pwc-archive/papers-with-abstracts huggingface dataset:
Support Neighbor Loss for Person Re-Identification
Authors: Kai Li, Zhengming Ding, Kunpeng Li, Yulun Zhang, Yun Fu
Date: 2018-08-18 00:00:00
Tasks: Person Re-Identification

Person re-identification (re-ID) has recently been tremendously boosted due
to the advancement of deep convolutional neural networks (CNN). The majority of
deep re-ID methods focus on designing new CNN architectures, while less
attention is paid on investigating the loss functions. Verification loss and
identification loss are two types of losses widely used to train various deep
re-ID models, both of which however have limitations. Verification loss guides
the networks to generate feature embeddings of which the intra-class variance
is decreased while the inter-class ones is enlarged. However, training networks
with verification loss tends to be of slow convergence and unstable performance
when the number of training samples is large. On the other hand, identification
loss has good separating and scalable property. But its neglect to explicitly
reduce the intra-class variance limits its performance on re-ID, because the
same person may have significant appearance disparity across different camera
views. To avoid the limitations of the two types of losses, we propose a new
loss, called support neighbor (SN) loss. Rather than being derived from data
sample pairs or triplets, SN loss is calculated based on the positive and
negative support neighbor sets of each anchor sample, which contain more
valuable contextual information and neighborhood structure that are beneficial
for more stable performance. To ensure scalability and separability, a
softmax-like function is formulated to push apart the positive and negative
support sets. To reduce intra-class variance, the distance between the anchor's
nearest positive neighbor and furthest positive sample is penalized.
Integrating SN loss on top of Resnet50, superior re-ID results to the
state-of-the-art ones are obtained on several widely used datasets.
Characterizing Transgender Health Issues in Twitter
Authors: Amir Karami, Frank Webb, Vanessa L. Kitzie
Date: 2018-08-18 00:00:00

Although there are millions of transgender people in the world, a lack of
information exists about their health issues. This issue has consequences for
the medical field, which only has a nascent understanding of how to identify
and meet this population's health-related needs. Social media sites like
Twitter provide new opportunities for transgender people to overcome these
barriers by sharing their personal health experiences. Our research employs a
computational framework to collect tweets from self-identified transgender
users, detect those that are health-related, and identify their information
needs. This framework is significant because it provides a macro-scale
perspective on an issue that lacks investigation at national or demographic
levels. Our findings identified 54 distinct health-related topics that we
grouped into 7 broader categories. Further, we found both linguistic and
topical differences in the health-related information shared by transgender men
(TM) as com-pared to transgender women (TW). These findings can help inform
medical and policy-based strategies for health interventions within transgender
communities. Also, our proposed approach can inform the development of
computational strategies to identify the health-related information needs of
other marginalized populations.
What do the US West Coast Public Libraries Post on Twitter?
Authors: Amir Karami, Matthew Collins
Date: 2018-08-17 00:00:00
Tasks: Public Relations

Twitter has provided a great opportunity for public libraries to disseminate
information for a variety of purposes. Twitter data have been applied in
different domains such as health, politics, and history. There are thousands of
public libraries in the US, but no study has yet investigated the content of
their social media posts like tweets to find their interests. Moreover,
traditional content analysis of Twitter content is not an efficient task for
exploring thousands of tweets. Therefore, there is a need for automatic methods
to overcome the limitations of manual methods. This paper proposes a
computational approach to collecting and analyzing using Twitter Application
Programming Interfaces (API) and investigates more than 138,000 tweets from 48
US west coast libraries using topic modeling. We found 20 topics and assigned
them to five categories including public relations, book, event, training, and
social good. Our results show that the US west coast libraries are more
interested in using Twitter for public relations and book-related events. This
research has both practical and theoretical applications for libraries as well
as other organizations to explore social media actives of their customer and
themselves.
Optimizing Deep Neural Network Architecture: A Tabu Search Based Approach
Authors: Tarun Kumar Gupta, Khalid Raza
Date: 2018-08-17 00:00:00

