pwc-archive/papers-with-abstracts huggingface dataset:
Automated Matchmaking to Improve Accuracy of Applicant Selection for University Education System
Authors: Oludayo O. Olugbara, Manish Joshi, Michael M. Modiba, Virendrakumar C. Bhavsar
Date: 2015-07-09 00:00:00
Tasks: Fairness

The accurate applicant selection for university education is imperative to
ensure fairness and optimal use of institutional resources. Although various
approaches are operational in tertiary educational institutions for selecting
applicants, a novel method of automated matchmaking is explored in the current
study. The method functions by matching a prospective students skills profile
to a programmes requisites profile.
  Empirical comparisons of the results, calculated by automated matchmaking and
two other selection methods, show matchmaking to be a viable alternative for
accurate selection of applicants. Matchmaking offers a unique advantage that it
neither requires data from other applicants nor compares applicants with each
other. Instead, it emphasises norms that define admissibility to a programme.
  We have proposed the use of technology to minimize the gap between students
aspirations, skill sets and course requirements. It is a solution to minimize
the number of students who get frustrated because of mismatched course
selection.
Generalized Video Deblurring for Dynamic Scenes
Authors: Tae Hyun Kim, Kyoung Mu Lee
Date: 2015-07-09 00:00:00
Proceeding: CVPR 2015 6
Tasks: Deblurring, Optical Flow Estimation, Video Deblurring

Several state-of-the-art video deblurring methods are based on a strong
assumption that the captured scenes are static. These methods fail to deblur
blurry videos in dynamic scenes. We propose a video deblurring method to deal
with general blurs inherent in dynamic scenes, contrary to other methods. To
handle locally varying and general blurs caused by various sources, such as
camera shake, moving objects, and depth variation in a scene, we approximate
pixel-wise kernel with bidirectional optical flows. Therefore, we propose a
single energy model that simultaneously estimates optical flows and latent
frames to solve our deblurring problem. We also provide a framework and
efficient solvers to optimize the energy model. By minimizing the proposed
energy function, we achieve significant improvements in removing blurs and
estimating accurate optical flows in blurry frames. Extensive experimental
results demonstrate the superiority of the proposed method in real and
challenging videos that state-of-the-art methods fail in either deblurring or
optical flow estimation.
Spaced seeds improve k-mer-based metagenomic classification
Authors: Karel Brinda, Maciej Sykulski, Gregory Kucherov
Date: 2015-02-22 00:00:00
Tasks: Classification, General Classification

Metagenomics is a powerful approach to study genetic content of environmental
samples that has been strongly promoted by NGS technologies. To cope with
massive data involved in modern metagenomic projects, recent tools [4, 39] rely
on the analysis of k-mers shared between the read to be classified and sampled
reference genomes. Within this general framework, we show in this work that
spaced seeds provide a significant improvement of classification accuracy as
opposed to traditional contiguous k-mers. We support this thesis through a
series a different computational experiments, including simulations of
large-scale metagenomic projects. Scripts and programs used in this study, as
well as supplementary material, are available from
http://github.com/gregorykucherov/spaced-seeds-for-metagenomics.
A Logic of Knowing How
Authors: Yanjing Wang
Date: 2015-05-25 00:00:00
Tasks: Philosophy, valid

In this paper, we propose a single-agent modal logic framework for reasoning
about goal-direct "knowing how" based on ideas from linguistics, philosophy,
modal logic and automated planning. We first define a modal language to express
"I know how to guarantee phi given psi" with a semantics not based on standard
epistemic models but labelled transition systems that represent the agent's
knowledge of his own abilities. A sound and complete proof system is given to
capture the valid reasoning patterns about "knowing how" where the most
important axiom suggests its compositional nature.
Learning Structured Ordinal Measures for Video based Face Recognition
Authors: Ran He, Tieniu Tan, Larry Davis, Zhenan Sun
Date: 2015-07-09 00:00:00
Tasks: Face Recognition

