pwc-archive/papers-with-abstracts huggingface dataset:
Robust Non-linear Regression: A Greedy Approach Employing Kernels with Application to Image Denoising
Authors: George Papageorgiou, Pantelis Bouboulis, Sergios Theodoridis
Date: 2016-01-04 00:00:00
Tasks: Denoising, Image Denoising, regression

We consider the task of robust non-linear regression in the presence of both
inlier noise and outliers. Assuming that the unknown non-linear function
belongs to a Reproducing Kernel Hilbert Space (RKHS), our goal is to estimate
the set of the associated unknown parameters. Due to the presence of outliers,
common techniques such as the Kernel Ridge Regression (KRR) or the Support
Vector Regression (SVR) turn out to be inadequate. Instead, we employ sparse
modeling arguments to explicitly model and estimate the outliers, adopting a
greedy approach. The proposed robust scheme, i.e., Kernel Greedy Algorithm for
Robust Denoising (KGARD), is inspired by the classical Orthogonal Matching
Pursuit (OMP) algorithm. Specifically, the proposed method alternates between a
KRR task and an OMP-like selection step. Theoretical results concerning the
identification of the outliers are provided. Moreover, KGARD is compared
against other cutting edge methods, where its performance is evaluated via a
set of experiments with various types of noise. Finally, the proposed robust
estimation framework is applied to the task of image denoising, and its
enhanced performance in the presence of outliers is demonstrated.
To Swap or Not to Swap? Exploiting Dependency Word Pairs for Reordering in Statistical Machine Translation
Authors: Christian Hadiwinoto, Yang Liu, Hwee Tou Ng
Date: 2016-08-03 00:00:00
Tasks: Machine Translation, Sentence, Translation

Reordering poses a major challenge in machine translation (MT) between two
languages with significant differences in word order. In this paper, we present
a novel reordering approach utilizing sparse features based on dependency word
pairs. Each instance of these features captures whether two words, which are
related by a dependency link in the source sentence dependency parse tree,
follow the same order or are swapped in the translation output. Experiments on
Chinese-to-English translation show a statistically significant improvement of
1.21 BLEU point using our approach, compared to a state-of-the-art statistical
MT system that incorporates prior reordering approaches.
Learning Common and Specific Features for RGB-D Semantic Segmentation with Deconvolutional Networks
Authors: Jinghua Wang, Zhenhua Wang, DaCheng Tao, Simon See, Gang Wang
Date: 2016-08-03 00:00:00
Tasks: Segmentation, Semantic Segmentation

In this paper, we tackle the problem of RGB-D semantic segmentation of indoor
images. We take advantage of deconvolutional networks which can predict
pixel-wise class labels, and develop a new structure for deconvolution of
multiple modalities. We propose a novel feature transformation network to
bridge the convolutional networks and deconvolutional networks. In the feature
transformation network, we correlate the two modalities by discovering common
features between them, as well as characterize each modality by discovering
modality specific features. With the common features, we not only closely
correlate the two modalities, but also allow them to borrow features from each
other to enhance the representation of shared information. With specific
features, we capture the visual patterns that are only visible in one modality.
The proposed network achieves competitive segmentation accuracy on NYU depth
dataset V1 and V2.
Challenges in video based object detection in maritime scenario using computer vision
Authors: D. K. Prasad, C. K. Prasath, D. Rajan, L. Rachmawati, E. Rajabaly, C. Quek
Date: 2016-08-03 00:00:00
Tasks: object-detection, Object Detection

This paper discusses the technical challenges in maritime image processing
and machine vision problems for video streams generated by cameras. Even well
documented problems of horizon detection and registration of frames in a video
are very challenging in maritime scenarios. More advanced problems of
background subtraction and object detection in video streams are very
challenging. Challenges arising from the dynamic nature of the background,
unavailability of static cues, presence of small objects at distant
backgrounds, illumination effects, all contribute to the challenges as
discussed here.
FPGA system for real-time computational extended depth of field imaging using phase aperture coding
Authors: Tal Remez, Or Litany, Shachar Yoseff, Harel Haim, Alex Bronstein
Date: 2016-08-03 00:00:00
Tasks: Deblurring