The performance of Feedforward neural network (FNN) fully de-pends upon the
selection of architecture and training algorithm. FNN architecture can be
tweaked using several parameters, such as the number of hidden layers, number
of hidden neurons at each hidden layer and number of connections between
layers. There may be exponential combinations for these architectural
attributes which may be unmanageable manually, so it requires an algorithm
which can automatically design an optimal architecture with high generalization
ability. Numerous optimization algorithms have been utilized for FNN
architecture determination. This paper proposes a new methodology which can
work on the estimation of hidden layers and their respective neurons for FNN.
This work combines the advantages of Tabu search (TS) and Gradient descent with
momentum backpropagation (GDM) training algorithm to demonstrate how Tabu
search can automatically select the best architecture from the populated
architectures based on minimum testing error criteria. The proposed approach
has been tested on four classification benchmark dataset of different size.
On Geometric Analysis of Affine Sparse Subspace Clustering
Authors: Chun-Guang Li, Chong You, René Vidal
Date: 2018-08-17 00:00:00
Tasks: Clustering

Sparse subspace clustering (SSC) is a state-of-the-art method for segmenting
a set of data points drawn from a union of subspaces into their respective
subspaces. It is now well understood that SSC produces subspace-preserving data
affinity under broad geometric conditions but suffers from a connectivity
issue. In this paper, we develop a novel geometric analysis for a variant of
SSC, named affine SSC (ASSC), for the problem of clustering data from a union
of affine subspaces. Our contributions include a new concept called affine
independence for capturing the arrangement of a collection of affine subspaces.
Under the affine independence assumption, we show that ASSC is guaranteed to
produce subspace-preserving affinity. Moreover, inspired by the phenomenon that
the $\ell_1$ regularization no longer induces sparsity when the solution is
nonnegative, we further show that subspace-preserving recovery can be achieved
under much weaker conditions for all data points other than the extreme points
of samples from each subspace. In addition, we confirm a curious observation
that the affinity produced by ASSC may be subspace-dense---which could
guarantee the subspace-preserving affinity of ASSC to produce correct
clustering under rather weak conditions. We validate the theoretical findings
on carefully designed synthetic data and evaluate the performance of ASSC on
several real data sets.
Collaborative Pressure Ulcer Prevention: An Automated Skin Damage and Pressure Ulcer Assessment Tool for Nursing Professionals, Patients, Family Members and Carers
Authors: Paul Fergus, Carl Chalmers, David Tully
Date: 2018-08-17 00:00:00
Tasks: Transfer Learning

This paper describes the Pressure Ulcers Online Website, which is a first
step solution towards a new and innovative platform for helping people to
detect, understand and manage pressure ulcers. It outlines the reasons why the
project has been developed and provides a central point of contact for pressure
ulcer analysis and ongoing research. Using state-of-the-art technologies in
convolutional neural networks and transfer learning along with end-to-end web
technologies, this platform allows pressure ulcers to be analysed and findings
to be reported. As the system evolves through collaborative partnerships,
future versions will provide decision support functions to describe the complex
characteristics of pressure ulcers along with information on wound care across
multiple user boundaries. This project is therefore intended to raise awareness
and support for people suffering with or providing care for pressure ulcers.
A study on speech enhancement using exponent-only floating point quantized neural network (EOFP-QNN)
Authors: Yi-Te Hsu, Yu-Chen Lin, Szu-Wei Fu, Yu Tsao, Tei-Wei Kuo
Date: 2018-08-17 00:00:00
Tasks: Quantization, regression, Speech Enhancement

