pwc-archive/papers-with-abstracts huggingface dataset:
Utility in Fashion with implicit feedback
Authors: Garg Vikram, Sathyanarayana Girish, Borar Sumit, Rajan Aruna
Date: 2018-06-30 00:00:00
Tasks: Marketing, Recommendation Systems

Fashion preference is a fuzzy concept that depends on customer taste,
prevailing norms in fashion product/style, henceforth used interchangeably, and
a customer's perception of utility or fashionability, yet fashion e-retail
relies on algorithmically generated search and recommendation systems that
process structured data and images to best match customer preference. Retailers
study tastes solely as a function of what sold vs what did not, and take it to
represent customer preference. Such explicit modeling, however, belies the
underlying user preference, which is a complicated interplay of preference and
commercials such as brand, price point, promotions, other sale events, and
competitor push/marketing. It is hard to infer a notion of utility or even
customer preference by looking at sales data.
  In search and recommendation systems for fashion e-retail, customer
preference is implicitly derived by user-user similarity or item-item
similarity. In this work, we aim to derive a metric that separates the buying
preferences of users from the commercials of the merchandise (price,
promotions, etc). We extend our earlier work on explicit signals to gauge
sellability or preference with implicit signals from user behaviour.
Ballistocardiogram-based Authentication using Convolutional Neural Networks
Authors: Hebert Joshua, Lewis Brittany, Cai Hang, Venkatasubramanian Krishna K., Provost Matthew, Charlebois Kelly
Date: 2018-06-28 00:00:00

The goal of this work is to demonstrate the use of the ballistocardiogram
(BCG) signal, derived using head-mounted wearable devices, as a viable
biometric for authentication. The BCG signal is the measure of an person's body
acceleration as a result of the heart's ejection of blood. It is a
characterization of the cardiac cycle and can be derived non-invasively from
the measurement of subtle movements of a person's extremities. In this paper,
we use several versions of the BCG signal, derived from accelerometer and
gyroscope sensors on a Smart Eyewear (SEW) device, for authentication. The
derived BCG signals are used to train a convolutional neural network (CNN) as
an authentication model, which is personalized for each subject. We evaluate
our authentication models using data from 12 subjects and show that our
approach has an equal error rate (EER) of 3.5% immediately after training and
13\% after about 2 months, in the worst case. We also explore the use of our
authentication approach for people with motor disabilities. Our analysis using
a separate dataset of 6 subjects with non-spastic cerebral palsy shows an EER
of 11.2% immediately after training and 21.6% after about 2 months, in the
worst-case.
Robust Heartbeat Detection from Multimodal Data via CNN-based Generalizable Information Fusion
Authors: Chandra B S, Sastry C S, Jana S
Date: 2018-06-29 00:00:00
Tasks: Management

Objective: Heartbeat detection remains central to cardiac disease diagnosis
and management, and is traditionally performed based on electrocardiogram
(ECG). To improve robustness and accuracy of detection, especially, in certain
critical-care scenarios, the use of additional physiological signals such as
arterial blood pressure (BP) has recently been suggested. There, estimation of
heartbeat location requires information fusion from multiple signals. However,
reported efforts in this direction often obtain multimodal estimates somewhat
indirectly, by voting among separately obtained signal-specific intermediate
estimates. In contrast, we propose to directly fuse information from multiple
signals without requiring intermediate estimates, and thence estimate heartbeat
location in a robust manner. Method: We propose as a heartbeat detector, a
convolutional neural network (CNN) that learns fused features from multiple
physiological signals. This method eliminates the need for hand-picked
signal-specific features and ad hoc fusion schemes. Further, being data-driven,
the same algorithm learns suitable features from arbitrary set of signals.
Results: Using ECG and BP signals of PhysioNet 2014 Challenge database, we
obtained a score of 94%. Further, using two ECG channels of MIT-BIH arrhythmia
database, we scored 99.92\%. Both those scores compare favourably with
previously reported database-specific results. Also, our detector achieved high
accuracy in a variety of clinical conditions. Conclusion: The proposed
CNN-based information fusion (CIF) algorithm is generalizable, robust and
efficient in detecting heartbeat location from multiple signals. Significance:
In medical signal monitoring systems, our technique would accurately estimate
heartbeat locations even when only a subset of channels are reliable.
A New ECOC Algorithm for Multiclass Microarray Data Classification
Authors: Sun Mengxin, Liu Kunhong, Hong Qingqi, Wang Beizhan
Date: 2018-06-22 00:00:00
Tasks: Classification, General Classification

