pwc-archive/papers-with-abstracts huggingface dataset:
Local Deep Neural Networks for Age and Gender Classification
Authors: Zukang Liao, Stavros Petridis, Maja Pantic
Date: 2017-03-24 00:00:00
Tasks: Age And Gender Classification, Age Classification, Classification, Gender Classification, General Classification

Local deep neural networks have been recently introduced for gender
recognition. Although, they achieve very good performance they are very
computationally expensive to train. In this work, we introduce a simplified
version of local deep neural networks which significantly reduces the training
time. Instead of using hundreds of patches per image, as suggested by the
original method, we propose to use 9 overlapping patches per image which cover
the entire face region. This results in a much reduced training time, since
just 9 patches are extracted per image instead of hundreds, at the expense of a
slightly reduced performance. We tested the proposed modified local deep neural
networks approach on the LFW and Adience databases for the task of gender and
age classification. For both tasks and both databases the performance is up to
1% lower compared to the original version of the algorithm. We have also
investigated which patches are more discriminative for age and gender
classification. It turns out that the mouth and eyes regions are useful for age
classification, whereas just the eye region is useful for gender
classification.
Content-Based Image Retrieval Based on Late Fusion of Binary and Local Descriptors
Authors: Nouman Ali, Danish Ali Mazhar, Zeshan Iqbal, Rehan Ashraf, Jawad Ahmed, Farrukh Zeeshan Khan
Date: 2017-03-24 00:00:00
Tasks: Content-Based Image Retrieval, Image Retrieval, Retrieval, Translation

One of the challenges in Content-Based Image Retrieval (CBIR) is to reduce
the semantic gaps between low-level features and high-level semantic concepts.
In CBIR, the images are represented in the feature space and the performance of
CBIR depends on the type of selected feature representation. Late fusion also
known as visual words integration is applied to enhance the performance of
image retrieval. The recent advances in image retrieval diverted the focus of
research towards the use of binary descriptors as they are reported
computationally efficient. In this paper, we aim to investigate the late fusion
of Fast Retina Keypoint (FREAK) and Scale Invariant Feature Transform (SIFT).
The late fusion of binary and local descriptor is selected because among binary
descriptors, FREAK has shown good results in classification-based problems
while SIFT is robust to translation, scaling, rotation and small distortions.
The late fusion of FREAK and SIFT integrates the performance of both feature
descriptors for an effective image retrieval. Experimental results and
comparisons show that the proposed late fusion enhances the performances of
image retrieval.
Vehicle Speed Detecting App
Authors: Itoro Ikon
Date: 2017-02-17 00:00:00
Tasks: Position

The report presents the measurement of vehicular speed using a smartphone
camera. The speed measurement is accomplished by detecting the position of the
vehicle on a camera frame using the LBP cascade classifier of OpenCV API, the
displacement of the detected vehicle with time is used to compute the speed.
Conversion coefficient is determined to map the pixel displacement to actual
vehicle distance. The speeds measured are proportional to the ground truth
speeds.
Long-Term Evolution of Genetic Programming Populations
Authors: W. B. Langdon
Date: 2017-03-24 00:00:00

We evolve binary mux-6 trees for up to 100000 generations evolving some
programs with more than a hundred million nodes. Our unbounded Long-Term
Evolution Experiment LTEE GP appears not to evolve building blocks but does
suggests a limit to bloat. We do see periods of tens even hundreds of
generations where the population is 100 percent functionally converged. The
distribution of tree sizes is not as predicted by theory.
Medical Image Retrieval using Deep Convolutional Neural Network
Authors: Adnan Qayyum, Syed Muhammad Anwar, Muhammad Awais, Muhammad Majid
Date: 2017-03-24 00:00:00
Tasks: Classification, General Classification, Image Retrieval, Medical Image Retrieval, Retrieval