We present a proof-of-concept end-to-end system for computational extended
depth of field (EDOF) imaging. The acquisition is performed through a
phase-coded aperture implemented by placing a thin wavelength-dependent optical
mask inside the pupil of a conventional camera lens, as a result of which, each
color channel is focused at a different depth. The reconstruction process
receives the raw Bayer image as the input, and performs blind estimation of the
output color image in focus at an extended range of depths using a patch-wise
sparse prior. We present a fast non-iterative reconstruction algorithm
operating with constant latency in fixed-point arithmetics and achieving
real-time performance in a prototype FPGA implementation. The output of the
system, on simulated and real-life scenes, is qualitatively and quantitatively
better than the result of clear-aperture imaging followed by state-of-the-art
blind deblurring.
Fuzzy c-Shape: A new algorithm for clustering finite time series waveforms
Authors: Fateme Fahiman, Jame C. Bezdek, Sarah M. Erfani, Christopher Leckie, Marimuthu Palaniswami
Date: 2016-08-03 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

The existence of large volumes of time series data in many applications has
motivated data miners to investigate specialized methods for mining time series
data. Clustering is a popular data mining method due to its powerful
exploratory nature and its usefulness as a preprocessing step for other data
mining techniques. This article develops two novel clustering algorithms for
time series data that are extensions of a crisp c-shapes algorithm. The two new
algorithms are heuristic derivatives of fuzzy c-means (FCM). Fuzzy c-Shapes
plus (FCS+) replaces the inner product norm in the FCM model with a shape-based
distance function. Fuzzy c-Shapes double plus (FCS++) uses the shape-based
distance, and also replaces the FCM cluster centers with shape-extracted
prototypes. Numerical experiments on 48 real time series data sets show that
the two new algorithms outperform state-of-the-art shape-based clustering
algorithms in terms of accuracy and efficiency. Four external cluster validity
indices (the Rand index, Adjusted Rand Index, Variation of Information, and
Normalized Mutual Information) are used to match candidate partitions generated
by each of the studied algorithms. All four indices agree that for these finite
waveform data sets, FCS++ gives a small improvement over FCS+, and in turn,
FCS+ is better than the original crisp c-shapes method. Finally, we apply two
tests of statistical significance to the three algorithms. The Wilcoxon and
Friedman statistics both rank the three algorithms in exactly the same way as
the four cluster validity indices.
DeepWriter: A Multi-Stream Deep CNN for Text-independent Writer Identification
Authors: Linjie Xing, Yu Qiao
Date: 2016-06-21 00:00:00
Tasks: Data Augmentation, Sentence, Task 2

Text-independent writer identification is challenging due to the huge
variation of written contents and the ambiguous written styles of different
writers. This paper proposes DeepWriter, a deep multi-stream CNN to learn deep
powerful representation for recognizing writers. DeepWriter takes local
handwritten patches as input and is trained with softmax classification loss.
The main contributions are: 1) we design and optimize multi-stream structure
for writer identification task; 2) we introduce data augmentation learning to
enhance the performance of DeepWriter; 3) we introduce a patch scanning
strategy to handle text image with different lengths. In addition, we find that
different languages such as English and Chinese may share common features for
writer identification, and joint training can yield better performance.
Experimental results on IAM and HWDB datasets show that our models achieve high
identification accuracy: 99.01% on 301 writers and 97.03% on 657 writers with
one English sentence input, 93.85% on 300 writers with one Chinese character
input, which outperform previous methods with a large margin. Moreover, our
models obtain accuracy of 98.01% on 301 writers with only 4 English alphabets
as input.
Keyphrase Extraction using Sequential Labeling
Authors: Sujatha Das Gollapalli, Xiao-Li Li
Date: 2016-08-01 00:00:00
Tasks: Keyphrase Extraction, POS, Retrieval

