pwc-archive/papers-with-abstracts huggingface dataset:
From Videos to URLs: A Multi-Browser Guide To Extract User's Behavior with Optical Character Recognition
Authors: Mojtaba Heidarysafa, James Reed, Kamran Kowsari, April Celeste R. Leviton, Janet I. Warren, Donald E. Brown
Date: 2018-11-15 00:00:00
Tasks: Marketing, Optical Character Recognition, Optical Character Recognition (OCR)

Tracking users' activities on the World Wide Web (WWW) allows researchers to analyze each user's internet behavior as time passes and for the amount of time spent on a particular domain. This analysis can be used in research design, as researchers may access to their participant's behaviors while browsing the web. Web search behavior has been a subject of interest because of its real-world applications in marketing, digital advertisement, and identifying potential threats online. In this paper, we present an image-processing based method to extract domains which are visited by a participant over multiple browsers during a lab session. This method could provide another way to collect users' activities during an online session given that the session recorder collected the data. The method can also be used to collect the textual content of web-pages that an individual visits for later analysis
Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation
Authors: Fan Wang, Bo Zhou, Ke Chen, Tingxiang Fan, Xi Zhang, Jiangyong Li, Hao Tian, Jia Pan
Date: 2018-11-15 00:00:00
Tasks: Autonomous Navigation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Combining deep neural networks with reinforcement learning has shown great
potential in the next-generation intelligent control. However, there are
challenges in terms of safety and cost in practical applications. In this
paper, we propose the Intervention Aided Reinforcement Learning (IARL)
framework, which utilizes human intervened robot-environment interaction to
improve the policy. We used the Unmanned Aerial Vehicle (UAV) as the test
platform. We built neural networks as our policy to map sensor readings to
control signals on the UAV. Our experiment scenarios cover both simulation and
reality. We show that our approach substantially reduces the human intervention
and improves the performance in autonomous navigation, at the same time it
ensures safety and keeps training cost acceptable.
GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition
Authors: Hanqing Chao, Yiwei He, Junping Zhang, Jianfeng Feng
Date: 2018-11-15 00:00:00
Tasks: Gait Recognition, Multiview Gait Recognition

As a unique biometric feature that can be recognized at a distance, gait has
broad applications in crime prevention, forensic identification and social
security. To portray a gait, existing gait recognition methods utilize either a
gait template, where temporal information is hard to preserve, or a gait
sequence, which must keep unnecessary sequential constraints and thus loses the
flexibility of gait recognition. In this paper we present a novel perspective,
where a gait is regarded as a set consisting of independent frames. We propose
a new network named GaitSet to learn identity information from the set. Based
on the set perspective, our method is immune to permutation of frames, and can
naturally integrate frames from different videos which have been filmed under
different scenarios, such as diverse viewing angles, different clothes/carrying
conditions. Experiments show that under normal walking conditions, our
single-model method achieves an average rank-1 accuracy of 95.0% on the CASIA-B
gait dataset and an 87.1% accuracy on the OU-MVLP gait dataset. These results
represent new state-of-the-art recognition accuracy. On various complex
scenarios, our model exhibits a significant level of robustness. It achieves
accuracies of 87.2% and 70.4% on CASIA-B under bag-carrying and coat-wearing
walking conditions, respectively. These outperform the existing best methods by
a large margin. The method presented can also achieve a satisfactory accuracy
with a small number of frames in a test sample, e.g., 82.5% on CASIA-B with
only 7 frames. The source code has been released at
https://github.com/AbnerHqC/GaitSet.
Characterizing Design Patterns of EHR-Driven Phenotype Extraction Algorithms
Authors: Yizhen Zhong, Luke Rasmussen, Yu Deng, Jennifer Pacheco, Maureen Smith, Justin Starren, Wei-Qi Wei, Peter Speltz, Joshua Denny, Nephi Walton, George Hripcsak, Christopher G. Chute, Yuan Luo
Date: 2018-11-15 00:00:00
Tasks: Classification, General Classification

The automatic development of phenotype algorithms from Electronic Health
Record data with machine learning (ML) techniques is of great interest given
the current practice is very time-consuming and resource intensive. The
extraction of design patterns from phenotype algorithms is essential to
understand their rationale and standard, with great potential to automate the
development process. In this pilot study, we perform network visualization on
the design patterns and their associations with phenotypes and sites. We
classify design patterns using the fragments from previously annotated
phenotype algorithms as the ground truth. The classification performance is
used as a proxy for coherence at the attribution level. The bag-of-words
representation with knowledge-based features generated a good performance in
the classification task (0.79 macro-f1 scores). Good classification accuracy
with simple features demonstrated the attribution coherence and the feasibility
of automatic identification of design patterns. Our results point to both the
feasibility and challenges of automatic identification of phenotyping design
patterns, which would power the automatic development of phenotype algorithms.
Implementing a Portable Clinical NLP System with a Common Data Model - a Lisp Perspective
Authors: Yuan Luo, Peter Szolovits
Date: 2018-11-15 00:00:00
Tasks: Computational Phenotyping, Domain Adaptation, Relation Extraction