Numerous studies have investigated the effectiveness of neural network
quantization on pattern classification tasks. The present study, for the first
time, investigated the performance of speech enhancement (a regression task in
speech processing) using a novel exponent-only floating-point quantized neural
network (EOFP-QNN). The proposed EOFP-QNN consists of two stages:
mantissa-quantization and exponent-quantization. In the mantissa-quantization
stage, EOFP-QNN learns how to quantize the mantissa bits of the model
parameters while preserving the regression accuracy using the least mantissa
precision. In the exponent-quantization stage, the exponent part of the
parameters is further quantized without causing any additional performance
degradation. We evaluated the proposed EOFP quantization technique on two types
of neural networks, namely, bidirectional long short-term memory (BLSTM) and
fully convolutional neural network (FCN), on a speech enhancement task.
Experimental results showed that the model sizes can be significantly reduced
(the model sizes of the quantized BLSTM and FCN models were only 18.75% and
21.89%, respectively, compared to those of the original models) while
maintaining satisfactory speech-enhancement performance.
Disambiguating fine-grained place names from descriptions by clustering
Authors: Hao Chen, Maria Vasardani, Stephan Winter
Date: 2018-08-17 00:00:00
Tasks: Clustering

Everyday place descriptions often contain place names of fine-grained
features, such as buildings or businesses, that are more difficult to
disambiguate than names referring to larger places, for example cities or
natural geographic features. Fine-grained places are often significantly more
frequent and more similar to each other, and disambiguation heuristics
developed for larger places, such as those based on population or containment
relationships, are often not applicable in these cases. In this research, we
address the disambiguation of fine-grained place names from everyday place
descriptions. For this purpose, we evaluate the performance of different
existing clustering-based approaches, since clustering approaches require no
more knowledge other than the locations of ambiguous place names. We consider
not only approaches developed specifically for place name disambiguation, but
also clustering algorithms developed for general data mining that could
potentially be leveraged. We compare these methods with a novel algorithm, and
show that the novel algorithm outperforms the other algorithms in terms of
disambiguation precision and distance error over several tested datasets.
Bitstream-Based JPEG Image Encryption with File-Size Preserving
Authors: Hiroyuki Kobayashi, Hitoshi Kiya
Date: 2018-08-17 00:00:00

An encryption scheme of JPEG images in the bitstream domain is proposed. The
proposed scheme preserves the JPEG format even after encrypting the images, and
the file size of encrypted images is the exact same as that of the original
JPEG images. Several methods for encrypting JPEG images in the bitstream domain
have been proposed. However, since some marker codes are generated or lost in
the encryption process, the file size of JPEG bitstreams is generally changed
due to the encryption operations. The proposed method inputs JPEG bitstreams
and selectively encrypts the additional bit components of the Huffman code in
the bitstreams. This feature allows us to have encrypted images with the same
data size as that recoded in the image transmission process, when JPEG images
are replaced with the encrypted ones by the hooking, so that the image
transmission are successfully carried out after the hooking.
Benchmarking Automatic Machine Learning Frameworks
Authors: Adithya Balaji, Alexander Allen
Date: 2018-08-17 00:00:00
Tasks: Automated Feature Engineering, AutoML, Benchmarking, BIG-bench Machine Learning, Classification, General Classification, Hyperparameter Optimization, regression

AutoML serves as the bridge between varying levels of expertise when
designing machine learning systems and expedites the data science process. A
wide range of techniques is taken to address this, however there does not exist
an objective comparison of these techniques. We present a benchmark of current
open source AutoML solutions using open source datasets. We test auto-sklearn,
TPOT, auto_ml, and H2O's AutoML solution against a compiled set of regression
and classification datasets sourced from OpenML and find that auto-sklearn
performs the best across classification datasets and TPOT performs the best
across regression datasets.
Study of Set-Membership Adaptive Kernel Algorithms
Authors: A. Flores, R. C. de Lamare
Date: 2018-08-15 00:00:00
Tasks: Time Series, Time Series Analysis

