pwc-archive/papers-with-abstracts huggingface dataset:
Discretizing Continuous Action Space for On-Policy Optimization
Authors: Yunhao Tang, Shipra Agrawal
Date: 2019-01-29 00:00:00
Tasks: continuous-control, Continuous Control, Inductive Bias

In this work, we show that discretizing action space for continuous control is a simple yet powerful technique for on-policy optimization. The explosion in the number of discrete actions can be efficiently addressed by a policy with factorized distribution across action dimensions. We show that the discrete policy achieves significant performance gains with state-of-the-art on-policy optimization algorithms (PPO, TRPO, ACKTR) especially on high-dimensional tasks with complex dynamics. Additionally, we show that an ordinal parameterization of the discrete distribution can introduce the inductive bias that encodes the natural ordering between discrete actions. This ordinal architecture further significantly improves the performance of PPO/TRPO.
Impact of Training Dataset Size on Neural Answer Selection Models
Authors: Trond Linjordet, Krisztian Balog
Date: 2019-01-29 00:00:00
Tasks: Answer Selection

It is held as a truism that deep neural networks require large datasets to
train effective models. However, large datasets, especially with high-quality
labels, can be expensive to obtain. This study sets out to investigate (i) how
large a dataset must be to train well-performing models, and (ii) what impact
can be shown from fractional changes to the dataset size. A practical method to
investigate these questions is to train a collection of deep neural answer
selection models using fractional subsets of varying sizes of an initial
dataset. We observe that dataset size has a conspicuous lack of effect on the
training of some of these models, bringing the underlying algorithms into
question.
Predictive Maintenance in Photovoltaic Plants with a Big Data Approach
Authors: Alessandro Betti, Maria Luisa Lo Trovato, Fabio Salvatore Leonardi, Giuseppe Leotta, Fabrizio Ruffini, Ciro Lanzetta
Date: 2019-01-29 00:00:00
Tasks: Clustering, Prediction

This paper presents a novel and flexible solution for fault prediction based
on data collected from SCADA system. Fault prediction is offered at two
different levels based on a data-driven approach: (a) generic fault/status
prediction and (b) specific fault class prediction, implemented by means of two
different machine learning based modules built on an unsupervised clustering
algorithm and a Pattern Recognition Neural Network, respectively. Model has
been assessed on a park of six photovoltaic (PV) plants up to 10 MW and on more
than one hundred inverter modules of three different technology brands. The
results indicate that the proposed method is effective in (a) predicting
incipient generic faults up to 7 days in advance with sensitivity up to 95% and
(b) anticipating damage of specific fault classes with times ranging from few
hours up to 7 days. The model is easily deployable for on-line monitoring of
anomalies on new PV plants and technologies, requiring only the availability of
historical SCADA and fault data, fault taxonomy and inverter electrical
datasheet. Keywords: Data Mining, Fault Prediction, Inverter Module, Key
Performance Indicator, Lost Production
Automated Analysis, Reporting, and Archiving for Robotic Nondestructive Assay of Holdup Deposits
Authors: Heather Jones, Siri Maley, Kenji Yonekawa, Mohammadreza Mousaei, J. David Yesso, David Kohanbash, William Whittaker
Date: 2019-01-29 00:00:00

To decommission deactivated gaseous diffusion enrichment facilities, miles of
contaminated pipe must be measured. The current method requires thousands of
manual measurements, repeated manual data transcription, and months of manual
analysis. The Pipe Crawling Activity Measurement System (PCAMS), developed by
Carnegie Mellon University and in commissioning for use at the DOE Portsmouth
Gaseous Diffusion Enrichment Facility, uses a robot to measure Uranium-235 from
inside pipes and automatically log the data. Radiation measurements, as well as
imagery, geometric modeling, and precise measurement positioning data are
digitally transferred to the PCAMS server. On the server, data can be
automatically processed in minutes and summarized for analyst review.
Measurement reports are auto-generated with the push of a button. A database
specially-configured to hold heterogeneous data such as spectra, images, and
robot trajectories serves as archive. This paper outlines the features and
design of the PCAMS Post-Processing Software, currently in commissioning for
use at the Portsmouth Gaseous Diffusion Enrichment Facility. The analysis
process, the analyst interface to the system, and the content of auto-generated
reports are each described. Example pipe-interior geometric surface models,
illustration of how key report features apply in operational runs, and user
feedback are discussed.
Automated Prototype for Asteroids Detection
Authors: D. Copandean, O. Vaduvescu, D. Gorgan
Date: 2019-01-29 00:00:00