Keyphrases efficiently summarize a document's content and are used in various
document processing and retrieval tasks. Several unsupervised techniques and
classifiers exist for extracting keyphrases from text documents. Most of these
methods operate at a phrase-level and rely on part-of-speech (POS) filters for
candidate phrase generation. In addition, they do not directly handle
keyphrases of varying lengths. We overcome these modeling shortcomings by
addressing keyphrase extraction as a sequential labeling task in this paper. We
explore a basic set of features commonly used in NLP tasks as well as
predictions from various unsupervised methods to train our taggers. In addition
to a more natural modeling for the keyphrase extraction problem, we show that
tagging models yield significant performance benefits over existing
state-of-the-art extraction methods.
Combining Random Walks and Nonparametric Bayesian Topic Model for Community Detection
Authors: Ruimin Zhu, Wenxin Jiang
Date: 2016-07-19 00:00:00
Tasks: Community Detection, Stochastic Block Model, Variational Inference

Community detection has been an active research area for decades. Among all
probabilistic models, Stochastic Block Model has been the most popular one.
This paper introduces a novel probabilistic model: RW-HDP, based on random
walks and Hierarchical Dirichlet Process, for community extraction. In RW-HDP,
random walks conducted in a social network are treated as documents; nodes are
treated as words. By using Hierarchical Dirichlet Process, a nonparametric
Bayesian model, we are not only able to cluster nodes into different
communities, but also determine the number of communities automatically. We use
Stochastic Variational Inference for our model inference, which makes our
method time efficient and can be easily extended to an online learning
algorithm.
Empirical Evaluation of Real World Tournaments
Authors: Nicholas Mattei, Toby Walsh
Date: 2016-08-03 00:00:00

Computational Social Choice (ComSoc) is a rapidly developing field at the
intersection of computer science, economics, social choice, and political
science. The study of tournaments is fundamental to ComSoc and many results
have been published about tournament solution sets and reasoning in
tournaments. Theoretical results in ComSoc tend to be worst case and tell us
little about performance in practice. To this end we detail some experiments on
tournaments using real wold data from soccer and tennis. We make three main
contributions to the understanding of tournaments using real world data from
English Premier League, the German Bundesliga, and the ATP World Tour: (1) we
find that the NP-hard question of finding a seeding for which a given team can
win a tournament is easily solvable in real world instances, (2) using detailed
and principled methodology from statistical physics we show that our real world
data obeys a log-normal distribution; and (3) leveraging our log-normal
distribution result and using robust statistical methods, we show that the
popular Condorcet Random (CR) tournament model does not generate realistic
tournament data.
Modelling Temporal Information Using Discrete Fourier Transform for Recognizing Emotions in User-generated Videos
Authors: Haimin Zhang, Min Xu
Date: 2016-03-20 00:00:00
Tasks: Emotion Classification, Emotion Recognition, Video Emotion Recognition

With the widespread of user-generated Internet videos, emotion recognition in
those videos attracts increasing research efforts. However, most existing works
are based on framelevel visual features and/or audio features, which might fail
to model the temporal information, e.g. characteristics accumulated along time.
In order to capture video temporal information, in this paper, we propose to
analyse features in frequency domain transformed by discrete Fourier transform
(DFT features). Frame-level features are firstly extract by a pre-trained deep
convolutional neural network (CNN). Then, time domain features are transferred
and interpolated into DFT features. CNN and DFT features are further encoded
and fused for emotion classification. By this way, static image features
extracted from a pre-trained deep CNN and temporal information represented by
DFT features are jointly considered for video emotion recognition. Experimental
results demonstrate that combining DFT features can effectively capture
temporal information and therefore improve emotion recognition performance. Our
approach has achieved a state-of-the-art performance on the largest video
emotion dataset (VideoEmotion-8 dataset), improving accuracy from 51.1% to
62.6%.
A Stochastic Temporal Model of Polyphonic MIDI Performance with Ornaments
Authors: Eita Nakamura, Nobutaka Ono, Shigeki Sagayama, Kenji Watanabe
Date: 2014-04-08 00:00:00