This paper presents a structured ordinal measure method for video-based face
recognition that simultaneously learns ordinal filters and structured ordinal
features. The problem is posed as a non-convex integer program problem that
includes two parts. The first part learns stable ordinal filters to project
video data into a large-margin ordinal space. The second seeks self-correcting
and discrete codes by balancing the projected data and a rank-one ordinal
matrix in a structured low-rank way. Unsupervised and supervised structures are
considered for the ordinal matrix. In addition, as a complement to hierarchical
structures, deep feature representations are integrated into our method to
enhance coding stability. An alternating minimization method is employed to
handle the discrete and low-rank constraints, yielding high-quality codes that
capture prior structures well. Experimental results on three commonly used face
video databases show that our method with a simple voting classifier can
achieve state-of-the-art recognition rates using fewer features and samples.
Intrinsic Non-stationary Covariance Function for Climate Modeling
Authors: Chintan A. Dalal, Vladimir Pavlovic, Robert E. Kopp
Date: 2015-07-09 00:00:00
Tasks: regression

Designing a covariance function that represents the underlying correlation is
a crucial step in modeling complex natural systems, such as climate models.
Geospatial datasets at a global scale usually suffer from non-stationarity and
non-uniformly smooth spatial boundaries. A Gaussian process regression using a
non-stationary covariance function has shown promise for this task, as this
covariance function adapts to the variable correlation structure of the
underlying distribution. In this paper, we generalize the non-stationary
covariance function to address the aforementioned global scale geospatial
issues. We define this generalized covariance function as an intrinsic
non-stationary covariance function, because it uses intrinsic statistics of the
symmetric positive definite matrices to represent the characteristic length
scale and, thereby, models the local stochastic process. Experiments on a
synthetic and real dataset of relative sea level changes across the world
demonstrate improvements in the error metrics for the regression estimates
using our newly proposed approach.
The Shadows of a Cycle Cannot All Be Paths
Authors: Prosenjit Bose, Jean-Lou De Carufel, Michael G. Dobbins, Heuna Kim, Giovanni Viglietta
Date: 2015-07-09 00:00:00
Tasks: All

A "shadow" of a subset $S$ of Euclidean space is an orthogonal projection of
$S$ into one of the coordinate hyperplanes. In this paper we show that it is
not possible for all three shadows of a cycle (i.e., a simple closed curve) in
$\mathbb R^3$ to be paths (i.e., simple open curves).
  We also show two contrasting results: the three shadows of a path in $\mathbb
R^3$ can all be cycles (although not all convex) and, for every $d\geq 1$,
there exists a $d$-sphere embedded in $\mathbb R^{d+2}$ whose $d+2$ shadows
have no holes (i.e., they deformation-retract onto a point).
Achieving Synergy in Cognitive Behavior of Humanoids via Deep Learning of Dynamic Visuo-Motor-Attentional Coordination
Authors: Jungsik Hwang, Minju Jung, Naveen Madapana, Jinhyung Kim, Minkyu Choi, Jun Tani
Date: 2015-07-09 00:00:00

The current study examines how adequate coordination among different
cognitive processes including visual recognition, attention switching, action
preparation and generation can be developed via learning of robots by
introducing a novel model, the Visuo-Motor Deep Dynamic Neural Network (VMDNN).
The proposed model is built on coupling of a dynamic vision network, a motor
generation network, and a higher level network allocated on top of these two.
The simulation experiments using the iCub simulator were conducted for
cognitive tasks including visual object manipulation responding to human
gestures. The results showed that synergetic coordination can be developed via
iterative learning through the whole network when spatio-temporal hierarchy and
temporal one can be self-organized in the visual pathway and in the motor
pathway, respectively, such that the higher level can manipulate them with
abstraction.
Neural Network Classifiers for Natural Food Products
Authors: Jaderick P. Pabico, Alona V. De Grano, Alan L. Zarsuela
Date: 2015-07-09 00:00:00

Two cheap, off-the-shelf machine vision systems (MVS), each using an
artificial neural network (ANN) as classifier, were developed, improved and
evaluated to automate the classification of tomato ripeness and acceptability
of eggs, respectively. Six thousand color images of human-graded tomatoes and
750 images of human-graded eggs were used to train, test, and validate several
multi-layered ANNs. The ANNs output the corresponding grade of the produce by
accepting as inputs the spectral patterns of the background-less image. In both
MVS, the ANN with the highest validation rate was automatically chosen by a
heuristic and its performance compared to that of the human graders'. Using the
validation set, the MVS correctly graded 97.00\% and 86.00\% of the tomato and
egg data, respectively. The human grader's, however, were measured to perform
at a daily average of 92.65\% and 72.67\% for tomato and egg grading,
respectively. This results show that an ANN-based MVS is a potential
alternative to manual grading.
An Extragradient-Based Alternating Direction Method for Convex Minimization
Authors: Tianyi Lin, Shiqian Ma, Shuzhong Zhang
Date: 2013-01-27 00:00:00