Near Earth Asteroids (NEAs) are discovered daily, mainly by few major
surveys, nevertheless many of them remain unobserved for years, even decades.
Even so, there is room for new discoveries, including those submitted by
smaller projects and amateur astronomers. Besides the well-known surveys that
have their own automated system of asteroid detection, there are only a few
software solutions designed to help amateurs and mini-surveys in NEAs
discovery. Some of these obtain their results based on the blink method in
which a set of reduced images are shown one after another and the astronomer
has to visually detect real moving objects in a series of images. This
technique becomes harder with the increase in size of the CCD cameras. Aiming
to replace manual detection we propose an automated pipeline prototype for
asteroids detection, written in Python under Linux, which calls some 3rd party
astrophysics libraries.
Short-term Demand Forecasting for Online Car-hailing Services using Recurrent Neural Networks
Authors: Alireza Nejadettehad, Hamid Mahini, Behnam Bahrak
Date: 2019-01-29 00:00:00
Tasks: Demand Forecasting, Management, Time Series, Time Series Analysis, Time Series Forecasting, Time Series Prediction

Short-term traffic flow prediction is one of the crucial issues in
intelligent transportation system, which is an important part of smart cities.
Accurate predictions can enable both the drivers and the passengers to make
better decisions about their travel route, departure time and travel origin
selection, which can be helpful in traffic management. Multiple models and
algorithms based on time series prediction and machine learning were applied to
this issue and achieved acceptable results. Recently, the availability of
sufficient data and computational power, motivates us to improve the prediction
accuracy via deep-learning approaches. Recurrent neural networks have become
one of the most popular methods for time series forecasting, however, due to
the variety of these networks, the question that which type is the most
appropriate one for this task remains unsolved. In this paper, we use three
kinds of recurrent neural networks including simple RNN units, GRU and LSTM
neural network to predict short-term traffic flow. The dataset from TAP30
Corporation is used for building the models and comparing RNNs with several
well-known models, such as DEMA, LASSO and XGBoost. The results show that all
three types of RNNs outperform the others, however, more simple RNNs such as
simple recurrent units and GRU perform work better than LSTM in terms of
accuracy and training time.
Learning Context-Dependent Choice Functions
Authors: Karlson Pfannschmidt, Pritha Gupta, Björn Haddenhorst, Eyke Hüllermeier
Date: 2019-01-29 00:00:00

Choice functions accept a set of alternatives as input and produce a preferred subset of these alternatives as output. We study the problem of learning such functions under conditions of context-dependence of preferences, which means that the preference in favor of a certain choice alternative may depend on what other options are also available. In spite of its practical relevance, this kind of context-dependence has received little attention in preference learning so far. We propose a suitable model based on context-dependent (latent) utility functions, thereby reducing the problem to the task of learning such utility functions. Practically, this comes with a number of challenges. For example, the set of alternatives provided as input to a choice function can be of any size, and the output of the function should not depend on the order in which the alternatives are presented. To meet these requirements, we propose two general approaches based on two representations of context-dependent utility functions, as well as instantiations in the form of appropriate end-to-end trainable neural network architectures. Moreover, to demonstrate the performance of both networks, we present extensive empirical evaluations on both synthetic and real-world datasets.
Decoder-tailored Polar Code Design Using the Genetic Algorithm
Authors: Ahmed Elkelesh, Moustafa Ebada, Sebastian Cammerer, Stephan ten Brink
Date: 2019-01-28 00:00:00
Tasks: Decoder, Playing the Game of 2048