We study indeterminacies in realization of ornaments and how they can be
incorporated in a stochastic performance model applicable for music information
processing such as score-performance matching. We point out the importance of
temporal information, and propose a hidden Markov model which describes it
explicitly and represents ornaments with several state types. Following a
review of the indeterminacies, they are carefully incorporated into the model
through its topology and parameters, and the state construction for quite
general polyphonic scores is explained in detail. By analyzing piano
performance data, we find significant overlaps in inter-onset-interval
distributions of chordal notes, ornaments, and inter-chord events, and the data
is used to determine details of the model. The model is applied for score
following and offline score-performance matching, yielding highly accurate
matching for performances with many ornaments and relatively frequent errors,
repeats, and skips.
Homotopy relations for digital images
Authors: Laurence Boxer, P. Christopher Staecker
Date: 2015-09-22 00:00:00

We introduce three generalizations of homotopy equivalence in digital images,
to allow us to express whether a finite and an infinite digital image are
similar with respect to homotopy.
  We show that these three generalizations are not equivalent to ordinary
homotopy equivalence, and give several examples. We show that, like homotopy
equivalence, our three generalizations imply isomorphism of fundamental groups,
and are preserved under wedges and Cartesian products.
One-Class Slab Support Vector Machine
Authors: Victor Fragoso, Walter Scheirer, Joao Hespanha, Matthew Turk
Date: 2016-08-02 00:00:00
Tasks: One-class classifier

This work introduces the one-class slab SVM (OCSSVM), a one-class classifier
that aims at improving the performance of the one-class SVM. The proposed
strategy reduces the false positive rate and increases the accuracy of
detecting instances from novel classes. To this end, it uses two parallel
hyperplanes to learn the normal region of the decision scores of the target
class. OCSSVM extends one-class SVM since it can scale and learn non-linear
decision functions via kernel methods. The experiments on two publicly
available datasets show that OCSSVM can consistently outperform the one-class
SVM and perform comparable to or better than other state-of-the-art one-class
classifiers.
Incremental Real-Time Multibody VSLAM with Trajectory Optimization Using Stereo Camera
Authors: N. Dinesh Reddy, Iman Abbasnejad, Sheetal Reddy, Amit Kumar Mondal, Vindhya Devalla
Date: 2016-08-02 00:00:00
Tasks: Motion Segmentation

Real time outdoor navigation in highly dynamic environments is an crucial
problem. The recent literature on real time static SLAM don't scale up to
dynamic outdoor environments. Most of these methods assume moving objects as
outliers or discard the information provided by them. We propose an algorithm
to jointly infer the camera trajectory and the moving object trajectory
simultaneously. In this paper, we perform a sparse scene flow based motion
segmentation using a stereo camera. The segmented objects motion models are
used for accurate localization of the camera trajectory as well as the moving
objects. We exploit the relationship between moving objects for improving the
accuracy of the poses. We formulate the poses as a factor graph incorporating
all the constraints. We achieve exact incremental solution by solving a full
nonlinear optimization problem in real time. The evaluation is performed on the
challenging KITTI dataset with multiple moving cars.Our method outperforms the
previous baselines in outdoor navigation.
Continuation semantics for multi-quantifier sentences: operation-based approaches
Authors: Justyna Grudzinska, Marek Zawadowski
Date: 2016-07-31 00:00:00
Tasks: Attribute, Semantic Composition

Classical scope-assignment strategies for multi-quantifier sentences involve
quantifier phrase (QP)-movement. More recent continuation-based approaches
provide a compelling alternative, for they interpret QP's in situ - without
resorting to Logical Forms or any structures beyond the overt syntax. The
continuation-based strategies can be divided into two groups: those that locate
the source of scope-ambiguity in the rules of semantic composition and those
that attribute it to the lexical entries for the quantifier words. In this
paper, we focus on the former operation-based approaches and the nature of the
semantic operations involved. More specifically, we discuss three such possible
operation-based strategies for multi-quantifier sentences, together with their
relative merits and costs.
Proceedings of the 2016 Workshop on Semantic Spaces at the Intersection of NLP, Physics and Cognitive Science
Authors: Dimitrios Kartsaklis, Martha Lewis, Laura Rimell
Date: 2016-08-02 00:00:00
Tasks: Sentence

