pwc-archive/papers-with-abstracts huggingface dataset:
Robust Wireless Body Area Networks Coexistence: A Game Theoretic Approach to Time-Division MAC
Date: 2018-08-30 00:00:00

The enabling of wireless body area networks (WBANs) coexistence by radio
interference mitigation is very important due to a rapid growth in potential
users, and a lack of a central coordinator among WBANs that are closely
located. In this paper, we propose a TDMA based MAC layer Scheme, with a
back-off mechanism that reduces packet collision probability; and estimate
performance using a Markov chain model. Based on the MAC layer scheme, a novel
non-cooperative game is proposed to jointly adjust sensor node's transmit power
and rate. In comparison with the state-of-art, simulation that includes
empirical data shows that the proposed approach leads to higher throughput and
longer node lifespan as WBAN wearers dynamically move into each other's
vicinity. Moreover, by adaptively tuning contention windows size an alternative
game is developed, which significantly reduces the latency. Both proposed games
provide robust transmission under strong inter-WBAN interferences, but are
demonstrated to be applicable to different scenarios. The uniqueness and
existence of Nash Equilibrium (NE), as well as close-to-optimum social
efficiency, is also proven for both games.
Color Image Encryption Based on Chaotic Block Permutation and XOR Operation
Date: 2018-08-30 00:00:00

In this paper, chaotic block image permutation and XOR operation are
performed to achieve image encryption. The studied method of encryption makes
use of chaotic systems properties for secure and speed image encryption.
Firstly, the original image is divided into blocks of equal size. Then, two
chaotic maps are used to generate two key streams which are permuted among
themselves to produce one key steam. The image blocks are then shuffled using
part of key stream. Finally, scrambled image is diffused by XOR operation with
the key stream to get the encrypted image. The experimental results of several
performance analyses about the pixel correlation, various statistical analysis,
information entropy analysis, differential analysis, the key space and key
sensitivity analysis, show that the algorithm can resist several know attacks
effectively and has the advantages of large key space, high security, and high
speed, assuring safety performance and secure image encryption.
Outage Probability of Millimeter Wave Cellular Uplink with Truncated Power Control
Date: 2018-08-30 00:00:00

In this paper, using the stochastic geometry, we develop a tractable uplink
modeling framework for the outage probability of both the single and multi-tier
millimeter wave (mmWave) cellular networks. Each tier's mmWave base stations
(BSs) are randomly located and they have particular spatial density, antenna
gain, receiver sensitivity, blockage parameter and pathloss exponents. Our
model takes account of the maximum power limitation and the per-user power
control. More specifically, each user, which could be in line-of-sight (LOS) or
non-LOS to its serving mmWave BS, controls its transmit power such that the
received signal power at its serving BS is equal to a predefined threshold.
Hence, a truncated channel inversion power control scheme is implemented for
the uplink of mmWave cellular networks. We derive closed-form expressions for
the signal-to-interference-and-noise-ratio (SINR) outage probability for the
uplink of both the single and multi-tier mmWave cellular networks. Furthermore,
we analyze the case with a dense network by utilizing the simplified model,
where the LOS region is approximated as a fixed LOS disc. The results show that
imposing a maximum power constraint on the user significantly affects the SINR
outage probability in the uplink of mmWave cellular networks.
On the Performance of a Relay-Assisted Multi-Hop Asymmetric FSO/RF Communication System over Negative Exponential atmospheric turbulence with the effect of pointing error
Date: 2018-11-20 00:00:00

