pwc-archive/papers-with-abstracts huggingface dataset:
Exploring Online Ad Images Using a Deep Convolutional Neural Network Approach
Authors: Michael Fire, Jonathan Schler
Date: 2015-09-02 00:00:00

Online advertising is a huge, rapidly growing advertising market in today's
world. One common form of online advertising is using image ads. A decision is
made (often in real time) every time a user sees an ad, and the advertiser is
eager to determine the best ad to display. Consequently, many algorithms have
been developed that calculate the optimal ad to show to the current user at the
present time. Typically, these algorithms focus on variations of the ad,
optimizing among different properties such as background color, image size, or
set of images. However, there is a more fundamental layer. Our study looks at
new qualities of ads that can be determined before an ad is shown (rather than
online optimization) and defines which ads are most likely to be successful.
  We present a set of novel algorithms that utilize deep-learning image
processing, machine learning, and graph theory to investigate online
advertising and to construct prediction models which can foresee an image ad's
success. We evaluated our algorithms on a dataset with over 260,000 ad images,
as well as a smaller dataset specifically related to the automotive industry,
and we succeeded in constructing regression models for ad image click rate
prediction. The obtained results emphasize the great potential of using
deep-learning algorithms to effectively and efficiently analyze image ads and
to create better and more innovative online ads. Moreover, the algorithms
presented in this paper can help predict ad success and can be applied to
analyze other large-scale image corpora.
Adaptive Contract Design for Crowdsourcing Markets: Bandit Algorithms for Repeated Principal-Agent Problems
Authors: Chien-Ju Ho, Aleksandrs Slivkins, Jennifer Wortman Vaughan
Date: 2014-05-12 00:00:00
Tasks: Multi-Armed Bandits

Crowdsourcing markets have emerged as a popular platform for matching
available workers with tasks to complete. The payment for a particular task is
typically set by the task's requester, and may be adjusted based on the quality
of the completed work, for example, through the use of "bonus" payments. In
this paper, we study the requester's problem of dynamically adjusting
quality-contingent payments for tasks. We consider a multi-round version of the
well-known principal-agent model, whereby in each round a worker makes a
strategic choice of the effort level which is not directly observable by the
requester. In particular, our formulation significantly generalizes the
budget-free online task pricing problems studied in prior work.
  We treat this problem as a multi-armed bandit problem, with each "arm"
representing a potential contract. To cope with the large (and in fact,
infinite) number of arms, we propose a new algorithm, AgnosticZooming, which
discretizes the contract space into a finite number of regions, effectively
treating each region as a single arm. This discretization is adaptively
refined, so that more promising regions of the contract space are eventually
discretized more finely. We analyze this algorithm, showing that it achieves
regret sublinear in the time horizon and substantially improves over
non-adaptive discretization (which is the only competing approach in the
literature).
  Our results advance the state of art on several different topics: the theory
of crowdsourcing markets, principal-agent problems, multi-armed bandits, and
dynamic pricing.
A fast numerical method for max-convolution and the application to efficient max-product inference in Bayesian networks
Authors: Oliver Serang
Date: 2015-01-12 00:00:00

Observations depending on sums of random variables are common throughout many
fields; however, no efficient solution is currently known for performing
max-product inference on these sums of general discrete distributions
(max-product inference can be used to obtain maximum a posteriori estimates).
The limiting step to max-product inference is the max-convolution problem
(sometimes presented in log-transformed form and denoted as "infimal
convolution", "min-convolution", or "convolution on the tropical semiring"),
for which no O(k log(k)) method is currently known. Here I present a O(k
log(k)) numerical method for estimating the max-convolution of two nonnegative
vectors (e.g., two probability mass functions), where k is the length of the
larger vector. This numerical max-convolution method is then demonstrated by
performing fast max-product inference on a convolution tree, a data structure
for performing fast inference given information on the sum of n discrete random
variables in O(n k log(n k) log(n) ) steps (where each random variable has an
arbitrary prior distribution on k contiguous possible states). The numerical
max-convolution method can be applied to specialized classes of hidden Markov
models to reduce the runtime of computing the Viterbi path from n k^2 to n k
log(k), and has potential application to the all-pairs shortest paths problem.
Enhancement and Recognition of Reverberant and Noisy Speech by Extending Its Coherence
Authors: Scott Wisdom, Thomas Powers, Les Atlas, James Pitton
Date: 2015-09-02 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Speech Enhancement, speech-recognition, Speech Recognition