With a widespread use of digital imaging data in hospitals, the size of
medical image repositories is increasing rapidly. This causes difficulty in
managing and querying these large databases leading to the need of content
based medical image retrieval (CBMIR) systems. A major challenge in CBMIR
systems is the semantic gap that exists between the low level visual
information captured by imaging devices and high level semantic information
perceived by human. The efficacy of such systems is more crucial in terms of
feature representations that can characterize the high-level information
completely. In this paper, we propose a framework of deep learning for CBMIR
system by using deep Convolutional Neural Network (CNN) that is trained for
classification of medical images. An intermodal dataset that contains twenty
four classes and five modalities is used to train the network. The learned
features and the classification results are used to retrieve medical images.
For retrieval, best results are achieved when class based predictions are used.
An average classification accuracy of 99.77% and a mean average precision of
0.69 is achieved for retrieval task. The proposed method is best suited to
retrieve multimodal medical images for different body organs.
Batch-normalized joint training for DNN-based distant speech recognition
Authors: Mirco Ravanelli, Philemon Brakel, Maurizio Omologo, Yoshua Bengio
Date: 2017-03-24 00:00:00
Tasks: Distant Speech Recognition, Speech Enhancement, speech-recognition, Speech Recognition

Improving distant speech recognition is a crucial step towards flexible
human-machine interfaces. Current technology, however, still exhibits a lack of
robustness, especially when adverse acoustic conditions are met. Despite the
significant progress made in the last years on both speech enhancement and
speech recognition, one potential limitation of state-of-the-art technology
lies in composing modules that are not well matched because they are not
trained jointly. To address this concern, a promising approach consists in
concatenating a speech enhancement and a speech recognition deep neural network
and to jointly update their parameters as if they were within a single bigger
network. Unfortunately, joint training can be difficult because the output
distribution of the speech enhancement system may change substantially during
the optimization procedure. The speech recognition module would have to deal
with an input distribution that is non-stationary and unnormalized. To mitigate
this issue, we propose a joint training approach based on a fully
batch-normalized architecture. Experiments, conducted using different datasets,
tasks and acoustic conditions, revealed that the proposed framework
significantly overtakes other competitive solutions, especially in challenging
environments.
K-Means Clustering using Tabu Search with Quantized Means
Authors: Kojo Sarfo Gyamfi, James Brusey, Andrew Hunt
Date: 2017-03-24 00:00:00
Tasks: Clustering, Efficient Exploration

The Tabu Search (TS) metaheuristic has been proposed for K-Means clustering
as an alternative to Lloyd's algorithm, which for all its ease of
implementation and fast runtime, has the major drawback of being trapped at
local optima. While the TS approach can yield superior performance, it involves
a high computational complexity. Moreover, the difficulty in parameter
selection in the existing TS approach does not make it any more attractive.
This paper presents an alternative, low-complexity formulation of the TS
optimization procedure for K-Means clustering. This approach does not require
many parameter settings. We initially constrain the centers to points in the
dataset. We then aim at evolving these centers using a unique neighborhood
structure that makes use of gradient information of the objective function.
This results in an efficient exploration of the search space, after which the
means are refined. The proposed scheme is implemented in MATLAB and tested on
four real-world datasets, and it achieves a significant improvement over the
existing TS approach in terms of the intra cluster sum of squares and
computational time.
A Regularization Approach to Blind Deblurring and Denoising of QR Barcodes
Authors: Yves van Gennip, Prashant Athavale, Jérôme Gilles, Rustum Choksi
Date: 2014-10-23 00:00:00
Tasks: Deblurring, Denoising

QR bar codes are prototypical images for which part of the image is a priori
known (required patterns). Open source bar code readers, such as ZBar, are
readily available. We exploit both these facts to provide and assess purely
regularization-based methods for blind deblurring of QR bar codes in the
presence of noise.
Linear classifier design under heteroscedasticity in Linear Discriminant Analysis
Authors: Kojo Sarfo Gyamfi, James Brusey, Andrew Hunt, Elena Gaura
Date: 2017-03-24 00:00:00
Tasks: Binary Classification, Classification, General Classification, Handwriting Recognition, Medical Diagnosis