In this paper, a multi-user multi-hop hybrid FSO / RF system is presented.
This structure is consisted of two main parts. The main motivation of
presenting this structure is communication in long-range impassable links or
some specific atmospheric conditions under which RF connection becomes easily
disrupted. Although these effects could be mitigated by consuming more power or
adding processing complexity, but a small user mobile phone cannot deserve more
complexity or power supply. The fact that FSO and RF links are complementary of
each other brings a new solution in mind; an access point that amplifies
received signal via short-range RF link and forwards it to via long-range FSO
link, could solve the mentioned problem. This scenario is exactly implemented
at the first part of the proposed structure. At the second part, a multi-hop
hybrid parallel FSO / RF link is implemented to connect source and destination
Base Stations. It is the first time that in a multi-hop FSO / RF system,
multi-user scheme, signal selection at each hop, known and un-known CSI in
amplify and forward relaying, and saturate atmospheric turbulence with the
effect of pointing error are considered. New expressions are derived in
closed-form for Bit Error Rate (BER) and Outage Probability of the proposed
structure and verified by MATLAB simulations. The proposed structure has
advantages of FSO, RF, relay-assisted, and multi-user systems at the same time.
Results indicate that it has low dependence on number of users and number of
relays. Therefore, it is suitable for areas with varying population and
long-range links. This structure offers independent performance without
additional power consumption, processing latency, and complexity.
Large-Scale Cover Song Detection in Digital Music Libraries Using Metadata, Lyrics and Audio Features
Authors: Correya Albin Andrew, Hennequin Romain, Arcos Mickaël
Date: 2018-08-30 00:00:00
Tasks: Cover song identification, Information Retrieval, Music Information Retrieval, Retrieval, text similarity

Cover song detection is a very relevant task in Music Information Retrieval
(MIR) studies and has been mainly addressed using audio-based systems. Despite
its potential impact in industrial contexts, low performances and lack of
scalability have prevented such systems from being adopted in practice for
large applications. In this work, we investigate whether textual music
information (such as metadata and lyrics) can be used along with audio for
large-scale cover identification problem in a wide digital music library. We
benchmark this problem using standard text and state of the art audio
similarity measures. Our studies shows that these methods can significantly
increase the accuracy and scalability of cover detection systems on Million
Song Dataset (MSD) and Second Hand Song (SHS) datasets. By only leveraging
standard tf-idf based text similarity measures on song titles and lyrics, we
achieved 35.5% of absolute increase in mean average precision compared to the
current scalable audio content-based state of the art methods on MSD. These
experimental results suggests that new methodologies can be encouraged among
researchers to leverage and identify more sophisticated NLP-based techniques to
improve current cover song identification systems in digital music libraries
with metadata.
An Exponential Cox-Ingersoll-Ross Process as Discounting Factor
Date: 2018-08-30 00:00:00

We consider an economic agent (a household or an insurance company) modelling
its surplus process by a deterministic process or by a Brownian motion with
drift. The goal is to maximise the expected discounted spendings/dividend
payments, given that the discounting factor is given by an exponential CIR
process. In the deterministic case, we are able to find explicit expressions
for the optimal strategy and the value function. For the Brownian motion case,
we offer a method allowing to show that for a small volatility the optimal
strategy is a constant-barrier strategy.
The role of complex analysis in modeling economic growth
Date: 2018-08-30 00:00:00

Development and growth are complex and tumultuous processes. Modern economic
growth theories identify some key determinants of economic growth. However, the
relative importance of the determinants remains unknown, and additional
variables may help clarify the directions and dimensions of the interactions.
The novel stream of literature on economic complexity goes beyond aggregate
measures of productive inputs, and considers instead a more granular and
structural view of the productive possibilities of countries, i.e. their
capabilities. Different endowments of capabilities are crucial ingredients in
explaining differences in economic performances. In this paper we employ
economic fitness, a measure of productive capabilities obtained through complex
network techniques. Focusing on the combined roles of fitness and some more
traditional drivers of growth, we build a bridge between economic growth
theories and the economic complexity literature. Our findings, in agreement
with other recent empirical studies, show that fitness plays a crucial role in
fostering economic growth and, when it is included in the analysis, can be
either complementary to traditional drivers of growth or can completely
overshadow them.
Optimum window length of Savitzky-Golay filters with arbitrary order
Date: 2018-08-30 00:00:00
Tasks: Denoising