Most speech enhancement algorithms make use of the short-time Fourier
transform (STFT), which is a simple and flexible time-frequency decomposition
that estimates the short-time spectrum of a signal. However, the duration of
short STFT frames are inherently limited by the nonstationarity of speech
signals. The main contribution of this paper is a demonstration of speech
enhancement and automatic speech recognition in the presence of reverberation
and noise by extending the length of analysis windows. We accomplish this
extension by performing enhancement in the short-time fan-chirp transform
(STFChT) domain, an overcomplete time-frequency representation that is coherent
with speech signals over longer analysis window durations than the STFT. This
extended coherence is gained by using a linear model of fundamental frequency
variation of voiced speech signals. Our approach centers around using a
single-channel minimum mean-square error log-spectral amplitude (MMSE-LSA)
estimator proposed by Habets, which scales coefficients in a time-frequency
domain to suppress noise and reverberation. In the case of multiple
microphones, we preprocess the data with either a minimum variance
distortionless response (MVDR) beamformer, or a delay-and-sum beamformer (DSB).
We evaluate our algorithm on both speech enhancement and recognition tasks for
the REVERB challenge dataset. Compared to the same processing done in the STFT
domain, our approach achieves significant improvement in terms of objective
enhancement metrics (including PESQ---the ITU-T standard measurement for speech
quality). In terms of automatic speech recognition (ASR) performance as
measured by word error rate (WER), our experiments indicate that the STFT with
a long window is more effective for ASR.
Sensor-Type Classification in Buildings
Authors: Dezhi Hong, Jorge Ortiz, Arka Bhattacharya, Kamin Whitehouse
Date: 2015-09-01 00:00:00
Tasks: Classification, Ensemble Learning, General Classification, Vocal Bursts Type Prediction

Many sensors/meters are deployed in commercial buildings to monitor and
optimize their performance. However, because sensor metadata is inconsistent
across buildings, software-based solutions are tightly coupled to the sensor
metadata conventions (i.e. schemas and naming) for each building. Running the
same software across buildings requires significant integration effort.
  Metadata normalization is critical for scaling the deployment process and
allows us to decouple building-specific conventions from the code written for
building applications. It also allows us to deal with missing metadata. One
important aspect of normalization is to differentiate sensors by the typeof
phenomena being observed. In this paper, we propose a general, simple, yet
effective classification scheme to differentiate sensors in buildings by type.
We perform ensemble learning on data collected from over 2000 sensor streams in
two buildings. Our approach is able to achieve more than 92% accuracy for
classification within buildings and more than 82% accuracy for across
buildings. We also introduce a method for identifying potential misclassified
streams. This is important because it allows us to identify opportunities to
attain more input from experts -- input that could help improve classification
accuracy when ground truth is unavailable. We show that by adjusting a
threshold value we are able to identify at least 30% of the misclassified
instances.
On TD(0) with function approximation: Concentration bounds and a centered variant with exponential convergence
Authors: Nathaniel Korda, L. A. Prashanth
Date: 2014-11-12 00:00:00

We provide non-asymptotic bounds for the well-known temporal difference
learning algorithm TD(0) with linear function approximators. These include
high-probability bounds as well as bounds in expectation. Our analysis suggests
that a step-size inversely proportional to the number of iterations cannot
guarantee optimal rate of convergence unless we assume (partial) knowledge of
the stationary distribution for the Markov chain underlying the policy
considered. We also provide bounds for the iterate averaged TD(0) variant,
which gets rid of the step-size dependency while exhibiting the optimal rate of
convergence. Furthermore, we propose a variant of TD(0) with linear
approximators that incorporates a centering sequence, and establish that it
exhibits an exponential rate of convergence in expectation. We demonstrate the
usefulness of our bounds on two synthetic experimental settings.
A Robust Regression Approach for Background/Foreground Segmentation
Authors: Shervin Minaee, Haoping Yu, Yao Wang
Date: 2014-12-16 00:00:00
Tasks: Clustering, Foreground Segmentation, regression, Segmentation