Under normality and homoscedasticity assumptions, Linear Discriminant
Analysis (LDA) is known to be optimal in terms of minimising the Bayes error
for binary classification. In the heteroscedastic case, LDA is not guaranteed
to minimise this error. Assuming heteroscedasticity, we derive a linear
classifier, the Gaussian Linear Discriminant (GLD), that directly minimises the
Bayes error for binary classification. In addition, we also propose a local
neighbourhood search (LNS) algorithm to obtain a more robust classifier if the
data is known to have a non-normal distribution. We evaluate the proposed
classifiers on two artificial and ten real-world datasets that cut across a
wide range of application areas including handwriting recognition, medical
diagnosis and remote sensing, and then compare our algorithm against existing
LDA approaches and other linear classifiers. The GLD is shown to outperform the
original LDA procedure in terms of the classification accuracy under
heteroscedasticity. While it compares favourably with other existing
heteroscedastic LDA approaches, the GLD requires as much as 60 times lower
training time on some datasets. Our comparison with the support vector machine
(SVM) also shows that, the GLD, together with the LNS, requires as much as 150
times lower training time to achieve an equivalent classification accuracy on
some of the datasets. Thus, our algorithms can provide a cheap and reliable
option for classification in a lot of expert systems.
Calendar.help: Designing a Workflow-Based Scheduling Agent with Humans in the Loop
Authors: Justin Cranshaw, Emad Elwany, Todd Newman, Rafal Kocielnik, Bowen Yu, Sandeep Soni, Jaime Teevan, Andrés Monroy-Hernández
Date: 2017-03-24 00:00:00
Tasks: Scheduling

Although information workers may complain about meetings, they are an
essential part of their work life. Consequently, busy people spend a
significant amount of time scheduling meetings. We present Calendar.help, a
system that provides fast, efficient scheduling through structured workflows.
Users interact with the system via email, delegating their scheduling needs to
the system as if it were a human personal assistant. Common scheduling
scenarios are broken down using well-defined workflows and completed as a
series of microtasks that are automated when possible and executed by a human
otherwise. Unusual scenarios fall back to a trained human assistant who
executes them as unstructured macrotasks. We describe the iterative approach we
used to develop Calendar.help, and share the lessons learned from scheduling
thousands of meetings during a year of real-world deployments. Our findings
provide insight into how complex information tasks can be broken down into
repeatable components that can be executed efficiently to improve productivity.
Asymmetric Learning Vector Quantization for Efficient Nearest Neighbor Classification in Dynamic Time Warping Spaces
Authors: Brijnesh Jain, David Schultz
Date: 2017-03-24 00:00:00
Tasks: Classification, Dynamic Time Warping, General Classification, Quantization, Time Series, Time Series Analysis, Time Series Classification

The nearest neighbor method together with the dynamic time warping (DTW)
distance is one of the most popular approaches in time series classification.
This method suffers from high storage and computation requirements for large
training sets. As a solution to both drawbacks, this article extends learning
vector quantization (LVQ) from Euclidean spaces to DTW spaces. The proposed LVQ
scheme uses asymmetric weighted averaging as update rule. Empirical results
exhibited superior performance of asymmetric generalized LVQ (GLVQ) over other
state-of-the-art prototype generation methods for nearest neighbor
classification.
Inverse Reinforcement Learning in Swarm Systems
Authors: Adrian Šošić, Wasiur R. KhudaBukhsh, Abdelhak M. Zoubir, Heinz Koeppl
Date: 2016-02-17 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Inverse reinforcement learning (IRL) has become a useful tool for learning
behavioral models from demonstration data. However, IRL remains mostly
unexplored for multi-agent systems. In this paper, we show how the principle of
IRL can be extended to homogeneous large-scale problems, inspired by the
collective swarming behavior of natural systems. In particular, we make the
following contributions to the field: 1) We introduce the swarMDP framework, a
sub-class of decentralized partially observable Markov decision processes
endowed with a swarm characterization. 2) Exploiting the inherent homogeneity
of this framework, we reduce the resulting multi-agent IRL problem to a
single-agent one by proving that the agent-specific value functions in this
model coincide. 3) To solve the corresponding control problem, we propose a
novel heterogeneous learning scheme that is particularly tailored to the swarm
setting. Results on two example systems demonstrate that our framework is able
to produce meaningful local reward models from which we can replicate the
observed global system dynamics.
Reasoning by Cases in Structured Argumentation
Authors: Mathieu Beirlaen, Jesse Heyninck, Christian Straßer
Date: 2017-03-24 00:00:00

