factors into local calculations,
which we prove is given by a broad class of transfer biases. Theoretically, we
show the efficiency of our approach. Empirically, we show that compared to
single task learning, transfer learning is better able to positively identify
true edges. We apply the method to whole-brain neuroimaging data.
Bridging Information Criteria and Parameter Shrinkage for Model Selection
Authors: Kun Zhang, Heng Peng, Laiwan Chan, Aapo Hyvarinen
Date: 2013-07-08 00:00:00
Tasks: Model Selection

Model selection based on classical information criteria, such as BIC, is
generally computationally demanding, but its properties are well studied. On
the other hand, model selection based on parameter shrinkage by $\ell_1$-type
penalties is computationally efficient. In this paper we make an attempt to
combine their strengths, and propose a simple approach that penalizes the
likelihood with data-dependent $\ell_1$ penalties as in adaptive Lasso and
exploits a fixed penalization parameter. Even for finite samples, its model
selection results approximately coincide with those based on information
criteria; in particular, we show that in some special cases, this approach and
the corresponding information criterion produce exactly the same model. One can
also consider this approach as a way to directly determine the penalization
parameter in adaptive Lasso to achieve information criteria-like model
selection. As extensions, we apply this idea to complex models including
Gaussian mixture model and mixture of factor analyzers, whose model selection
is traditionally difficult to do; by adopting suitable penalties, we provide
continuous approximators to the corresponding information criteria, which are
easy to optimize and enable efficient model selection.
Multiarmed Bandits With Limited Expert Advice
Authors: Satyen Kale
Date: 2013-06-19 00:00:00

We solve the COLT 2013 open problem of \citet{SCB} on minimizing regret in
the setting of advice-efficient multiarmed bandits with expert advice. We give
an algorithm for the setting of K arms and N experts out of which we are
allowed to query and use only M experts' advices in each round, which has a
regret bound of \tilde{O}\bigP{\sqrt{\frac{\min\{K, M\} N}{M} T}} after T
rounds. We also prove that any algorithm for this problem must have expected
regret at least \tilde{\Omega}\bigP{\sqrt{\frac{\min\{K, M\} N}{M}T}}, thus
showing that our upper bound is nearly tight.
Inconsistency and Accuracy of Heuristics with A* Search
Authors: Hang Dinh, Hieu Dinh
Date: 2013-07-08 00:00:00
Tasks: Heuristic Search

Many studies in heuristic search suggest that the accuracy of the heuristic
used has a positive impact on improving the performance of the search. In
another direction, historical research perceives that the performance of
heuristic search algorithms, such as A* and IDA*, can be improved by requiring
the heuristics to be consistent -- a property satisfied by any perfect
heuristic. However, a few recent studies show that inconsistent heuristics can
also be used to achieve a large improvement in these heuristic search
algorithms. These results leave us a natural question: which property of
heuristics, accuracy or consistency/inconsistency, should we focus on when
building heuristics? While there are studies on the heuristic accuracy with the
assumption of consistency, no studies on both the inconsistency and the
accuracy of heuristics are known to our knowledge.
  In this study, we investigate the relationship between the inconsistency and
the accuracy of heuristics with A* search. Our analytical result reveals a
correlation between these two properties. We then run experiments on the domain
for the Knapsack problem with a family of practical heuristics. Our empirical
results show that in many cases, the more accurate heuristics also have higher
level of inconsistency and result in fewer node expansions by A*.
A Knowledge-based Treatment of Human-Automation Systems
Authors: Yoram Moses, Marcia K. Shamo
Date: 2013-07-08 00:00:00