In this paper, we consider the problem of minimizing the sum of two convex
functions subject to linear linking constraints. The classical alternating
direction type methods usually assume that the two convex functions have
relatively easy proximal mappings. However, many problems arising from
statistics, image processing and other fields have the structure that while one
of the two functions has easy proximal mapping, the other function is smoothly
convex but does not have an easy proximal mapping. Therefore, the classical
alternating direction methods cannot be applied. To deal with the difficulty,
we propose in this paper an alternating direction method based on
extragradients. Under the assumption that the smooth function has a Lipschitz
continuous gradient, we prove that the proposed method returns an
$\epsilon$-optimal solution within $O(1/\epsilon)$ iterations. We apply the
proposed method to solve a new statistical model called fused logistic
regression. Our numerical experiments show that the proposed method performs
very well when solving the test problems. We also test the performance of the
proposed method through solving the lasso problem arising from statistics and
compare the result with several existing efficient solvers for this problem;
the results are very encouraging indeed.
Multisection in the Stochastic Block Model using Semidefinite Programming
Authors: Naman Agarwal, Afonso S. Bandeira, Konstantinos Koiliaris, Alexandra Kolla
Date: 2015-07-08 00:00:00
Tasks: Open-Ended Question Answering, Stochastic Block Model

We consider the problem of identifying underlying community-like structures
in graphs. Towards this end we study the Stochastic Block Model (SBM) on
$k$-clusters: a random model on $n=km$ vertices, partitioned in $k$ equal sized
clusters, with edges sampled independently across clusters with probability $q$
and within clusters with probability $p$, $p>q$. The goal is to recover the
initial "hidden" partition of $[n]$. We study semidefinite programming (SDP)
based algorithms in this context. In the regime $p = \frac{\alpha \log(m)}{m}$
and $q = \frac{\beta \log(m)}{m}$ we show that a certain natural SDP based
algorithm solves the problem of {\em exact recovery} in the $k$-community SBM,
with high probability, whenever $\sqrt{\alpha} - \sqrt{\beta} > \sqrt{1}$, as
long as $k=o(\log n)$. This threshold is known to be the information
theoretically optimal. We also study the case when $k=\theta(\log(n))$. In this
case however we achieve recovery guarantees that no longer match the optimal
condition $\sqrt{\alpha} - \sqrt{\beta} > \sqrt{1}$, thus leaving achieving
optimality for this range an open question.
End-to-end people detection in crowded scenes
Authors: Russell Stewart, Mykhaylo Andriluka
Date: 2015-06-16 00:00:00
Proceeding: CVPR 2016 6

Current people detectors operate either by scanning an image in a sliding
window fashion or by classifying a discrete set of proposals. We propose a
model that is based on decoding an image into a set of people detections. Our
system takes an image as input and directly outputs a set of distinct detection
hypotheses. Because we generate predictions jointly, common post-processing
steps such as non-maximum suppression are unnecessary. We use a recurrent LSTM
layer for sequence generation and train our model end-to-end with a new loss
function that operates on sets of detections. We demonstrate the effectiveness
of our approach on the challenging task of detecting people in crowded scenes.
Feature Representation in Convolutional Neural Networks
Authors: Ben Athiwaratkun, Keegan Kang
Date: 2015-07-08 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Convolutional Neural Networks (CNNs) are powerful models that achieve
impressive results for image classification. In addition, pre-trained CNNs are
also useful for other computer vision tasks as generic feature extractors. This
paper aims to gain insight into the feature aspect of CNN and demonstrate other
uses of CNN features. Our results show that CNN feature maps can be used with
Random Forests and SVM to yield classification results that outperforms the
original CNN. A CNN that is less than optimal (e.g. not fully trained or
overfitting) can also extract features for Random Forest/SVM that yield
competitive classification accuracy. In contrast to the literature which uses
the top-layer activations as feature representation of images for other tasks,
using lower-layer features can yield better results for classification.
A Hierarchical Recurrent Encoder-Decoder For Generative Context-Aware Query Suggestion
Authors: Alessandro Sordoni, Yoshua Bengio, Hossein Vahabi, Christina Lioma, Jakob G. Simonsen, Jian-Yun Nie
Date: 2015-07-08 00:00:00
Tasks: Decoder