Background/foreground segmentation has a lot of applications in image and
video processing. In this paper, a segmentation algorithm is proposed which is
mainly designed for text and line extraction in screen content. The proposed
method makes use of the fact that the background in each block is usually
smoothly varying and can be modeled well by a linear combination of a few
smoothly varying basis functions, while the foreground text and graphics create
sharp discontinuity. The algorithm separates the background and foreground
pixels by trying to fit pixel values in the block into a smooth function using
a robust regression method. The inlier pixels that can fit well will be
considered as background, while remaining outlier pixels will be considered
foreground. This algorithm has been extensively tested on several images from
HEVC standard test sequences for screen content coding, and is shown to have
superior performance over other methods, such as the k-means clustering based
segmentation algorithm in DjVu. This background/foreground segmentation can be
used in different applications such as: text extraction, separate coding of
background and foreground for compression of screen content and mixed content
documents, principle line extraction from palmprint and crease detection in
fingerprint images.
Learning to Segment Object Candidates
Authors: Pedro O. Pinheiro, Ronan Collobert, Piotr Dollar
Date: 2015-06-20 00:00:00
Proceeding: NeurIPS 2015 12
Tasks: Object, object-detection, Object Detection, Segmentation, Superpixels

Recent object detection systems rely on two critical steps: (1) a set of
object proposals is predicted as efficiently as possible, and (2) this set of
candidate proposals is then passed to an object classifier. Such approaches
have been shown they can be fast, while achieving the state of the art in
detection performance. In this paper, we propose a new way to generate object
proposals, introducing an approach based on a discriminative convolutional
network. Our model is trained jointly with two objectives: given an image
patch, the first part of the system outputs a class-agnostic segmentation mask,
while the second part of the system outputs the likelihood of the patch being
centered on a full object. At test time, the model is efficiently applied on
the whole test image and generates a set of segmentation masks, each of them
being assigned with a corresponding object likelihood score. We show that our
model yields significant improvements over state-of-the-art object proposal
algorithms. In particular, compared to previous approaches, our model obtains
substantially higher object recall using fewer proposals. We also show that our
model is able to generalize to unseen categories it has not seen during
training. Unlike all previous approaches for generating object masks, we do not
rely on edges, superpixels, or any other form of low-level segmentation.
Iterative hypothesis testing for multi-object tracking in presence of features with variable reliability
Authors: Amit Kumar K. C., Damien Delannay, Christophe De Vleeschouwer
Date: 2015-09-01 00:00:00
Tasks: Multi-Object Tracking, Object Tracking, Two-sample testing

This paper assumes prior detections of multiple targets at each time instant,
and uses a graph-based approach to connect those detections across time, based
on their position and appearance estimates. In contrast to most earlier works
in the field, our framework has been designed to exploit the appearance
features, even when they are only sporadically available, or affected by a
non-stationary noise, along the sequence of detections. This is done by
implementing an iterative hypothesis testing strategy to progressively
aggregate the detections into short trajectories, named tracklets.
Specifically, each iteration considers a node, named key-node, and investigates
how to link this key-node with other nodes in its neighborhood, under the
assumption that the target appearance is defined by the key-node appearance
estimate. This is done through shortest path computation in a temporal
neighborhood of the key-node. The approach is conservative in that it only
aggregates the shortest paths that are sufficiently better compared to
alternative paths. It is also multi-scale in that the size of the investigated
neighborhood is increased proportionally to the number of detections already
aggregated into the key-node. The multi-scale nature of the process and the
progressive relaxation of its conservativeness makes it both computationally
efficient and effective.
  Experimental validations are performed extensively on a toy example, a 15
minutes long multi-view basketball dataset, and other monocular pedestrian
datasets.
Robust Face Recognition via Multimodal Deep Face Representation
Authors: Changxing Ding, DaCheng Tao
Date: 2015-09-01 00:00:00
Tasks: Face Recognition, Robust Face Recognition