In a supervisory control system the human agent knowledge of past, current,
and future system behavior is critical for system performance. Being able to
reason about that knowledge in a precise and structured manner is central to
effective system design. In this paper we introduce the application of a
well-established formal approach to reasoning about knowledge to the modeling
and analysis of complex human-automation systems. An intuitive notion of
knowledge in human-automation systems is sketched and then cast as a formal
model. We present a case study in which the approach is used to model and
reason about a classic problem from the human-automation systems literature;
the results of our analysis provide evidence for the validity and value of
reasoning about complex systems in terms of the knowledge of the system agents.
To conclude, we discuss research directions that will extend this approach, and
note several systems in the aviation and human-robot team domains that are of
particular interest.
Transmodal Analysis of Neural Signals
Authors: Yaroslav O. Halchenko, Michael Hanke, James V. Haxby, Stephen Jose Hanson, Christoph S. Herrmann
Date: 2013-07-08 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Localizing neuronal activity in the brain, both in time and in space, is a
central challenge to advance the understanding of brain function. Because of
the inability of any single neuroimaging techniques to cover all aspects at
once, there is a growing interest to combine signals from multiple modalities
in order to benefit from the advantages of each acquisition method. Due to the
complexity and unknown parameterization of any suggested complete model of BOLD
response in functional magnetic resonance imaging (fMRI), the development of a
reliable ultimate fusion approach remains difficult. But besides the primary
goal of superior temporal and spatial resolution, conjoint analysis of data
from multiple imaging modalities can alternatively be used to segregate neural
information from physiological and acquisition noise. In this paper we suggest
a novel methodology which relies on constructing a quantifiable mapping of data
from one modality (electroencephalography; EEG) into another (fMRI), called
transmodal analysis of neural signals (TRANSfusion). TRANSfusion attempts to
map neural data embedded within the EEG signal into its reflection in fMRI
data. Assessing the mapping performance on unseen data allows to localize brain
areas where a significant portion of the signal could be reliably
reconstructed, hence the areas neural activity of which is reflected in both
EEG and fMRI data. Consecutive analysis of the learnt model allows to localize
areas associated with specific frequency bands of EEG, or areas functionally
related (connected or coherent) to any given EEG sensor. We demonstrate the
performance of TRANSfusion on artificial and real data from an auditory
experiment. We further speculate on possible alternative uses: cross-modal data
filtering and EEG-driven interpolation of fMRI signals to obtain arbitrarily
high temporal sampling of BOLD.
Differential Contrastive Divergence
Authors: David McAllester
Date: 2009-03-13 00:00:00

This paper has been retracted.
A PAC-Bayesian Tutorial with A Dropout Bound
Authors: David McAllester
Date: 2013-07-08 00:00:00
Tasks: Generalization Bounds

This tutorial gives a concise overview of existing PAC-Bayesian theory
focusing on three generalization bounds. The first is an Occam bound which
handles rules with finite precision parameters and which states that
generalization loss is near training loss when the number of bits needed to
write the rule is small compared to the sample size. The second is a
PAC-Bayesian bound providing a generalization guarantee for posterior
distributions rather than for individual rules. The PAC-Bayesian bound
naturally handles infinite precision rule parameters, $L_2$ regularization,
{\em provides a bound for dropout training}, and defines a natural notion of a
single distinguished PAC-Bayesian posterior distribution. The third bound is a
training-variance bound --- a kind of bias-variance analysis but with bias
replaced by expected training loss. The training-variance bound dominates the
other bounds but is more difficult to interpret. It seems to suggest variance
reduction methods such as bagging and may ultimately provide a more meaningful
analysis of dropouts.
Finding the creatures of habit; Clustering households based on their flexibility in using electricity
Authors: Ian Dent, Tony Craig, Uwe Aickelin, Tom Rodden
Date: 2013-07-08 00:00:00
Tasks: Clustering

Changes in the UK electricity market, particularly with the roll out of smart
meters, will provide greatly increased opportunities for initiatives intended
to change households' electricity usage patterns for the benefit of the overall
system. Users show differences in their regular behaviours and clustering
households into similar groupings based on this variability provides for
efficient targeting of initiatives. Those people who are stuck into a regular
pattern of activity may be the least receptive to an initiative to change
behaviour. A sample of 180 households from the UK are clustered into four
groups as an initial test of the concept and useful, actionable groupings are
found.
An investigation into the relationship between type-2 FOU size and environmental uncertainty in robotic control
Authors: Naisan Benatar, Uwe Aickelin, Jonathan M. Garibaldi
Date: 2013-07-04 00:00:00
Tasks: Vocal Bursts Type Prediction

It has been suggested that, when faced with large amounts of uncertainty in
situations of automated control, type-2 fuzzy logic based controllers will
out-perform the simpler type-1 varieties due to the latter lacking the
flexibility to adapt accordingly. This paper aims to investigate this problem
in detail in order to analyse when a type-2 controller will improve upon type-1
performance. A robotic sailing boat is subjected to several experiments in
which the uncertainty and difficulty of the sailing problem is increased in
order to observe the effects on measured performance. Improved performance is
observed but not in every case. The size of the FOU is shown to be have a large
effect on performance with potentially severe performance penalties for
incorrectly sized footprints.
The most controversial topics in Wikipedia: A multilingual and geographical analysis
Authors: Taha Yasseri, Anselm Spoerri, Mark Graham, János Kertész
Date: 2013-05-23 00:00:00
Tasks: Articles

We present, visualize and analyse the similarities and differences between
the controversial topics related to "edit wars" identified in 10 different
language versions of Wikipedia. After a brief review of the related work we
describe the methods developed to locate, measure, and categorize the
controversial topics in the different languages. Visualizations of the degree
of overlap between the top 100 lists of most controversial articles in
different languages and the content related to geographical locations will be
presented. We discuss what the presented analysis and visualizations can tell
us about the multicultural aspects of Wikipedia and practices of
peer-production. Our results indicate that Wikipedia is more than just an
encyclopaedia; it is also a window into convergent and divergent social-spatial
priorities, interests and preferences.