This volume contains the Proceedings of the 2016 Workshop on Semantic Spaces
at the Intersection of NLP, Physics and Cognitive Science (SLPCS 2016), which
was held on the 11th of June at the University of Strathclyde, Glasgow, and was
co-located with Quantum Physics and Logic (QPL 2016). Exploiting the common
ground provided by the concept of a vector space, the workshop brought together
researchers working at the intersection of Natural Language Processing (NLP),
cognitive science, and physics, offering them an appropriate forum for
presenting their uniquely motivated work and ideas. The interplay between these
three disciplines inspired theoretically motivated approaches to the
understanding of how word meanings interact with each other in sentences and
discourse, how diagrammatic reasoning depicts and simplifies this interaction,
how language models are determined by input from the world, and how word and
sentence meanings interact logically. This first edition of the workshop
consisted of three invited talks from distinguished speakers (Hans Briegel,
Peter G\"ardenfors, Dominic Widdows) and eight presentations of selected
contributed papers. Each submission was refereed by at least three members of
the Programme Committee, who delivered detailed and insightful comments and
suggestions.
Automated X-ray Image Analysis for Cargo Security: Critical Review and Future Promise
Authors: Thomas W. Rogers, Nicolas Jaccard, Edward J. Morton, Lewis D. Griffin
Date: 2016-08-02 00:00:00
Tasks: Image Manipulation

We review the relatively immature field of automated image analysis for X-ray
cargo imagery. There is increasing demand for automated analysis methods that
can assist in the inspection and selection of containers, due to the
ever-growing volumes of traded cargo and the increasing concerns that customs-
and security-related threats are being smuggled across borders by organised
crime and terrorist networks. We split the field into the classical pipeline of
image preprocessing and image understanding. Preprocessing includes: image
manipulation; quality improvement; Threat Image Projection (TIP); and material
discrimination and segmentation. Image understanding includes: Automated Threat
Detection (ATD); and Automated Contents Verification (ACV). We identify several
gaps in the literature that need to be addressed and propose ideas for future
research. Where the current literature is sparse we borrow from the
single-view, multi-view, and CT X-ray baggage domains, which have some
characteristics in common with X-ray cargo.
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
Authors: Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, Ali Farhadi
Date: 2016-03-16 00:00:00
Tasks: Binarization, Classification with Binary Neural Network, General Classification

We propose two efficient approximations to standard convolutional neural
networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks,
the filters are approximated with binary values resulting in 32x memory saving.
In XNOR-Networks, both the filters and the input to convolutional layers are
binary. XNOR-Networks approximate convolutions using primarily binary
operations. This results in 58x faster convolutional operations and 32x memory
savings. XNOR-Nets offer the possibility of running state-of-the-art networks
on CPUs (rather than GPUs) in real-time. Our binary networks are simple,
accurate, efficient, and work on challenging visual tasks. We evaluate our
approach on the ImageNet classification task. The classification accuracy with
a Binary-Weight-Network version of AlexNet is only 2.9% less than the
full-precision AlexNet (in top-1 measure). We compare our method with recent
network binarization methods, BinaryConnect and BinaryNets, and outperform
these methods by large margins on ImageNet, more than 16% in top-1 accuracy.
Blocking Collapsed Gibbs Sampler for Latent Dirichlet Allocation Models
Authors: Xin Zhang, Scott A. Sisson
Date: 2016-08-02 00:00:00
Tasks: Blocking

The latent Dirichlet allocation (LDA) model is a widely-used latent variable
model in machine learning for text analysis. Inference for this model typically
involves a single-site collapsed Gibbs sampling step for latent variables
associated with observations. The efficiency of the sampling is critical to the
success of the model in practical large scale applications. In this article, we
introduce a blocking scheme to the collapsed Gibbs sampler for the LDA model
which can, with a theoretical guarantee, improve chain mixing efficiency. We
develop two procedures, an O(K)-step backward simulation and an O(log K)-step
nested simulation, to directly sample the latent variables within each block.
We demonstrate that the blocking scheme achieves substantial improvements in
chain mixing compared to the state of the art single-site collapsed Gibbs
sampler. We also show that when the number of topics is over hundreds, the
nested-simulation blocking scheme can achieve a significant reduction in
computation time compared to the single-site sampler.