pwc-archive/papers-with-abstracts huggingface dataset:
Mathematical Language Processing: Automatic Grading and Feedback for Open Response Mathematical Questions
Authors: Andrew S. Lan, Divyanshu Vats, Andrew E. Waters, Richard G. Baraniuk
Date: 2015-01-18 00:00:00
Tasks: Clustering

While computer and communication technologies have provided effective means
to scale up many aspects of education, the submission and grading of
assessments such as homework assignments and tests remains a weak link. In this
paper, we study the problem of automatically grading the kinds of open response
mathematical questions that figure prominently in STEM (science, technology,
engineering, and mathematics) courses. Our data-driven framework for
mathematical language processing (MLP) leverages solution data from a large
number of learners to evaluate the correctness of their solutions, assign
partial-credit scores, and provide feedback to each learner on the likely
locations of any errors. MLP takes inspiration from the success of natural
language processing for text data and comprises three main steps. First, we
convert each solution to an open response mathematical question into a series
of numerical features. Second, we cluster the features from several solutions
to uncover the structures of correct, partially correct, and incorrect
solutions. We develop two different clustering approaches, one that leverages
generic clustering algorithms and one based on Bayesian nonparametrics. Third,
we automatically grade the remaining (potentially large number of) solutions
based on their assigned cluster and one instructor-provided grade per cluster.
As a bonus, we can track the cluster assignment of each step of a multistep
solution and determine when it departs from a cluster of correct solutions,
which enables us to indicate the likely locations of errors to learners. We
test and validate MLP on real-world MOOC data to demonstrate how it can
substantially reduce the human effort required in large-scale educational
platforms.
Deep Belief Nets for Topic Modeling
Authors: Lars Maaloe, Morten Arngren, Ole Winther
Date: 2015-01-18 00:00:00
Tasks: Collaborative Filtering, Retrieval

Applying traditional collaborative filtering to digital publishing is
challenging because user data is very sparse due to the high volume of
documents relative to the number of users. Content based approaches, on the
other hand, is attractive because textual content is often very informative. In
this paper we describe large-scale content based collaborative filtering for
digital publishing. To solve the digital publishing recommender problem we
compare two approaches: latent Dirichlet allocation (LDA) and deep belief nets
(DBN) that both find low-dimensional latent representations for documents.
Efficient retrieval can be carried out in the latent representation. We work
both on public benchmarks and digital media content provided by Issuu, an
online publishing platform. This article also comes with a newly developed deep
belief nets toolbox for topic modeling tailored towards performance evaluation
of the DBN model and comparisons to the LDA model.
Phrase Based Language Model For Statistical Machine Translation
Authors: Jia Xu, Geliang Chen
Date: 2015-01-18 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, model, speech-recognition, Speech Recognition, Translation

We consider phrase based Language Models (LM), which generalize the commonly
used word level models. Similar concept on phrase based LMs appears in speech
recognition, which is rather specialized and thus less suitable for machine
translation (MT). In contrast to the dependency LM, we first introduce the
exhaustive phrase-based LMs tailored for MT use. Preliminary experimental
results show that our approach outperform word based LMs with the respect to
perplexity and translation quality.
Information Theory and its Relation to Machine Learning
Authors: Bao-Gang Hu
Date: 2015-01-18 00:00:00
Tasks: BIG-bench Machine Learning, Position, Relation

In this position paper, I first describe a new perspective on machine
learning (ML) by four basic problems (or levels), namely, "What to learn?",
"How to learn?", "What to evaluate?", and "What to adjust?". The paper stresses
more on the first level of "What to learn?", or "Learning Target Selection".
Towards this primary problem within the four levels, I briefly review the
existing studies about the connection between information theoretical learning
(ITL [1]) and machine learning. A theorem is given on the relation between the
empirically-defined similarity measure and information measures. Finally, a
conjecture is proposed for pursuing a unified mathematical interpretation to
learning target selection.
Image classification by visual bag-of-words refinement and reduction
Authors: Zhiwu Lu, Li-Wei Wang, Ji-Rong Wen
Date: 2015-01-18 00:00:00
Tasks: Classification, Clustering, General Classification, image-classification, Image Classification