We extend the $ASPIC^+$ framework for structured argumentation so as to allow
applications of the reasoning by cases inference scheme for defeasible
arguments. Given an argument with conclusion `$A$ or $B$', an argument based on
$A$ with conclusion $C$, and an argument based on $B$ with conclusion $C$, we
allow the construction of an argument with conclusion $C$. We show how our
framework leads to different results than other approaches in non-monotonic
logic for dealing with disjunctive information, such as disjunctive default
theory or approaches based on the OR-rule (which allows to derive a defeasible
rule `If ($A$ or $B$) then $C$', given two defeasible rules `If $A$ then $C$'
and `If $B$ then $C$'). We raise new questions regarding the subtleties of
reasoning defeasibly with disjunctive information, and show that its
formalization is more intricate than one would presume.
Smart Augmentation - Learning an Optimal Data Augmentation Strategy
Authors: Joseph Lemley, Shabab Bazrafkan, Peter Corcoran
Date: 2017-03-24 00:00:00
Tasks: Data Augmentation, Transfer Learning

A recurring problem faced when training neural networks is that there is
typically not enough data to maximize the generalization capability of deep
neural networks(DNN). There are many techniques to address this, including data
augmentation, dropout, and transfer learning. In this paper, we introduce an
additional method which we call Smart Augmentation and we show how to use it to
increase the accuracy and reduce overfitting on a target network. Smart
Augmentation works by creating a network that learns how to generate augmented
data during the training process of a target network in a way that reduces that
networks loss. This allows us to learn augmentations that minimize the error of
that network.
  Smart Augmentation has shown the potential to increase accuracy by
demonstrably significant measures on all datasets tested. In addition, it has
shown potential to achieve similar or improved performance levels with
significantly smaller network sizes in a number of tested cases.
Feature Fusion using Extended Jaccard Graph and Stochastic Gradient Descent for Robot
Authors: Shenglan Liu, Muxin Sun, Wei Wang, Feilong Wang
Date: 2017-03-24 00:00:00

Robot vision is a fundamental device for human-robot interaction and robot
complex tasks. In this paper, we use Kinect and propose a feature graph fusion
(FGF) for robot recognition. Our feature fusion utilizes RGB and depth
information to construct fused feature from Kinect. FGF involves multi-Jaccard
similarity to compute a robust graph and utilize word embedding method to
enhance the recognition results. We also collect DUT RGB-D face dataset and a
benchmark datset to evaluate the effectiveness and efficiency of our method.
The experimental results illustrate FGF is robust and effective to face and
object datasets in robot applications.
Characterizing the Google Books corpus: Strong limits to inferences of socio-cultural and linguistic evolution
Authors: Eitan Adam Pechenick, Christopher M. Danforth, Peter Sheridan Dodds
Date: 2015-01-05 00:00:00
Tasks: Articles

It is tempting to treat frequency trends from the Google Books data sets as indicators of the "true" popularity of various words and phrases. Doing so allows us to draw quantitatively strong conclusions about the evolution of cultural perception of a given topic, such as time or gender. However, the Google Books corpus suffers from a number of limitations which make it an obscure mask of cultural popularity. A primary issue is that the corpus is in effect a library, containing one of each book. A single, prolific author is thereby able to noticeably insert new phrases into the Google Books lexicon, whether the author is widely read or not. With this understood, the Google Books corpus remains an important data set to be considered more lexicon-like than text-like. Here, we show that a distinct problematic feature arises from the inclusion of scientific texts, which have become an increasingly substantive portion of the corpus throughout the 1900s. The result is a surge of phrases typical to academic articles but less common in general, such as references to time in the form of citations. We highlight these dynamics by examining and comparing major contributions to the statistical divergence of English data sets between decades in the period 1800--2000. We find that only the English Fiction data set from the second version of the corpus is not heavily affected by professional texts, in clear contrast to the first version of the fiction data set and both unfiltered English data sets. Our findings emphasize the need to fully characterize the dynamics of the Google Books corpus before using these data sets to draw broad conclusions about cultural and linguistic evolution.
A Hybrid Deep Learning Approach for Texture Analysis
Authors: Hussein Adly, Mohamed Moustafa
Date: 2017-03-24 00:00:00
Tasks: Classification, Deep Learning, General Classification, Texture Classification