We propose a new framework for constructing polar codes (i.e., selecting the
frozen bit positions) for arbitrary channels, and tailored to a given decoding
algorithm, rather than based on the (not necessarily optimal) assumption of
successive cancellation (SC) decoding. The proposed framework is based on the
Genetic Algorithm (GenAlg), where populations (i.e., collections) of
information sets evolve successively via evolutionary transformations based on
their individual error-rate performance. These populations converge towards an
information set that fits both the decoding behavior and the defined channel.
Using our proposed algorithm over the additive white Gaussian noise (AWGN)
channel, we construct a polar code of length 2048 with code rate 0.5, without
the CRC-aid, tailored to plain successive cancellation list (SCL) decoding,
achieving the same error-rate performance as the CRC-aided SCL decoding, and
leading to a coding gain of 1 dB at BER of $10^{-6}$. Further, a belief
propagation (BP)-tailored construction approaches the SCL error-rate
performance without any modifications in the decoding algorithm itself. The
performance gains can be attributed to the significant reduction in the total
number of low-weight codewords. To demonstrate the flexibility, coding gains
for the Rayleigh channel are shown under SCL and BP decoding. Besides
improvements in error-rate performance, we show that, when required, the GenAlg
can be also set up to reduce the decoding complexity, e.g., the SCL list size
or the number of BP iterations can be reduced, while maintaining the same
error-rate performance.
Additive Margin SincNet for Speaker Recognition
Authors: João Antônio Chagas Nunes, David Macêdo, Cleber Zanchettin
Date: 2019-01-28 00:00:00
Tasks: Deep Learning, Speaker Recognition

Speaker Recognition is a challenging task with essential applications such as
authentication, automation, and security. The SincNet is a new deep learning
based model which has produced promising results to tackle the mentioned task.
To train deep learning systems, the loss function is essential to the network
performance. The Softmax loss function is a widely used function in deep
learning methods, but it is not the best choice for all kind of problems. For
distance-based problems, one new Softmax based loss function called Additive
Margin Softmax (AM-Softmax) is proving to be a better choice than the
traditional Softmax. The AM-Softmax introduces a margin of separation between
the classes that forces the samples from the same class to be closer to each
other and also maximizes the distance between classes. In this paper, we
propose a new approach for speaker recognition systems called AM-SincNet, which
is based on the SincNet but uses an improved AM-Softmax layer. The proposed
method is evaluated in the TIMIT dataset and obtained an improvement of
approximately 40% in the Frame Error Rate compared to SincNet.
Fair Regression for Health Care Spending
Authors: Anna Zink, Sherri Rose
Date: 2019-01-28 00:00:00
Tasks: Fairness, regression

The distribution of health care payments to insurance plans has substantial consequences for social policy. Risk adjustment formulas predict spending in health insurance markets in order to provide fair benefits and health care coverage for all enrollees, regardless of their health status. Unfortunately, current risk adjustment formulas are known to underpredict spending for specific groups of enrollees leading to undercompensated payments to health insurers. This incentivizes insurers to design their plans such that individuals in undercompensated groups will be less likely to enroll, impacting access to health care for these groups. To improve risk adjustment formulas for undercompensated groups, we expand on concepts from the statistics, computer science, and health economics literature to develop new fair regression methods for continuous outcomes by building fairness considerations directly into the objective function. We additionally propose a novel measure of fairness while asserting that a suite of metrics is necessary in order to evaluate risk adjustment formulas more fully. Our data application using the IBM MarketScan Research Databases and simulation studies demonstrate that these new fair regression methods may lead to massive improvements in group fairness (e.g., 98%) with only small reductions in overall fit (e.g., 4%).
Automatic end-to-end De-identification: Is high accuracy the only metric?
Authors: Vithya Yogarajan, Bernhard Pfahringer, Michael Mayo
Date: 2019-01-27 00:00:00
Tasks: De-identification