This paper presents a Lisp architecture for a portable NLP system, termed
LAPNLP, for processing clinical notes. LAPNLP integrates multiple standard,
customized and in-house developed NLP tools. Our system facilitates portability
across different institutions and data systems by incorporating an enriched
Common Data Model (CDM) to standardize necessary data elements. It utilizes
UMLS to perform domain adaptation when integrating generic domain NLP tools. It
also features stand-off annotations that are specified by positional reference
to the original document. We built an interval tree based search engine to
efficiently query and retrieve the stand-off annotations by specifying
positional requirements. We also developed a utility to convert an inline
annotation format to stand-off annotations to enable the reuse of clinical text
datasets with inline annotations. We experimented with our system on several
NLP facilitated tasks including computational phenotyping for lymphoma patients
and semantic relation extraction for clinical notes. These experiments
showcased the broader applicability and utility of LAPNLP.
Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network
Authors: Huicheng Liu
Date: 2018-11-15 00:00:00
Tasks: Stock Market Prediction, Stock Trend Prediction

Stock market prediction is one of the most attractive research topic since
the successful prediction on the market's future movement leads to significant
profit. Traditional short term stock market predictions are usually based on
the analysis of historical market data, such as stock prices, moving averages
or daily returns. However, financial news also contains useful information on
public companies and the market. Existing methods in finance literature exploit
sentiment signal features, which are limited by not considering factors such as
events and the news context. We address this issue by leveraging deep neural
models to extract rich semantic features from news text. In particular, a
Bidirectional-LSTM are used to encode the news text and capture the context
information, self attention mechanism are applied to distribute attention on
most relative words, news and days. In terms of predicting directional changes
in both Standard & Poor's 500 index and individual companies stock price, we
show that this technique is competitive with other state of the art approaches,
demonstrating the effectiveness of recent NLP technology advances for
computational finance.
Improving Skin Condition Classification with a Question Answering Model
Authors: Mohamed Akrout, Amir-Massoud Farahmand, Tory Jarmain
Date: 2018-11-15 00:00:00
Tasks: Classification, General Classification, Question Answering

We present a skin condition classification methodology based on a sequential
pipeline of a pre-trained Convolutional Neural Network (CNN) and a Question
Answering (QA) model. This method enables us to not only increase the
classification confidence and accuracy of the deployed CNN system, but also
enables the emulation of the conventional approach of doctors asking the
relevant questions in refining the ultimate diagnosis and differential. By
combining the CNN output in the form of classification probabilities as a prior
to the QA model and the image textual description, we greedily ask the best
symptom that maximizes the information gain over symptoms. We demonstrate that
combining the QA model with the CNN increases the accuracy up to 10% as
compared to the CNN alone, and more than 30% as compared to the QA model alone.
Exploiting Sentence Embedding for Medical Question Answering
Authors: Yu Hao, Xien Liu, Ji Wu, Ping Lv
Date: 2018-11-15 00:00:00
Tasks: Medical Question Answering, Question Answering, Sentence, Sentence Embedding, Sentence-Embedding

Despite the great success of word embedding, sentence embedding remains a
not-well-solved problem. In this paper, we present a supervised learning
framework to exploit sentence embedding for the medical question answering
task. The learning framework consists of two main parts: 1) a sentence
embedding producing module, and 2) a scoring module. The former is developed
with contextual self-attention and multi-scale techniques to encode a sentence
into an embedding tensor. This module is shortly called Contextual
self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two
scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association
Scoring (SAS). SMS measures similarity while SAS captures association between
sentence pairs: a medical question concatenated with a candidate choice, and a
piece of corresponding supportive evidence. The proposed framework is examined
by two Medical Question Answering(MedicalQA) datasets which are collected from
real-world applications: medical exam and clinical diagnosis based on
electronic medical records (EMR). The comparison results show that our proposed
framework achieved significant improvements compared to competitive baseline
approaches. Additionally, a series of controlled experiments are also conducted
to illustrate that the multi-scale strategy and the contextual self-attention
layer play important roles for producing effective sentence embedding, and the
two kinds of scoring strategies are highly complementary to each other for
question answering problems.
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Authors: Vincent Casser, Soeren Pirk, Reza Mahjourian, Anelia Angelova
Date: 2018-11-15 00:00:00
Tasks: Depth And Camera Motion, Depth Estimation, Monocular Depth Estimation, Motion Estimation, Robot Navigation, Unsupervised Monocular Depth Estimation

Models and examples built with TensorFlow
Orthogonal Policy Gradient and Autonomous Driving Application
Authors: Mincong Luo, Yin Tong, Jiachi Liu
Date: 2018-11-15 00:00:00
Tasks: Autonomous Driving, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