The classification of multi-class microarray datasets is a hard task because
of the small samples size in each class and the heavy overlaps among classes.
To effectively solve these problems, we propose novel Error Correcting Output
Code (ECOC) algorithm by Enhance Class Separability related Data Complexity
measures during encoding process, named as ECOCECS. In this algorithm, two
nearest neighbor related DC measures are deployed to extract the intrinsic
overlapping information from microarray data. Our ECOC algorithm aims to search
an optimal class split scheme by minimizing these measures. The class splitting
process ends when each class is separated from others, and then the class
assignment scheme is mapped as a coding matrix. Experiments are carried out on
five microarray datasets, and results demonstrate the effectiveness and
robustness of our method in comparison with six state-of-art ECOC methods. In
short, our work confirm the probability of applying DC to ECOC framework.
Data Efficient Lithography Modeling with Transfer Learning and Active Data Selection
Authors: Lin Yibo, Li Meng, Watanabe Yuki, Kimura Taiki, Matsunawa Tetsuaki, Nojima Shigeki, Pan David Z.
Date: 2018-06-27 00:00:00
Tasks: Active Learning, Transfer Learning, valid

Lithography simulation is one of the key steps in physical verification,
enabled by the substantial optical and resist models. A resist model bridges
the aerial image simulation to printed patterns. While the effectiveness of
learning-based solutions for resist modeling has been demonstrated, they are
considerably data-demanding. Meanwhile, a set of manufactured data for a
specific lithography configuration is only valid for the training of one single
model, indicating low data efficiency. Due to the complexity of the
manufacturing process, obtaining enough data for acceptable accuracy becomes
very expensive in terms of both time and cost, especially during the evolution
of technology generations when the design space is intensively explored. In
this work, we propose a new resist modeling framework for contact layers,
utilizing existing data from old technology nodes and active selection of data
in a target technology node, to reduce the amount of data required from the
target lithography configuration. Our framework based on transfer learning and
active learning techniques is effective within a competitive range of accuracy,
i.e., 3-10X reduction on the amount of training data with comparable accuracy
to the state-of-the-art learning approach.
Blind Identification of SFBC-OFDM Signals Based on the Central Limit Theorem
Date: 2019-08-14 00:00:00

Previous approaches for blind identification of space-frequency block codes
(SFBC) do not perform well for short observation periods due to their
inefficient utilization of frequency-domain redundancy. This paper proposes a
hypothesis test (HT)-based algorithm and a support vector machine (SVM)-based
algorithm for SFBC signals identification over frequency-selective fading
channels to exploit two-dimensional space-frequency domain redundancy. Based on
the central limit theorem, space-domain redundancy is exploited to construct
the cross-correlation function of the estimator and frequency-domain redundancy
is incorporated in the construction of the statistics. The difference between
the two proposed algorithms is that the HT-based algorithm constructs a
chi-square statistic and employs an HT to make the decision, while the
SVM-based algorithm constructs a non-central chi-square statistic with unknown
mean as a strongly-distinguishable statistical feature and uses an SVM to make
the decision. Both algorithms do not require knowledge of the channel
coefficients, modulation type or noise power, and the SVM-based algorithm does
not require timing synchronization. Simulation results verify the superior
performance of the proposed algorithms for short observation periods with
comparable computational complexity to conventional algorithms, as well as
their acceptable identification performance in the presence of transmission
impairments.
Signals as Parametric Curves: Application to Independent Component Analysis and Blind Source Separation
Date: 2018-07-10 00:00:00
Tasks: blind source separation, Image Registration