One of the widely used denoising methods in different domains is the
Savitzky-Golay (SG) filter. The SG filter has two design parameters: window
length and the filter order. As the length of the window increases, the
estimation variance decreases, but the bias error increases at the same time.
Mean square error (MSE) measure includes both bias and variance criteria. In
this paper, we obtain the optimal window length of an SG filter with arbitrary
order which minimizes the MSE. To achieve the optimal window length, we propose
an algorithm whose performance is better than the existing methods. In this
paper, we follow the viewpoint proposed by Persson and Strang and design the
filter on the basis of Chebyshev orthogonal polynomials
Securing Tag-based recommender systems against profile injection attacks: A comparative study
Authors: Pitsilis Georgios, Ramampiaro Heri, Langseth Helge
Date: 2018-08-30 00:00:00
Tasks: Deep Learning, Recommendation Systems, TAG

This work addresses challenges related to attacks on social tagging systems,
which often comes in a form of malicious annotations or profile injection
attacks. In particular, we study various countermeasures against two types of
threats for such systems, the Overload and the Piggyback attacks. The studied
countermeasures include baseline classifiers such as, Naive Bayes filter and
Support Vector Machine, as well as a deep learning-based approach. Our
evaluation performed over synthetic spam data, generated from del.icio.us,
shows that in most cases, the deep learning-based approach provides the best
protection against threats.
A global model for predicting the arrival of imported dengue infections
Date: 2019-11-11 00:00:00

With approximately half of the world's population at risk of contracting
dengue, this mosquito-borne disease is of global concern. International
travellers significantly contribute to dengue's rapid and large-scale spread by
importing the disease from endemic into non-endemic countries. To prevent
future outbreaks and dengue from establishing in non-endemic countries,
knowledge about the arrival time and location of infected travellers is
crucial. We propose a network model that predicts the monthly number of
dengue-infected air passengers arriving at any given airport. We consider
international air travel volumes to construct weighted networks, representing
passenger flows between airports. We further calculate the probability of
passengers, who travel through the international air transport network, being
infected with dengue. The probability of being infected depends on the
destination, duration and timing of travel. Our findings shed light onto dengue
importation routes and reveal country-specific reporting rates that have been
until now largely unknown. This paper provides important new knowledge about
the spreading dynamics of dengue that is highly beneficial for public health
authorities to strategically allocate the often limited resources to more
efficiently prevent the spread of dengue.
TenDSuR: Tensor-Based 4D Sub-Nyquist Radar
Date: 2018-08-31 00:00:00
Tasks: compressed sensing

We propose Tensor-based 4D Sub-Nyquist Radar (TenDSuR) that samples in
spectral, spatial, Doppler, and temporal domains at sub-Nyquist rates while
simultaneously recovering the target's direction, Doppler velocity, and range
without loss of native resolutions. We formulate the radar signal model wherein
the received echo samples are represented by a partial third-order tensor. We
then apply compressed sensing in the tensor domain and use our tensor-OMP and
tensor completion algorithms for signal recovery. Our numerical experiments
demonstrate joint estimation of all three target parameters at the same native
resolutions as a conventional radar but with reduced measurements. Furthermore,
tensor completion methods show enhanced performance in off-grid target recovery
with respect to tensor-OMP.
Identifying the Discount Factor in Dynamic Discrete Choice Models
Date: 2019-09-16 00:00:00
Tasks: Discrete Choice Models

Empirical research often cites observed choice responses to variation that
shifts expected discounted future utilities, but not current utilities, as an
intuitive source of information on time preferences. We study the
identification of dynamic discrete choice models under such economically
motivated exclusion restrictions on primitive utilities. We show that each
exclusion restriction leads to an easily interpretable moment condition with
the discount factor as the only unknown parameter. The identified set of
discount factors that solves this condition is finite, but not necessarily a
singleton. Consequently, in contrast to common intuition, an exclusion
restriction does not in general give point identification. Finally, we show
that exclusion restrictions have nontrivial empirical content: The implied
moment conditions impose restrictions on choices that are absent from the
unconstrained model.
A hybrid cellular automaton model of cartilage regeneration capturing the interactions between cellular dynamics and scaffold porosity
Date: 2018-08-29 00:00:00
Tasks: Sensitivity