This paper presents a new framework for visual bag-of-words (BOW) refinement
and reduction to overcome the drawbacks associated with the visual BOW model
which has been widely used for image classification. Although very influential
in the literature, the traditional visual BOW model has two distinct drawbacks.
Firstly, for efficiency purposes, the visual vocabulary is commonly constructed
by directly clustering the low-level visual feature vectors extracted from
local keypoints, without considering the high-level semantics of images. That
is, the visual BOW model still suffers from the semantic gap, and thus may lead
to significant performance degradation in more challenging tasks (e.g. social
image classification). Secondly, typically thousands of visual words are
generated to obtain better performance on a relatively large image dataset. Due
to such large vocabulary size, the subsequent image classification may take
sheer amount of time. To overcome the first drawback, we develop a graph-based
method for visual BOW refinement by exploiting the tags (easy to access
although noisy) of social images. More notably, for efficient image
classification, we further reduce the refined visual BOW model to a much
smaller size through semantic spectral clustering. Extensive experimental
results show the promising performance of the proposed framework for visual BOW
refinement and reduction.
Pairwise Constraint Propagation on Multi-View Data
Authors: Zhiwu Lu, Li-Wei Wang
Date: 2015-01-18 00:00:00
Tasks: graph construction, Retrieval

This paper presents a graph-based learning approach to pairwise constraint
propagation on multi-view data. Although pairwise constraint propagation has
been studied extensively, pairwise constraints are usually defined over pairs
of data points from a single view, i.e., only intra-view constraint propagation
is considered for multi-view tasks. In fact, very little attention has been
paid to inter-view constraint propagation, which is more challenging since
pairwise constraints are now defined over pairs of data points from different
views. In this paper, we propose to decompose the challenging inter-view
constraint propagation problem into semi-supervised learning subproblems so
that they can be efficiently solved based on graph-based label propagation. To
the best of our knowledge, this is the first attempt to give an efficient
solution to inter-view constraint propagation from a semi-supervised learning
viewpoint. Moreover, since graph-based label propagation has been adopted for
basic optimization, we develop two constrained graph construction methods for
interview constraint propagation, which only differ in how the intra-view
pairwise constraints are exploited. The experimental results in cross-view
retrieval have shown the promising performance of our inter-view constraint
propagation.
Regularized maximum correntropy machine
Authors: Jim Jing-Yan Wang, Yunji Wang, Bing-Yi Jing, Xin Gao
Date: 2015-01-18 00:00:00

In this paper we investigate the usage of regularized correntropy framework
for learning of classifiers from noisy labels. The class label predictors
learned by minimizing transitional loss functions are sensitive to the noisy
and outlying labels of training samples, because the transitional loss
functions are equally applied to all the samples. To solve this problem, we
propose to learn the class label predictors by maximizing the correntropy
between the predicted labels and the true labels of the training samples, under
the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we
regularize the predictor parameter to control the complexity of the predictor.
The learning problem is formulated by an objective function considering the
parameter regularization and MCC simultaneously. By optimizing the objective
function alternately, we develop a novel predictor learning algorithm. The
experiments on two chal- lenging pattern classification tasks show that it
significantly outperforms the machines with transitional loss functions.
Correntropy Induced L2 Graph for Robust Subspace Clustering
Authors: Canyi Lu, Jinhui Tang, Min Lin, Liang Lin, Shuicheng Yan, Zhouchen Lin
Date: 2015-01-18 00:00:00
Tasks: Clustering, graph construction

In this paper, we study the robust subspace clustering problem, which aims to
cluster the given possibly noisy data points into their underlying subspaces. A
large pool of previous subspace clustering methods focus on the graph
construction by different regularization of the representation coefficient. We
instead focus on the robustness of the model to non-Gaussian noises. We propose
a new robust clustering method by using the correntropy induced metric, which
is robust for handling the non-Gaussian and impulsive noises. Also we further
extend the method for handling the data with outlier rows/features. The
multiplicative form of half-quadratic optimization is used to optimize the
non-convex correntropy objective function of the proposed models. Extensive
experiments on face datasets well demonstrate that the proposed methods are
more robust to corruptions and occlusions.
Correlation Adaptive Subspace Segmentation by Trace Lasso
Authors: Canyi Lu, Jiashi Feng, Zhouchen Lin, Shuicheng Yan
Date: 2015-01-18 00:00:00
Tasks: Clustering, Segmentation