Images Stacks as Parametric Surfaces (ISPS) is a powerful model that was
originally proposed for image registration. Being closely related to mutual
information (MI) - the most classic similarity measure for image registration,
ISPS works well across different categories of registration problems. The
Signals as Parametric Curves (SPC) model is derived from ISPS extended to
1-dimensional signals. Blind Source Separation (BSS) is a classic problem in
signal processing, where Independent Component Analysis (ICA) based approaches
are popular and effective. Since MI plays an important role in ICA, based on
the close relationship with MI, we apply SPC model to BSS in this paper, and
propose a group of geometrical objective functions that are simple yet
powerful, and serve as replacements of original MI-based objective functions.
Motivated by the geometrical objective functions, we also propose a
second-order-statistics approach, FT-PCA. Both geometrical objective functions
and FT-PCA consider signals as functions instead of stochastic processes, make
use of derivative information of signals, and do not rely on the independence
assumption. In this paper, we discuss the reasonability of the assumptions of
geometrical objective functions and FT-PCA, and show their effectiveness by
synthetic experiments, comparing with other previous classic approaches.
Constrained State Estimation -- A Review
Authors: Nesrine Amor, Ghulam Rasool, Nidhal C. Bouaynaya
Date: 2018-07-10 00:00:00
Tasks: State Estimation

The real-world applications in signal processing generally involve estimating the system state or parameters in nonlinear, non-Gaussian dynamic systems. The estimation problem may get even more challenging when there are physical constraints on the system state. This tutorial-style paper reviews the Bayesian state estimation for (non)linear state-space systems and introduces the formulation of constrained state estimation in such scenarios. Specifically, we start by providing a review of unconstrained state estimation using Kalman filters (KF) for the linear systems and their extensions for nonlinear state-space systems, including extended Kalman filters (EKF), unscented Kalman filters (UKF), and ensemble Kalman filters (EnKF). Next, we present particle filters (PFs) for nonlinear state-space systems. Finally, we review constrained state estimation using various filtering techniques and highlight the advantages and disadvantages of the different constrained state estimation approaches.
The Recommendation System to SNS Community for Tourists by Using Altruistic Behaviors
Authors: Ichimura Takumi, Uemoto Takuya, Kamada Shin
Date: 2018-07-11 00:00:00

We have already developed the recommendation system of sightseeing
information on SNS by using smartphone based user participatory sensing system.
The system can post the attractive information for tourists to the specified
Facebook page by our developed smartphone application. The users in Facebook,
who are interested in sightseeing, can come flocking through information space
from far and near. However, the activities in the community on SNS are only
supported by the specified people called a hub. We proposed the method of
vitalization of tourist behaviors to give a stimulus to the people. We
developed the simulation system for multi agent system with altruistic
behaviors inspired by the Army Ants. The army ant takes feeding action with
altruistic behaviors to suppress selfish behavior to a common object used by a
plurality of users in common. In this paper, we introduced the altruism
behavior determined by some simulation to vitalize the SNS community. The
efficiency of the revitalization process of the community was investigated by
some experimental simulation results.
A Recommendation System of Grants to Acquire External Funds
Authors: Kamada Shin, Ichimura Takumi, Watanabe Takanobu
Date: 2018-07-11 00:00:00
Tasks: Retrieval

The recommendation system of the competitive grants to university researchers
by using the Grants-in-Aid for Scientific Research (KAKEN) keywords has been
developed. The system can determine the recommendation order of researchers to
each grant by the using the association rules between KAKEN application and
various information from the web site of the corresponding grant. However, our
developed previous system has some fatal errors in the retrieval algorithm. We
modify the algorithm and extend the retrieval data for web mining. If the grant
information is not enough to determine the relation, the system investigates
the past KAKEN records in the database for the researcher who acquired the past
grant. Moreover, the system retrieves the papers of the researchers to search
their interests. As a result, the agreement degree of the researcher's interest
to the grant increases. This paper discusses some simulation results.
Underwater Source Localization Using TDOA and FDOA Measurements with Unknown Propagation Speed and Sensor Parameter Errors
Date: 2018-07-25 00:00:00
Tasks: Position