Face images appeared in multimedia applications, e.g., social networks and
digital entertainment, usually exhibit dramatic pose, illumination, and
expression variations, resulting in considerable performance degradation for
traditional face recognition algorithms. This paper proposes a comprehensive
deep learning framework to jointly learn face representation using multimodal
information. The proposed deep learning structure is composed of a set of
elaborately designed convolutional neural networks (CNNs) and a three-layer
stacked auto-encoder (SAE). The set of CNNs extracts complementary facial
features from multimodal data. Then, the extracted features are concatenated to
form a high-dimensional feature vector, whose dimension is compressed by SAE.
All the CNNs are trained using a subset of 9,000 subjects from the publicly
available CASIA-WebFace database, which ensures the reproducibility of this
work. Using the proposed single CNN architecture and limited training data,
98.43% verification rate is achieved on the LFW database. Benefited from the
complementary information contained in multimodal data, our small ensemble
system achieves higher than 99.0% recognition rate on LFW using publicly
available training set.
Fingerprinting-Based Positioning in Distributed Massive MIMO Systems
Authors: Vladimir Savic, Erik G. Larsson
Date: 2015-09-01 00:00:00
Tasks: Autonomous Driving

Location awareness in wireless networks may enable many applications such as
emergency services, autonomous driving and geographic routing. Although there
are many available positioning techniques, none of them is adapted to work with
massive multiple-in-multiple-out (MIMO) systems, which represent a leading 5G
technology candidate. In this paper, we discuss possible solutions for
positioning of mobile stations using a vector of signals at the base station,
equipped with many antennas distributed over deployment area. Our main proposal
is to use fingerprinting techniques based on a vector of received signal
strengths. This kind of methods are able to work in highly-cluttered multipath
environments, and require just one base station, in contrast to standard
range-based and angle-based techniques. We also provide a solution for
fingerprinting-based positioning based on Gaussian process regression, and
discuss main applications and challenges.
A Fuzzy Approach for Feature Evaluation and Dimensionality Reduction to Improve the Quality of Web Usage Mining Results
Authors: Zahid Ansari, M. F. Azeem, A. Vinaya Babu, Waseem Ahmed
Date: 2015-09-01 00:00:00
Tasks: Clustering, Dimensionality Reduction, feature selection

Web Usage Mining is the application of data mining techniques to web usage
log repositories in order to discover the usage patterns that can be used to
analyze the users navigational behavior. During the preprocessing stage, raw
web log data is transformed into a set of user profiles. Each user profile
captures a set of URLs representing a user session. Clustering can be applied
to this sessionized data in order to capture similar interests and trends among
users navigational patterns. Since the sessionized data may contain thousands
of user sessions and each user session may consist of hundreds of URL accesses,
dimensionality reduction is achieved by eliminating the low support URLs. Very
small sessions are also removed in order to filter out the noise from the data.
But direct elimination of low support URLs and small sized sessions may results
in loss of a significant amount of information especially when the count of low
support URLs and small sessions is large. We propose a fuzzy solution to deal
with this problem by assigning weights to URLs and user sessions based on a
fuzzy membership function. After assigning the weights we apply a Fuzzy c-Mean
Clustering algorithm to discover the clusters of user profiles. In this paper,
we describe our fuzzy set theoretic approach to perform feature selection (or
dimensionality reduction) and session weight assignment. Finally we compare our
soft computing based approach of dimensionality reduction with the traditional
approach of direct elimination of small sessions and low support count URLs.
Our results show that fuzzy feature evaluation and dimensionality reduction
results in better performance and validity indices for the discovered clusters.
Fast rates in statistical and online learning
Authors: Tim van Erven, Peter D. Grünwald, Nishant A. Mehta, Mark D. Reid, Robert C. Williamson
Date: 2015-07-09 00:00:00
Tasks: Density Estimation, Learning Theory