This paper studies the subspace segmentation problem. Given a set of data
points drawn from a union of subspaces, the goal is to partition them into
their underlying subspaces they were drawn from. The spectral clustering method
is used as the framework. It requires to find an affinity matrix which is close
to block diagonal, with nonzero entries corresponding to the data point pairs
from the same subspace. In this work, we argue that both sparsity and the
grouping effect are important for subspace segmentation. A sparse affinity
matrix tends to be block diagonal, with less connections between data points
from different subspaces. The grouping effect ensures that the highly corrected
data which are usually from the same subspace can be grouped together. Sparse
Subspace Clustering (SSC), by using $\ell^1$-minimization, encourages sparsity
for data selection, but it lacks of the grouping effect. On the contrary,
Low-Rank Representation (LRR), by rank minimization, and Least Squares
Regression (LSR), by $\ell^2$-regularization, exhibit strong grouping effect,
but they are short in subset selection. Thus the obtained affinity matrix is
usually very sparse by SSC, yet very dense by LRR and LSR.
  In this work, we propose the Correlation Adaptive Subspace Segmentation
(CASS) method by using trace Lasso. CASS is a data correlation dependent method
which simultaneously performs automatic data selection and groups correlated
data together. It can be regarded as a method which adaptively balances SSC and
LSR. Both theoretical and experimental results show the effectiveness of CASS.
Implementation of Auto Monitoring and Short-Message-Service System via GSM Modem
Authors: Akilan Thangarajah, Buddhapala Wongkaew, Mongkol Ekpanyapong
Date: 2015-01-07 00:00:00

Auto-Monitoring and Short-Messaging-Service System is a real-time monitoring
system for any critical operational environments. It detects an undesired event
occurring in the environment, generates an alert with detailed message and
sends it to the user to prevent hazards. This system employs a Friendly ARM as
main controller while, sensors and terminals to interact with the real world. A
GSM network is utilized to bridge the communication between monitoring system
and user. This paper presents details of prototyping the system.
Generalised Random Forest Space Overview
Authors: Miron B. Kursa
Date: 2015-01-17 00:00:00

Assuming a view of the Random Forest as a special case of a nested ensemble
of interchangeable modules, we construct a generalisation space allowing one to
easily develop novel methods based on this algorithm. We discuss the role and
required properties of modules at each level, especially in context of some
already proposed RF generalisations.
Constructing the L2-Graph for Robust Subspace Learning and Subspace Clustering
Authors: Xi Peng, Zhiding Yu, Huajin Tang, Zhang Yi
Date: 2012-09-05 00:00:00
Tasks: Clustering, Image Clustering, Motion Segmentation

Under the framework of graph-based learning, the key to robust subspace
clustering and subspace learning is to obtain a good similarity graph that
eliminates the effects of errors and retains only connections between the data
points from the same subspace (i.e., intra-subspace data points). Recent works
achieve good performance by modeling errors into their objective functions to
remove the errors from the inputs. However, these approaches face the
limitations that the structure of errors should be known prior and a complex
convex problem must be solved. In this paper, we present a novel method to
eliminate the effects of the errors from the projection space (representation)
rather than from the input space. We first prove that $\ell_1$-, $\ell_2$-,
$\ell_{\infty}$-, and nuclear-norm based linear projection spaces share the
property of Intra-subspace Projection Dominance (IPD), i.e., the coefficients
over intra-subspace data points are larger than those over inter-subspace data
points. Based on this property, we introduce a method to construct a sparse
similarity graph, called L2-Graph. The subspace clustering and subspace
learning algorithms are developed upon L2-Graph. Experiments show that L2-Graph
algorithms outperform the state-of-the-art methods for feature extraction,
image clustering, and motion segmentation in terms of accuracy, robustness, and
time efficiency.
Second International Nurse Rostering Competition (INRC-II) --- Problem Description and Rules ---
Authors: Sara Ceschia, Nguyen Thi Thanh Dang, Patrick De Causmaecker, Stefaan Haspeslagh, Andrea Schaerf
Date: 2015-01-17 00:00:00
Tasks: All