Underwater source localization problems are complicated and challenging: a)
the sound propagation speed is often unknown and the unpredictable ocean
current might lead to the uncertainties of sensor parameters (i.e. position and
velocity); b) the underwater acoustic signal travels much slower than the radio
one in terrestrial environments, thus resulting in a significantly severe
Doppler effect; c) energy-efficient techniques are urgently required and hence
in favour of the design with a low computational complexity. Considering these
issues, we propose a simple and efficient underwater source localization
approach based on time difference of arrival (TDOA) and frequency difference of
arrival (FDOA) measurements, which copes with unknown propagation speed and
sensor parameter errors. The proposed method mitigates the impact of the
Doppler effect for accurately inferring the source parameters (i.e. position
and velocity). The Cramer-Rao lower bounds (CRLBs) for this kind of
localization are derived and, moreover, the analytical study shows that our
method can yield the performance that is very close to the CRLB, particularly
under small noise. The numerical results not only confirm the above conclusions
but also show that our method outperforms other competing approaches.
A CT image based finite element modelling to predict the mechanical behaviour of human arm
Date: 2018-07-10 00:00:00

In the present work, complex irregular bones and joints of the complete human
arm were developed in a computer-aided design environment. Finite element
analysis of an actual human arm was done to identify the distribution of stress
using von-Mises stress and maximum principal stress measures. The results
obtained from the present study revealed the region where, maximum stress was
developed for different loading and boundary conditions with different joint
rotations as obtained in the actual human arm. This subject specific analysis
helps to analyse the region of the arm in which the risk is more.
Modelling and analysis of biological joints of a human arm in different movements
Date: 2018-07-10 00:00:00

In the present work, irregular complex joints of the human arm are modelled
in the computer-aided design environment. Accurately the geometric models of
the joints and surrounding bony regions are developed from computed tomography
data set and the range of movements are analysed. In this present work,
biological joints are approximated into the mechanical joint without losing any
kinematic behaviour and degrees of freedom. Then the analysis is done to
determine the importance of joint geometries with different loading and
boundary conditions. The present study helps to predict the behavior of joint
configuration in the design stage.
A Technique for Multi-User MIMO using Spatial Channel Model for out-door environments
Date: 2018-07-10 00:00:00

Any wireless communication system needs to specify a propagation channel
model which acts as basis for performance evaluation and comparison. Spatial
channel models can be divided into deterministic i.e ray tracing, measurement
based which is based on channel information and geometry based stochastic
channel models which are based on assumption that directional structure of
channel can be modelled by last interaction between physical objects and
electromagnetic waves, before waves reach the base or mobile station. Multi
user double directional channel model (MDDCM) is a geometry based channel model
which is used to calculate the double directional channel information in
cellular system with MIMO mobile station and MIMO base station.
  In this research phase firstly, Multi User Multiple Input Multiple Output
(MU-MIMO) spatial channel model has been implemented for different outdoor
environments Urban Micro and Urban Macro using MATLAB for finding various
parameters like angle of arrival of the user, user direction and the distance
between user and access point (AP). Secondly coded (Multiple Input Multiple
Output-Orthogonal Frequency Division Multiplexing) MIMO-OFDM system has been
implemented using multipath Rayleigh faded channel and realistic Spatial
Channel Model. Different BER improvement techniques are used such as,
Viterbi-decoder, Time and Frequency inter-leaving. Multi-channel diversity is
also observed by using multiple antennas at transmitting and receiving end
[(2x2) and (2x4)] on both the channels. Effect of different modulation
techniques on BER performance is also observed.
Computer modelling of connectivity change suggests epileptogenesis mechanisms in idiopathic generalised epilepsy
Date: 2018-11-10 00:00:00