In the last decade, a considerable research effort has been devoted to
developing adaptive algorithms based on kernel functions. One of the main
features of these algorithms is that they form a family of universal
approximation techniques, solving problems with nonlinearities elegantly. In
this paper, we present data-selective adaptive kernel normalized least-mean
square (KNLMS) algorithms that can increase their learning rate and reduce
their computational complexity. In fact, these methods deal with kernel
expansions, creating a growing structure also known as the dictionary, whose
size depends on the number of observations and their innovation. The algorithms
described herein use an adaptive step-size to accelerate the learning and can
offer an excellent tradeoff between convergence speed and steady state, which
allows them to solve nonlinear filtering and estimation problems with a large
number of parameters without requiring a large computational cost. The
data-selective update scheme also limits the number of operations performed and
the size of the dictionary created by the kernel expansion, saving
computational resources and dealing with one of the major problems of kernel
adaptive algorithms. A statistical analysis is carried out along with a
computational complexity analysis of the proposed algorithms. Simulations show
that the proposed KNLMS algorithms outperform existing algorithms in examples
of nonlinear system identification and prediction of a time series originating
from a nonlinear difference equation.
Ricean K-factor Estimation based on Channel Quality Indicator in OFDM Systems using Neural Network
Authors: Kun Wang
Date: 2018-08-15 00:00:00
Tasks: General Classification

Ricean channel model is widely used in wireless communications to
characterize the channels with a line-of-sight path. The Ricean K factor,
defined as the ratio of direct path and scattered paths, provides a good
indication of the link quality. Most existing works estimate K factor based on
either maximum-likelihood criterion or higher-order moments, and the existing
works are targeted at K-factor estimation at receiver side. In this work, a
novel approach is proposed. Cast as a classification problem, the estimation of
K factor by neural network provides high accuracy. Moreover, the proposed
K-factor estimation is done at transmitter side for transmit processing, thus
saving the limited feedback bandwidth.
Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks
Authors: Muhammad Shahzad, Michael Maurer, Friedrich Fraundorfer, Yuanyuan Wang, Xiao Xiang Zhu
Date: 2018-08-14 00:00:00
Tasks: General Classification, image-classification, Image Classification

This paper addresses the highly challenging problem of automatically
detecting man-made structures especially buildings in very high resolution
(VHR) synthetic aperture radar (SAR) images. In this context, the paper has two
major contributions: Firstly, it presents a novel and generic workflow that
initially classifies the spaceborne TomoSAR point clouds $ - $ generated by
processing VHR SAR image stacks using advanced interferometric techniques known
as SAR tomography (TomoSAR) $ - $ into buildings and non-buildings with the aid
of auxiliary information (i.e., either using openly available 2-D building
footprints or adopting an optical image classification scheme) and later back
project the extracted building points onto the SAR imaging coordinates to
produce automatic large-scale benchmark labelled (buildings/non-buildings) SAR
datasets. Secondly, these labelled datasets (i.e., building masks) have been
utilized to construct and train the state-of-the-art deep Fully Convolution
Neural Networks with an additional Conditional Random Field represented as a
Recurrent Neural Network to detect building regions in a single VHR SAR image.
Such a cascaded formation has been successfully employed in computer vision and
remote sensing fields for optical image classification but, to our knowledge,
has not been applied to SAR images. The results of the building detection are
illustrated and validated over a TerraSAR-X VHR spotlight SAR image covering
approximately 39 km$ ^2 $ $ - $ almost the whole city of Berlin $ - $ with mean
pixel accuracies of around 93.84%
predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning
Authors: Mohamed R. Ibrahim, Helena Titheridge, Tao Cheng, James Haworth
Date: 2018-08-14 00:00:00
Tasks: image-classification, Image Classification, Satellite Image Classification