De-identification of electronic health records (EHR) is a vital step towards
advancing health informatics research and maximising the use of available data.
It is a two-step process where step one is the identification of protected
health information (PHI), and step two is replacing such PHI with surrogates.
Despite the recent advances in automatic de-identification of EHR, significant
obstacles remain if the abundant health data available are to be used to the
full potential. Accuracy in de-identification could be considered a necessary,
but not sufficient condition for the use of EHR without individual patient
consent. We present here a comprehensive review of the progress to date, both
the impressive successes in achieving high accuracy and the significant risks
and challenges that remain. To best of our knowledge, this is the first paper
to present a complete picture of end-to-end automatic de-identification. We
review 18 recently published automatic de-identification systems -designed to
de-identify EHR in the form of free text- to show the advancements made in
improving the overall accuracy of the system, and in identifying individual
PHI. We argue that despite the improvements in accuracy there remain challenges
in surrogate generation and replacements of identified PHIs, and the risks
posed to patient protection and privacy.
Pixelated Semantic Colorization
Authors: Jiaojiao Zhao, Jungong Han, Ling Shao, Cees G. M. Snoek
Date: 2019-01-27 00:00:00
Tasks: Colorization, Image Colorization, Object, Semantic Segmentation

While many image colorization algorithms have recently shown the capability
of producing plausible color versions from gray-scale photographs, they still
suffer from limited semantic understanding. To address this shortcoming, we
propose to exploit pixelated object semantics to guide image colorization. The
rationale is that human beings perceive and distinguish colors based on the
semantic categories of objects. Starting from an autoregressive model, we
generate image color distributions, from which diverse colored results are
sampled. We propose two ways to incorporate object semantics into the
colorization model: through a pixelated semantic embedding and a pixelated
semantic generator. Specifically, the proposed convolutional neural network
includes two branches. One branch learns what the object is, while the other
branch learns the object colors. The network jointly optimizes a color
embedding loss, a semantic segmentation loss and a color generation loss, in an
end-to-end fashion. Experiments on PASCAL VOC2012 and COCO-stuff reveal that
our network, when trained with semantic segmentation labels, produces more
realistic and finer results compared to the colorization state-of-the-art.
A Convolutional Neural Network model based on Neutrosophy for Noisy Speech Recognition
Authors: Elyas Rashno, Ahmad Akbari, Babak Nasersharif
Date: 2019-01-27 00:00:00
Tasks: General Classification, Noisy Speech Recognition, speech-recognition, Speech Recognition

Convolutional neural networks are sensitive to unknown noisy condition in the
test phase and so their performance degrades for the noisy data classification
task including noisy speech recognition. In this research, a new convolutional
neural network (CNN) model with data uncertainty handling; referred as NCNN
(Neutrosophic Convolutional Neural Network); is proposed for classification
task. Here, speech signals are used as input data and their noise is modeled as
uncertainty. In this task, using speech spectrogram, a definition of
uncertainty is proposed in neutrosophic (NS) domain. Uncertainty is computed
for each Time-frequency point of speech spectrogram as like a pixel. Therefore,
uncertainty matrix with the same size of spectrogram is created in NS domain.
In the next step, a two parallel paths CNN classification model is proposed.
Speech spectrogram is used as input of the first path and uncertainty matrix
for the second path. The outputs of two paths are combined to compute the final
output of the classifier. To show the effectiveness of the proposed method, it
has been compared with conventional CNN on the isolated words of Aurora2
dataset. The proposed method achieves the average accuracy of 85.96 in noisy
train data. It is more robust against Car, Airport and Subway noises with
accuracies 90, 88 and 81 in test sets A, B and C, respectively. Results show
that the proposed method outperforms conventional CNN with the improvement of
6, 5 and 2 percentage in test set A, test set B and test sets C, respectively.
It means that the proposed method is more robust against noisy data and handle
these data effectively.
Impact of Data Pruning on Machine Learning Algorithm Performance
Authors: Arun Thundyill Saseendran, Lovish Setia, Viren Chhabria, Debrup Chakraborty, Aneek Barman Roy
Date: 2019-01-11 00:00:00
Tasks: BIG-bench Machine Learning