The speed with which a learning algorithm converges as it is presented with
more data is a central problem in machine learning --- a fast rate of
convergence means less data is needed for the same level of performance. The
pursuit of fast rates in online and statistical learning has led to the
discovery of many conditions in learning theory under which fast learning is
possible. We show that most of these conditions are special cases of a single,
unifying condition, that comes in two forms: the central condition for 'proper'
learning algorithms that always output a hypothesis in the given model, and
stochastic mixability for online algorithms that may make predictions outside
of the model. We show that under surprisingly weak assumptions both conditions
are, in a certain sense, equivalent. The central condition has a
re-interpretation in terms of convexity of a set of pseudoprobabilities,
linking it to density estimation under misspecification. For bounded losses, we
show how the central condition enables a direct proof of fast rates and we
prove its equivalence to the Bernstein condition, itself a generalization of
the Tsybakov margin condition, both of which have played a central role in
obtaining fast rates in statistical learning. Yet, while the Bernstein
condition is two-sided, the central condition is one-sided, making it more
suitable to deal with unbounded losses. In its stochastic mixability form, our
condition generalizes both a stochastic exp-concavity condition identified by
Juditsky, Rigollet and Tsybakov and Vovk's notion of mixability. Our unifying
conditions thus provide a substantial step towards a characterization of fast
rates in statistical learning, similar to how classical mixability
characterizes constant regret in the sequential prediction with expert advice
setting.
Discovery of Web Usage Profiles Using Various Clustering Techniques
Authors: Zahid Ansari, Waseem Ahmed, M. F. Azeem, A. Vinaya Babu
Date: 2015-09-01 00:00:00
Tasks: Clustering

The explosive growth of World Wide Web (WWW) has necessitated the development
of Web personalization systems in order to understand the user preferences to
dynamically serve customized content to individual users. To reveal information
about user preferences from Web usage data, Web Usage Mining (WUM) techniques
are extensively being applied to the Web log data. Clustering techniques are
widely used in WUM to capture similar interests and trends among users
accessing a Web site. Clustering aims to divide a data set into groups or
clusters where inter-cluster similarities are minimized while the intra cluster
similarities are maximized. This paper reviews four of the popularly used
clustering techniques: k-Means, k-Medoids, Leader and DBSCAN. These techniques
are implemented and tested against the Web user navigational data. Performance
and validity results of each technique are presented and compared.
Remote sensing image classification exploiting multiple kernel learning
Authors: Claudio Cusano, Paolo Napoletano, Raimondo Schettini
Date: 2014-10-20 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Remote Sensing Image Classification

We propose a strategy for land use classification which exploits Multiple
Kernel Learning (MKL) to automatically determine a suitable combination of a
set of features without requiring any heuristic knowledge about the
classification task. We present a novel procedure that allows MKL to achieve
good performance in the case of small training sets. Experimental results on
publicly available datasets demonstrate the feasibility of the proposed
approach.
GR2RSS: Publishing Linked Open Commerce Data as RSS and Atom Feeds
Authors: Alex Stolz, Martin Hepp
Date: 2015-09-01 00:00:00
Tasks: Management

The integration of Linked Open Data (LOD) content in Web pages is a
challenging and sometimes tedious task for Web developers. At the same moment,
most software packages for blogs, content management systems (CMS), and shop
applications support the consumption of feed formats, namely RSS and Atom. In
this technical report, we demonstrate an on-line tool that fetches e-commerce
data from a SPARQL endpoint and syndicates obtained results as RSS or Atom
feeds. Our approach combines (1) the popularity and broad tooling support of
existing feed formats, (2) the precision of queries against structured data
built upon common Web vocabularies like schema.org, GoodRelations, FOAF, VCard,
and WGS 84, and (3) the ease of integrating content from a large number of Web
sites and other data sources in RDF in general.