Users may strive to formulate an adequate textual query for their information
need. Search engines assist the users by presenting query suggestions. To
preserve the original search intent, suggestions should be context-aware and
account for the previous queries issued by the user. Achieving context
awareness is challenging due to data sparsity. We present a probabilistic
suggestion model that is able to account for sequences of previous queries of
arbitrary lengths. Our novel hierarchical recurrent encoder-decoder
architecture allows the model to be sensitive to the order of queries in the
context while avoiding data sparsity. Additionally, our model can suggest for
rare, or long-tail, queries. The produced suggestions are synthetic and are
sampled one word at a time, using computationally cheap decoding techniques.
This is in contrast to current synthetic suggestion models relying upon machine
learning pipelines and hand-engineered feature sets. Results show that it
outperforms existing context-aware approaches in a next query prediction
setting. In addition to query suggestion, our model is general enough to be
used in a variety of other applications.
Intersecting Faces: Non-negative Matrix Factorization With New Guarantees
Authors: Rong Ge, James Zou
Date: 2015-07-08 00:00:00

Non-negative matrix factorization (NMF) is a natural model of admixture and
is widely used in science and engineering. A plethora of algorithms have been
developed to tackle NMF, but due to the non-convex nature of the problem, there
is little guarantee on how well these methods work. Recently a surge of
research have focused on a very restricted class of NMFs, called separable NMF,
where provably correct algorithms have been developed. In this paper, we
propose the notion of subset-separable NMF, which substantially generalizes the
property of separability. We show that subset-separability is a natural
necessary condition for the factorization to be unique or to have minimum
volume. We developed the Face-Intersect algorithm which provably and
efficiently solves subset-separable NMF under natural conditions, and we prove
that our algorithm is robust to small noise. We explored the performance of
Face-Intersect on simulations and discuss settings where it empirically
outperformed the state-of-art methods. Our work is a step towards finding
provably correct algorithms that solve large classes of NMF problems.
AutoCompete: A Framework for Machine Learning Competition
Authors: Abhishek Thakur, Artus Krohn-Grimberghe
Date: 2015-07-08 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we propose AutoCompete, a highly automated machine learning
framework for tackling machine learning competitions. This framework has been
learned by us, validated and improved over a period of more than two years by
participating in online machine learning competitions. It aims at minimizing
human interference required to build a first useful predictive model and to
assess the practical difficulty of a given machine learning challenge. The
proposed system helps in identifying data types, choosing a machine learn- ing
model, tuning hyper-parameters, avoiding over-fitting and optimization for a
provided evaluation metric. We also observe that the proposed system produces
better (or comparable) results with less runtime as compared to other
approaches.
Iris Recognition Using Scattering Transform and Textural Features
Authors: Shervin Minaee, Amirali Abdolrashidi, Yao Wang
Date: 2015-07-08 00:00:00
Tasks: Iris Recognition, Template Matching

Iris recognition has drawn a lot of attention since the mid-twentieth
century. Among all biometric features, iris is known to possess a rich set of
features. Different features have been used to perform iris recognition in the
past. In this paper, two powerful sets of features are introduced to be used
for iris recognition: scattering transform-based features and textural
features. PCA is also applied on the extracted features to reduce the
dimensionality of the feature vector while preserving most of the information
of its initial value. Minimum distance classifier is used to perform template
matching for each new test sample. The proposed scheme is tested on a
well-known iris database, and showed promising results with the best accuracy
rate of 99.2%.
Towards Good Practices for Very Deep Two-Stream ConvNets
Authors: Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao
Date: 2015-07-08 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, Computational Efficiency, Data Augmentation, GPU, Temporal Action Localization, Vocal Bursts Valence Prediction