Identifying current and future informal regions within cities remains a
crucial issue for policymakers and governments in developing countries. The
delineation process of identifying such regions in cities requires a lot of
resources. While there are various studies that identify informal settlements
based on satellite image classification, relying on both supervised or
unsupervised machine learning approaches, these models either require multiple
input data to function or need further development with regards to precision.
In this paper, we introduce a novel method for identifying and predicting
informal settlements using only street intersections data, regardless of the
variation of urban form, number of floors, materials used for construction or
street width. With such minimal input data, we attempt to provide planners and
policy-makers with a pragmatic tool that can aid in identifying informal zones
in cities. The algorithm of the model is based on spatial statistics and a
machine learning approach, using Multinomial Logistic Regression (MNL) and
Artificial Neural Networks (ANN). The proposed model relies on defining
informal settlements based on two ubiquitous characteristics that these regions
tend to be filled in with smaller subdivided lots of housing relative to the
formal areas within the local context, and the paucity of services and
infrastructure within the boundary of these settlements that require relatively
bigger lots. We applied the model in five major cities in Egypt and India that
have spatial structures in which informality is present. These cities are
Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India.
The predictSLUMS model shows high validity and accuracy for identifying and
predicting informality within the same city the model was trained on or in
different ones of a similar context.
The perceived quality of process discovery tools
Authors: Francis Bru, Jan Claes
Date: 2018-08-13 00:00:00

Process discovery has seen a rise in popularity in the last decade for both
researchers and businesses. Recent developments mainly focused on the power and
the functionalities of the discovery algorithm. While continuous improvement of
these functional aspects is very important, non-functional aspects such as
visualization and usability are often overlooked. However, these aspects are
considered valuable for end-users and play an important part in the experience
of these end-users when working with a process discovery tool. A questionnaire
has been sent out to give end-users the opportunity to voice their opinion on
available process discovery tools and about the state of process discovery as a
domain in general. The results of 66 respondents are presented and compared
with the answers of 63 respondents that were contacted through one particular
software vendor's employee and customer base (i.e., Celonis).
Learning Discriminative Hashing Codes for Cross-Modal Retrieval based on Multi-view Features
Authors: Jun Yu, Xiao-Jun Wu, Josef Kittler
Date: 2018-08-13 00:00:00
Tasks: Cross-Modal Retrieval, Information Retrieval, Quantization, Retrieval

Hashing techniques have been applied broadly in retrieval tasks due to their low storage requirements and high speed of processing. Many hashing methods based on a single view have been extensively studied for information retrieval. However, the representation capacity of a single view is insufficient and some discriminative information is not captured, which results in limited improvement. In this paper, we employ multiple views to represent images and texts for enriching the feature information. Our framework exploits the complementary information among multiple views to better learn the discriminative compact hash codes. A discrete hashing learning framework that jointly performs classifier learning and subspace learning is proposed to complete multiple search tasks simultaneously. Our framework includes two stages, namely a kernelization process and a quantization process. Kernelization aims to find a common subspace where multi-view features can be fused. The quantization stage is designed to learn discriminative unified hashing codes. Extensive experiments are performed on single-label datasets (WiKi and MMED) and multi-label datasets (MIRFlickr and NUS-WIDE) and the experimental results indicate the superiority of our method compared with the state-of-the-art methods.
Outer Product-based Neural Collaborative Filtering
Authors: Xiangnan He, Xiaoyu Du, Xiang Wang, Feng Tian, Jinhui Tang, Tat-Seng Chua
Date: 2018-08-12 00:00:00
Tasks: Collaborative Filtering, Multi-Domain Recommender Systems, Recommendation Systems

In this work, we contribute a new multi-layer neural network architecture
named ONCF to perform collaborative filtering. The idea is to use an outer
product to explicitly model the pairwise correlations between the dimensions of
the embedding space. In contrast to existing neural recommender models that
combine user embedding and item embedding via a simple concatenation or
element-wise product, our proposal of using outer product above the embedding
layer results in a two-dimensional interaction map that is more expressive and
semantically plausible. Above the interaction map obtained by outer product, we
propose to employ a convolutional neural network to learn high-order
correlations among embedding dimensions. Extensive experiments on two public
implicit feedback data demonstrate the effectiveness of our proposed ONCF
framework, in particular, the positive effect of using outer product to model
the correlations between embedding dimensions in the low level of multi-layer
neural recommender model. The experiment codes are available at:
https://github.com/duxy-me/ConvNCF