In this paper, we provide all information to participate to the Second
International Nurse Rostering Competition (INRC-II). First, we describe the
problem formulation, which, differently from INRC-I, is a multi-stage
procedure. Second, we illustrate all the necessary infrastructure do be used
together with the participant's solver, including the testbed, the file
formats, and the validation/simulation tools. Finally, we state the rules of
the competition. All update-to-date information about the competition is
available at http://mobiz.vives.be/inrc2/.
Meaningful Objects Segmentation from SAR Images via A Multi-Scale Non-Local Active Contour Model
Authors: Gui-Song Xia, Gang Liu, Wen Yang
Date: 2015-01-17 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

The segmentation of synthetic aperture radar (SAR) images is a longstanding
yet challenging task, not only because of the presence of speckle, but also due
to the variations of surface backscattering properties in the images.
Tremendous investigations have been made to eliminate the speckle effects for
the segmentation of SAR images, while few work devotes to dealing with the
variations of backscattering coefficients in the images. In order to overcome
both the two difficulties, this paper presents a novel SAR image segmentation
method by exploiting a multi-scale active contour model based on the non-local
processing principle. More precisely, we first formulize the SAR segmentation
problem with an active contour model by integrating the non-local interactions
between pairs of patches inside and outside the segmented regions. Secondly, a
multi-scale strategy is proposed to speed up the non-local active contour
segmentation procedure and to avoid falling into local minimum for achieving
more accurate segmentation results. Experimental results on simulated and real
SAR images demonstrate the efficiency and feasibility of the proposed method:
it can not only achieve precise segmentations for images with heavy speckles
and non-local intensity variations, but also can be used for SAR images from
different types of sensors.
A Fast Fractal Image Compression Algorithm Using Predefined Values for Contrast Scaling
Authors: H. Miar Naimi, M. Salarian
Date: 2015-01-17 00:00:00
Tasks: Image Compression

In this paper a new fractal image compression algorithm is proposed in which
the time of encoding process is considerably reduced. The algorithm exploits a
domain pool reduction approach, along with using innovative predefined values
for contrast scaling factor, S, instead of scanning the parameter space [0,1].
Within this approach only domain blocks with entropies greater than a threshold
are considered. As a novel point, it is assumed that in each step of the
encoding process, the domain block with small enough distance shall be found
only for the range blocks with low activity (equivalently low entropy). This
novel point is used to find reasonable estimations of S, and use them in the
encoding process as predefined values, mentioned above. The algorithm has been
examined for some well-known images. This result shows that our proposed
algorithm considerably reduces the encoding time producing images that are
approximately the same in quality.
Multidimensional Digital Filters for Point-Target Detection in Cluttered Infrared Scenes
Authors: Hugh L. Kennedy
Date: 2014-08-12 00:00:00
Tasks: Optical Flow Estimation

A 3-D spatiotemporal prediction-error filter (PEF), is used to enhance
foreground/background contrast in (real and simulated) sensor image sequences.
Relative velocity is utilized to extract point-targets that would otherwise be
indistinguishable on spatial frequency alone. An optical-flow field is
generated using local estimates of the 3-D autocorrelation function via the
application of the fast Fourier transform (FFT) and inverse FFT. Velocity
estimates are then used to tune in a background-whitening PEF that is matched
to the motion and texture of the local background. Finite-impulse-response
(FIR) filters are designed and implemented in the frequency domain. An
analytical expression for the frequency response of velocity-tuned FIR filters,
of odd or even dimension, with an arbitrary delay in each dimension, is
derived.
Semi-Supervised Sparse Coding
Authors: Jim Jing-Yan Wang, Xin Gao
Date: 2013-11-26 00:00:00