Dataset pruning is the process of removing sub-optimal tuples from a dataset
to improve the learning of a machine learning model. In this paper, we compared
the performance of different algorithms, first on an unpruned dataset and then
on an iteratively pruned dataset. The goal was to understand whether an
algorithm (say A) on an unpruned dataset performs better than another algorithm
(say B), will algorithm B perform better on the pruned data or vice-versa. The
dataset chosen for our analysis is a subset of the largest movie ratings
database publicly available on the internet, IMDb [1]. The learning objective
of the model was to predict the categorical rating of a movie among 5 bins:
poor, average, good, very good, excellent. The results indicated that an
algorithm that performed better on an unpruned dataset also performed better on
a pruned dataset.
Bayesian Networks based Hybrid Quantum-Classical Machine Learning Approach to Elucidate Gene Regulatory Pathways
Authors: Radhakrishnan Balu, Ajinkya Borle
Date: 2019-01-23 00:00:00
Tasks: ARC, BIG-bench Machine Learning

We report a scalable hybrid quantum-classical machine learning framework to
build Bayesian networks (BN) that captures the conditional dependence and
causal relationships of random variables. The generation of a BN consists of
finding a directed acyclic graph (DAG) and the associated joint probability
distribution of the nodes consistent with a given dataset. This is a
combinatorial problem of structural learning of the underlying graph, starting
from a single node and building one arc at a time, that fits a given ensemble
using maximum likelihood estimators (MLE). It is cast as an optimization
problem that consists of a scoring step performed on a classical computer,
penalties for acyclicity and number of parents allowed constraints, and a
search step implemented using a quantum annealer. We have assumed uniform
priors in deriving the Bayesian network that can be relaxed by formulating the
problem as an estimation Dirichlet parameters. We demonstrate the utility of
the framework by applying to the problem of elucidating the gene regulatory
network for the MAPK/Raf pathway in human T-cells using proteomics data where
the concentration of proteins, nodes of the BN, are interpreted as
probabilities.
Generative Adversarial Network with Multi-Branch Discriminator for Cross-Species Image-to-Image Translation
Authors: Ziqiang Zheng, Zhibin Yu, Haiyong Zheng, Yang Wu, Bing Zheng, Ping Lin
Date: 2019-01-24 00:00:00
Tasks: Generative Adversarial Network, Image Generation, Image-to-Image Translation, Translation

Current approaches have made great progress on image-to-image translation
tasks benefiting from the success of image synthesis methods especially
generative adversarial networks (GANs). However, existing methods are limited
to handling translation tasks between two species while keeping the content
matching on the semantic level. A more challenging task would be the
translation among more than two species. To explore this new area, we propose a
simple yet effective structure of a multi-branch discriminator for enhancing an
arbitrary generative adversarial architecture (GAN), named GAN-MBD. It takes
advantage of the boosting strategy to break a common discriminator into several
smaller ones with fewer parameters, which can enhance the generation and
synthesis abilities of GANs efficiently and effectively. Comprehensive
experiments show that the proposed multi-branch discriminator can dramatically
improve the performance of popular GANs on cross-species image-to-image
translation tasks while reducing the number of parameters for computation. The
code and some datasets are attached as supplementary materials for reference.
Quantifying Legibility of Indoor Spaces Using Deep Convolutional Neural Networks: Case Studies in Train Stations
Authors: Zhoutong Wang, Qianhui Liang, Fabio Duarte, Fan Zhang, Louis Charron, Lenna Johnsen, Bill Cai, Carlo Ratti
Date: 2019-01-22 00:00:00
Tasks: Survey

Legibility is the extent to which a space can be easily recognized.
Evaluating legibility is particularly desirable in indoor spaces, since it has
a large impact on human behavior and the efficiency of space utilization.
However, indoor space legibility has only been studied through survey and
trivial simulations and lacks reliable quantitative measurement. We utilized a
Deep Convolutional Neural Network (DCNN), which is structurally similar to a
human perception system, to model legibility in indoor spaces. To implement the
modeling of legibility for any indoor spaces, we designed an end-to-end
processing pipeline from indoor data retrieving to model training to spatial
legibility analysis. Although the model performed very well (98% top-1
accuracy) overall, there are still discrepancies in accuracy among different
spaces, reflecting legibility differences. To prove the validity of the
pipeline, we deployed a survey on Amazon Mechanical Turk, collecting 4,015
samples. The human samples showed a similar behavior pattern and mechanism as
the DCNN models. Further, we used model results to visually explain legibility
in different architectural programs, building age, building style, visual
clusterings of spaces and visual explanations for building age and
architectural functions.