To accelerate the development of strategies for cartilage tissue engineering,
models are necessary to study the interactions between cellular dynamics and
scaffold (SC) porosity. In experiments, cells are seeded in a porous SC where
over a month, the SC slowly degrades while cells divide and synthesize
extracellular matrix (ECM) constituents. We use an off-lattice cellular
automaton framework to model the individual behavior of cells within the SC.
The movement of cells and the ability to reproduce is determined by the
nutrient profile and local porosity. A phenomenological approach is used to
capture a continuous profile for SC and ECM evolution, which will then change
the local porosity. We parameterize the model by matching total cell counts to
chondrocytes seeded in a polyglycolic acid SC. We investigate the total cell
count and location of various cell populations for different initial SC
porosities. Similar to experiments, we observe cell counts that level off
around day 15 with higher values in SCs of lower initial porosity. Cell
clustering is observed in regions at the edge of the construct that are close
to the nutrient-rich medium in the fluid bath. Model results show that a bias
in motion due to sensitivity to porosity allows cells to move in a more optimal
arrangement. We investigate the distribution of cells as the cell reproduction
rate, cell movement distance, and sensitivity to porosity is varied. We observe
non monotonic changes in total cell counts within different regions of the
construct due to the interplay between porosity and cellular movement. We also
analyze the emergent average cell speed for different initial SC porosities,
observing an higher average for the lowest initial SC porosity. This model
provides a framework to further investigate how changes in biological
parameters can change the cellular count and distribution in SCs of different
initial porosity
Latent Space Temporal Model of Microbial Abundance to Predict Domination and Bacteremia
Date: 2018-08-31 00:00:00

Gut microbial composition has been linked to multiple health outcomes. Yet,
temporal analysis of this composition had been limited to deterministic models.
In this paper, we introduce a probabilistic model for the dynamics of
intestinal microbiomes that takes into account interaction among bacteria as
well as external effects such as antibiotics. The model successfully deals with
pragmatic issues such as random measurement error and varying time intervals
between measurements through latent space modeling. We demonstrate utility of
the model by using latent state features to predict the clinical events of
intestinal domination and bacteremia, improving accuracy over existing methods.
We further leverage this framework to validate known links between antibiotics
and clinical outcomes, while discovering new ones.
Single Channel ECG for Obstructive Sleep Apnea Severity Detection using a Deep Learning Approach
Date: 2018-08-31 00:00:00
Tasks: Specificity

Obstructive sleep apnea (OSA) is a common sleep disorder caused by abnormal
breathing. The severity of OSA can lead to many symptoms such as sudden cardiac
death (SCD). Polysomnography (PSG) is a gold standard for OSA diagnosis. It
records many signals from the patient's body for at least one whole night and
calculates the Apnea-Hypopnea Index (AHI) which is the number of apnea or
hypopnea incidences per hour. This value is then used to classify patients into
OSA severity levels. However, it has many disadvantages and limitations.
Consequently, we proposed a novel methodology of OSA severity classification
using a Deep Learning approach. We focused on the classification between normal
subjects (AHI $<$ 5) and severe OSA patients (AHI $>$ 30). The 15-second raw
ECG records with apnea or hypopnea events were used with a series of deep
learning models. The main advantages of our proposed method include easier data
acquisition, instantaneous OSA severity detection, and effective feature
extraction without domain knowledge from expertise. To evaluate our proposed
method, 545 subjects of which 364 were normal and 181 were severe OSA patients
obtained from the MrOS sleep study (Visit 1) database were used with the k-fold
cross-validation technique. The accuracy of 79.45\% for OSA severity
classification with sensitivity, specificity, and F-score was achieved. This is
significantly higher than the results from the SVM classifier with RR Intervals
and ECG derived respiration (EDR) signal feature extraction. The promising
result shows that this proposed method is a good start for the detection of OSA
severity from a single channel ECG which can be obtained from wearable devices
at home and can also be applied to near real-time alerting systems such as
before SCD occurs.
Repeated Coordination with Private Learning
Date: 2018-08-31 00:00:00