Sparse coding approximates the data sample as a sparse linear combination of
some basic codewords and uses the sparse codes as new presentations. In this
paper, we investigate learning discriminative sparse codes by sparse coding in
a semi-supervised manner, where only a few training samples are labeled. By
using the manifold structure spanned by the data set of both labeled and
unlabeled samples and the constraints provided by the labels of the labeled
samples, we learn the variable class labels for all the samples. Furthermore,
to improve the discriminative ability of the learned sparse codes, we assume
that the class labels could be predicted from the sparse codes directly using a
linear classifier. By solving the codebook, sparse codes, class labels and
classifier parameters simultaneously in a unified objective function, we
develop a semi-supervised sparse coding algorithm. Experiments on two
real-world pattern recognition problems demonstrate the advantage of the
proposed methods over supervised sparse coding methods on partially labeled
data sets.
Stochastic Gradient Descent, Weighted Sampling, and the Randomized Kaczmarz algorithm
Authors: Deanna Needell, Nathan Srebro, Rachel Ward
Date: 2013-10-21 00:00:00
Proceeding: NeurIPS 2014 12

We obtain an improved finite-sample guarantee on the linear convergence of
stochastic gradient descent for smooth and strongly convex objectives,
improving from a quadratic dependence on the conditioning $(L/\mu)^2$ (where
$L$ is a bound on the smoothness and $\mu$ on the strong convexity) to a linear
dependence on $L/\mu$. Furthermore, we show how reweighting the sampling
distribution (i.e. importance sampling) is necessary in order to further
improve convergence, and obtain a linear dependence in the average smoothness,
dominating previous results. We also discuss importance sampling for SGD more
broadly and show how it can improve convergence also in other scenarios. Our
results are based on a connection we make between SGD and the randomized
Kaczmarz algorithm, which allows us to transfer ideas between the separate
bodies of literature studying each of the two methods. In particular, we recast
the randomized Kaczmarz algorithm as an instance of SGD, and apply our results
to prove its exponential convergence, but to the solution of a weighted least
squares problem rather than the original least squares problem. We then present
a modified Kaczmarz algorithm with partially biased sampling which does
converge to the original least squares solution with the same exponential
convergence rate.
Coevolutionary intransitivity in games: A landscape analysis
Authors: Hendrik Richter
Date: 2015-01-16 00:00:00

Intransitivity is supposed to be a main reason for deficits in coevolutionary
progress and inheritable superiority. Besides, coevolutionary dynamics is
characterized by interactions yielding subjective fitness, but aiming at
solutions that are superior with respect to an objective measurement. Such an
approximation of objective fitness may be, for instance, generalization
performance. In the paper a link between rating-- and ranking--based measures
of intransitivity and fitness landscapes that can address the dichotomy between
subjective and objective fitness is explored. The approach is illustrated by
numerical experiments involving a simple random game with continuously tunable
degree of randomness.
Stochastic Gradient Based Extreme Learning Machines For Online Learning of Advanced Combustion Engines
Authors: Vijay Manikandan Janakiraman, XuanLong Nguyen, Dennis Assanis
Date: 2015-01-16 00:00:00

In this article, a stochastic gradient based online learning algorithm for
Extreme Learning Machines (ELM) is developed (SG-ELM). A stability criterion
based on Lyapunov approach is used to prove both asymptotic stability of
estimation error and stability in the estimated parameters suitable for
identification of nonlinear dynamic systems. The developed algorithm not only
guarantees stability, but also reduces the computational demand compared to the
OS-ELM approach based on recursive least squares. In order to demonstrate the
effectiveness of the algorithm on a real-world scenario, an advanced combustion
engine identification problem is considered. The algorithm is applied to two
case studies: An online regression learning for system identification of a
Homogeneous Charge Compression Ignition (HCCI) Engine and an online
classification learning (with class imbalance) for identifying the dynamic
operating envelope of the HCCI Engine. The results indicate that the accuracy
of the proposed SG-ELM is comparable to that of the state-of-the-art but adds
stability and a reduction in computational effort.