Patients with idiopathic generalised epilepsy (IGE) typically have normal
conventional magnetic resonance imaging (MRI), hence MRI based diagnosis is
challenging. Anatomical abnormalities underlying brain dysfunctions in IGE are
unclear and their relation to the pathomechanisms of epileptogenesis is poorly
understood. In this study, we applied connectometry, an advanced quantitative
neuroimaging technique for investigating localised changes in white-matter
tissue. Analysing white matter structures of 32 subjects we incorporated our
findings in a computational model of seizure dynamics to suggest a plausible
mechanism of epileptogenesis. Patients with IGE have significant bilateral
alterations in major white-matter fascicles. In the cingulum, fornix, and
superior longitudinal fasciculus, tract integrity is compromised, whereas in
specific parts of tracts between thalamus and the precentral gyrus, tract
integrity is enhanced in patients. Combining these alterations in a logistic
regression model, we computed the decision boundary that discriminated patients
and controls. The computational model, informed with the findings on the tract
abnormalities, specifically highlighted the importance of enhanced
cortico-reticular connections along with impaired cortico-cortical connections
in inducing pathological seizure-like dynamics. We emphasise taking
directionality of brain connectivity into consideration towards understanding
the pathological mechanisms; this is possible by combining neuroimaging and
computational modelling. Our imaging evidence of structural alterations suggest
the loss of cortico-cortical and enhancement of cortico-thalamic fibre
integrity in IGE. We further suggest that impaired connectivity from cortical
regions to the thalamic reticular nucleus offers a therapeutic target for
selectively modifying the brain circuit for reversing the mechanisms leading to
epileptogenesis.
Peerus Review: a tool for scientific experts finding
Authors: Brochier Robin  ERIC, Guille Adrien  ERIC, Velcin Julien  ERIC, Rothan Benjamin, Cioccio Di
Date: 2018-06-28 00:00:00
Tasks: Language Modeling, Language Modelling, Retrieval

We propose a tool for experts finding applied to academic data generated by
the start-up DSRT in the context of its application Peerus. A user may submit
the title, the abstract and optionnally the authors and the journal of
publication of a scientific article and the application then returns a list of
experts, potential reviewers of the submitted article. The retrieval algorithm
is a voting system based on a language modeling technique trained on several
millions of scientific papers.
Citrate stabilized gold nanoparticles interfere with amyloid fibril formation: D76N and {\Delta}N6 \b{eta}2-microglobulin variants
Date: 2018-07-10 00:00:00

Protein aggregation including the formation of dimers and multimers in
solution, underlies an array of human diseases such as systemic amyloidosis
which is a fatal disease caused by misfolding of native globular proteins
damaging the structure and function of affected organs. Different kind of
interactors can interfere with the formation of protein dimers and multimers in
solution. A very special class of interactors are nanoparticles thanks to the
extremely efficient extension of their interaction surface. In particular
citrate-coated gold nanoparticles (cit-AuNPs) were recently investigated with
amyloidogenic protein $\beta$2-microglobulin ($\beta$2m). Here we present the
computational studies on two challenging models known for their enhanced
amyloidogenic propensity, namely $\Delta$N6 and D76N $\beta$2m naturally
occurring variants, and disclose the role of cit-AuNPs on their
fibrillogenesis. The proposed interaction mechanism lies in the interference of
the cit-AuNPs with the protein dimers at the early stages of aggregation, that
induces dimer disassembling. As a consequence, natural fibril formation can be
inhibited. Relying on the comparison between atomistic simulations at multiple
levels (enhanced sampling molecular dynamics and Brownian dynamics) and protein
structural characterisation by NMR, we demonstrate that the cit-AuNPs
interactors are able to inhibit protein dimer assembling. As a consequence, the
natural fibril formation is also inhibited, as found in experiment.