Texture classification is a problem that has various applications such as
remote sensing and forest species recognition. Solutions tend to be custom fit
to the dataset used but fails to generalize. The Convolutional Neural Network
(CNN) in combination with Support Vector Machine (SVM) form a robust selection
between powerful invariant feature extractor and accurate classifier. The
fusion of experts provides stability in classification rates among different
datasets.
Scalable Person Re-identification on Supervised Smoothed Manifold
Authors: Song Bai, Xiang Bai, Qi Tian
Date: 2017-03-24 00:00:00
Proceeding: CVPR 2017 7
Tasks: Person Re-Identification

Most existing person re-identification algorithms either extract robust
visual features or learn discriminative metrics for person images. However, the
underlying manifold which those images reside on is rarely investigated. That
raises a problem that the learned metric is not smooth with respect to the
local geometry structure of the data manifold.
  In this paper, we study person re-identification with manifold-based affinity
learning, which did not receive enough attention from this area. An
unconventional manifold-preserving algorithm is proposed, which can 1) make the
best use of supervision from training data, whose label information is given as
pairwise constraints; 2) scale up to large repositories with low on-line time
complexity; and 3) be plunged into most existing algorithms, serving as a
generic postprocessing procedure to further boost the identification
accuracies. Extensive experimental results on five popular person
re-identification benchmarks consistently demonstrate the effectiveness of our
method. Especially, on the largest CUHK03 and Market-1501, our method
outperforms the state-of-the-art alternatives by a large margin with high
efficiency, which is more appropriate for practical applications.
Is language evolution grinding to a halt? The scaling of lexical turbulence in English fiction suggests it is not
Authors: Eitan Adam Pechenick, Christopher M. Danforth, Peter Sheridan Dodds
Date: 2015-03-11 00:00:00

Of basic interest is the quantification of the long term growth of a
language's lexicon as it develops to more completely cover both a culture's
communication requirements and knowledge space. Here, we explore the usage
dynamics of words in the English language as reflected by the Google Books 2012
English Fiction corpus. We critique an earlier method that found decreasing
birth and increasing death rates of words over the second half of the 20th
Century, showing death rates to be strongly affected by the imposed time cutoff
of the arbitrary present and not increasing dramatically. We provide a robust,
principled approach to examining lexical evolution by tracking the volume of
word flux across various relative frequency thresholds. We show that while the
overall statistical structure of the English language remains stable over time
in terms of its raw Zipf distribution, we find evidence of an enduring `lexical
turbulence': The flux of words across frequency thresholds from decade to
decade scales superlinearly with word rank and exhibits a scaling break we
connect to that of Zipf's law. To better understand the changing lexicon, we
examine the contributions to the Jensen-Shannon divergence of individual words
crossing frequency thresholds. We also find indications that scholarly works
about fiction are strongly represented in the 2012 English Fiction corpus, and
suggest that a future revision of the corpus should attempt to separate
critical works from fiction itself.
Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features
Authors: Pantelis Bouboulis, Symeon Chouvardas, Sergios Theodoridis
Date: 2017-03-23 00:00:00

We present a novel diffusion scheme for online kernel-based learning over
networks. So far, a major drawback of any online learning algorithm, operating
in a reproducing kernel Hilbert space (RKHS), is the need for updating a
growing number of parameters as time iterations evolve. Besides complexity,
this leads to an increased need of communication resources, in a distributed
setting. In contrast, the proposed method approximates the solution as a
fixed-size vector (of larger dimension than the input space) using Random
Fourier Features. This paves the way to use standard linear combine-then-adapt
techniques. To the best of our knowledge, this is the first time that a
complete protocol for distributed online learning in RKHS is presented.
Conditions for asymptotic convergence and boundness of the networkwise regret
are also provided. The simulated tests illustrate the performance of the
proposed scheme.