Deep convolutional networks have achieved great success for object
recognition in still images. However, for action recognition in videos, the
improvement of deep convolutional networks is not so evident. We argue that
there are two reasons that could probably explain this result. First the
current network architectures (e.g. Two-stream ConvNets) are relatively shallow
compared with those very deep models in image domain (e.g. VGGNet, GoogLeNet),
and therefore their modeling capacity is constrained by their depth. Second,
probably more importantly, the training dataset of action recognition is
extremely small compared with the ImageNet dataset, and thus it will be easy to
over-fit on the training dataset.
  To address these issues, this report presents very deep two-stream ConvNets
for action recognition, by adapting recent very deep architectures into video
domain. However, this extension is not easy as the size of action recognition
is quite small. We design several good practices for the training of very deep
two-stream ConvNets, namely (i) pre-training for both spatial and temporal
nets, (ii) smaller learning rates, (iii) more data augmentation techniques,
(iv) high drop out ratio. Meanwhile, we extend the Caffe toolbox into Multi-GPU
implementation with high computational efficiency and low memory consumption.
We verify the performance of very deep two-stream ConvNets on the dataset of
UCF101 and it achieves the recognition accuracy of $91.4\%$.
Double-Base Asymmetric AdaBoost
Authors: Iago Landesa-Vázquez, José Luis Alba-Castro
Date: 2015-07-08 00:00:00

Based on the use of different exponential bases to define class-dependent
error bounds, a new and highly efficient asymmetric boosting scheme, coined as
AdaBoostDB (Double-Base), is proposed. Supported by a fully theoretical
derivation procedure, unlike most of the other approaches in the literature,
our algorithm preserves all the formal guarantees and properties of original
(cost-insensitive) AdaBoost, similarly to the state-of-the-art Cost-Sensitive
AdaBoost algorithm. However, the key advantage of AdaBoostDB is that our novel
derivation scheme enables an extremely efficient conditional search procedure,
dramatically improving and simplifying the training phase of the algorithm.
Experiments, both over synthetic and real datasets, reveal that AdaBoostDB is
able to save over 99% training time with regard to Cost-Sensitive AdaBoost,
providing the same cost-sensitive results. This computational advantage of
AdaBoostDB can make a difference in problems managing huge pools of weak
classifiers in which boosting techniques are commonly used.
SAR Imaging of Moving Target based on Knowledge-aided Two-dimensional Autofocus
Authors: Xinhua Mao
Date: 2015-07-08 00:00:00
Tasks: Computational Efficiency, parameter estimation

Due to uncertainty on target's motion, the range cell migration (RCM) and
azimuth phase error (APE) of moving targets can't be completely compensated in
synthetic aperture radar (SAR) processing. Therefore, moving targets often
appear two-dimensional (2-D) defocused in SAR images. In this paper, a 2-D
autofocus method for refocusing defocused moving targets in SAR images is
presented. The new method only requires a direct estimate of APE, while the
residual 2-D phase error ( or RCM) is computed from the estimated APE by
exploiting the analytical relationship between the 2-D phase error ( or RCM)
and APE. Because the parameter estimation is performed in the reduced-dimension
space by exploiting prior knowledge on phase error structure, the proposed
approach offers clear advantages in both computational efficiency and
estimation accuracy.
Spotlight the Negatives: A Generalized Discriminative Latent Model
Authors: Hossein Azizpour, Mostafa Arefiyan, Sobhan Naderi Parizi, Stefan Carlsson
Date: 2015-07-08 00:00:00

Discriminative latent variable models (LVM) are frequently applied to various
visual recognition tasks. In these systems the latent (hidden) variables
provide a formalism for modeling structured variation of visual features.
Conventionally, latent variables are de- fined on the variation of the
foreground (positive) class. In this work we augment LVMs to include negative
latent variables corresponding to the background class. We formalize the
scoring function of such a generalized LVM (GLVM). Then we discuss a framework
for learning a model based on the GLVM scoring function. We theoretically
showcase how some of the current visual recognition methods can benefit from
this generalization. Finally, we experiment on a generalized form of Deformable
Part Models with negative latent variables and show significant improvements on
two different detection tasks.