We study a repeated game with payoff externalities and observable actions
where two players receive information over time about an underlying
payoff-relevant state, and strategically coordinate their actions. Players
learn about the true state from private signals, as well as the actions of
others. They commonly learn the true state (Cripps et al., 2008), but do not
coordinate in every equilibrium. We show that there exist stable equilibria in
which players can overcome unfavorable signal realizations and eventually
coordinate on the correct action, for any discount factor. For high discount
factors, we show that in addition players can also achieve efficient payoffs.
Bond Graph Representation of Chemical Reaction Networks
Date: 2018-10-14 00:00:00

The Bond Graph approach and the Chemical Reaction Network approach to
modelling biomolecular systems developed independently. This paper brings
together the two approaches by providing a bond graph interpretation of the
chemical reaction network concept of complexes. Both closed and open systems
are discussed.
  The method is illustrated using a simple enzyme-catalysed reaction and a
trans-membrane transporter.
Application of DenseNet in Camera Model Identification and Post-processing Detection
Date: 2019-05-27 00:00:00
Tasks: Image Forensics, Overall - Test, Transfer Learning

Camera model identification has earned paramount importance in the field of
image forensics with an upsurge of digitally altered images which are
constantly being shared through websites, media, and social applications. But,
the task of identification becomes quite challenging if metadata are absent
from the image and/or if the image has been post-processed. In this paper, we
present a DenseNet pipeline to solve the problem of identifying the source
camera-model of an image. Our approach is to extract patches of 256*256 from a
labeled image dataset and apply augmentations, i.e., Empirical Mode
Decomposition (EMD). We use this extended dataset to train a Neural Network
with the DenseNet-201 architecture. We concatenate the output features for 3
different sizes (64*64, 128*128, 256*256) and pass them to a secondary network
to make the final prediction. This strategy proves to be very robust for
identifying the source camera model, even when the original image is
post-processed. Our model has been trained and tested on the Forensic
Camera-Model Identification Dataset provided for the IEEE Signal Processing
(SP) Cup 2018. During testing we achieved an overall accuracy of 98.37%, which
is the current state-of-the-art on this dataset using a single model. We used
transfer learning and tested our model on the Dresden Database for Camera Model
Identification, with an overall test accuracy of over 99% for 19 models. In
addition, we demonstrate that the proposed pipeline is suitable for other
image-forensic classification tasks, such as, detecting the type of
post-processing applied to an image with an accuracy of 96.66% -- which
indicates the generality of our approach.
The Indirect Cost of Information
Date: 2020-04-29 00:00:00

We study the indirect cost of information from sequential information cost
minimization. A key sub-additivity condition, together with monotonicity
equivalently characterizes the class of indirect cost functions generated from
any direct information cost. Adding an extra (uniform) posterior separability
condition equivalently characterizes the indirect cost generated from any
direct cost favoring incremental evidences. We also provide the necessary and
sufficient condition when prior independent direct cost generates posterior
separable indirect cost.
Music Sequence Prediction with Mixture Hidden Markov Models
Authors: Li Tao, Choi Minsoo, Fu Kaiming, Lin Lei
Date: 2018-09-27 00:00:00
Tasks: Collaborative Filtering, Music Recommendation, Prediction, Recommendation Systems

Recommendation systems that automatically generate personalized music
playlists for users have attracted tremendous attention in recent years.
Nowadays, most music recommendation systems rely on item-based or user-based
collaborative filtering or content-based approaches. In this paper, we propose
a novel mixture hidden Markov model (HMM) for music play sequence prediction.
We compare the mixture model with state-of-the-art methods and evaluate the
predictions quantitatively and qualitatively on a large-scale real-world
dataset in a Kaggle competition. Results show that our model significantly
outperforms traditional methods as well as other competitors. We conclude by
envisioning a next-generation music recommendation system that integrates our
model with recent advances in deep learning, computer vision, and speech
techniques, and has promising potential in both academia and industry.