One less addressed issue of deep reinforcement learning is the lack of
generalization capability based on new state and new target, for complex tasks,
it is necessary to give the correct strategy and evaluate all possible actions
for current state. Fortunately, deep reinforcement learning has enabled
enormous progress in both subproblems: giving the correct strategy and
evaluating all actions based on the state.
  In this paper we present an approach called orthogonal policy gradient
descent(OPGD) that can make agent learn the policy gradient based on the
current state and the actions set, by which the agent can learn a policy
network with generalization capability. we evaluate the proposed method on the
3D autonomous driving enviroment TORCS compared with the baseline model,
detailed analyses of experimental results and proofs are also given.
Effect Handling for Composable Program Transformations in Edward2
Authors: Dave Moore, Maria I. Gorinova
Date: 2018-11-15 00:00:00
Tasks: Probabilistic Programming

Algebraic effects and handlers have emerged in the programming languages
community as a convenient, modular abstraction for controlling computational
effects. They have found several applications including concurrent programming,
meta programming, and more recently, probabilistic programming, as part of
Pyro's Poutines library. We investigate the use of effect handlers as a
lightweight abstraction for implementing probabilistic programming languages
(PPLs). We interpret the existing design of Edward2 as an accidental
implementation of an effect-handling mechanism, and extend that design to
support nested, composable transformations. We demonstrate that this enables
straightforward implementation of sophisticated model transformations and
inference algorithms.
Pure-Exploration for Infinite-Armed Bandits with General Arm Reservoirs
Authors: Maryam Aziz, Kevin Jamieson, Javed Aslam
Date: 2018-11-15 00:00:00

This paper considers a multi-armed bandit game where the number of arms is
much larger than the maximum budget and is effectively infinite. We
characterize necessary and sufficient conditions on the total budget for an
algorithm to return an {\epsilon}-good arm with probability at least 1 -
{\delta}. In such situations, the sample complexity depends on {\epsilon},
{\delta} and the so-called reservoir distribution {\nu} from which the means of
the arms are drawn iid. While a substantial literature has developed around
analyzing specific cases of {\nu} such as the beta distribution, our analysis
makes no assumption about the form of {\nu}. Our algorithm is based on
successive halving with the surprising exception that arms start to be
discarded after just a single pull, requiring an analysis that goes beyond
concentration alone. The provable correctness of this algorithm also provides
an explanation for the empirical observation that the most aggressive bracket
of the Hyperband algorithm of Li et al. (2017) for hyperparameter tuning is
almost always best.
Real-time Power System State Estimation and Forecasting via Deep Neural Networks
Authors: Liang Zhang, Gang Wang, Georgios B. Giannakis
Date: 2018-11-15 00:00:00
Tasks: Rolling Shutter Correction, State Estimation, Time Series Analysis

Contemporary power grids are being challenged by rapid voltage fluctuations
that are caused by large-scale deployment of renewable generation, electric
vehicles, and demand response programs. In this context, monitoring the grid's
operating conditions in real time becomes increasingly critical. With the
emergent large scale and nonconvexity however, the existing power system state
estimation (PSSE) schemes become computationally expensive or yield suboptimal
performance. To bypass these hurdles, this paper advocates deep neural networks
(DNNs) for real-time power system monitoring. By unrolling an iterative
physics-based prox-linear solver, a novel model-specific DNN is developed for
real-time PSSE with affordable training and minimal tuning effort. To further
enable system awareness even ahead of the time horizon, as well as to endow the
DNN-based estimator with resilience, deep recurrent neural networks (RNNs) are
also pursued for power system state forecasting. Deep RNNs leverage the
long-term nonlinear dependencies present in the historical voltage time series
to enable forecasting, and they are easy to implement. Numerical tests showcase
improved performance of the proposed DNN-based estimation and forecasting
approaches compared with existing alternatives. In real load data experiments
on the IEEE 118-bus benchmark system, the novel model-specific DNN-based PSSE
scheme outperforms nearly by an order-of-magnitude the competing alternatives,
including the widely adopted Gauss-Newton PSSE solver.
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks
Authors: Jing Shi, Jiaming Xu, Yiqun Yao, Bo Xu
Date: 2018-11-15 00:00:00
Tasks: Deep Reinforcement Learning, One-Shot Learning, Outlier Detection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep neural networks have shown superior performance in many regimes to
remember familiar patterns with large amounts of data. However, the standard
supervised deep learning paradigm is still limited when facing the need to
learn new concepts efficiently from scarce data. In this paper, we present a
memory-augmented neural network which is motivated by the process of human
concept learning. The training procedure, imitating the concept formation
course of human, learns how to distinguish samples from different classes and
aggregate samples of the same kind. In order to better utilize the advantages
originated from the human behavior, we propose a sequential process, during
which the network should decide how to remember each sample at every step. In
this sequential process, a stable and interactive memory serves as an important
module. We validate our model in some typical one-shot learning tasks and also
an exploratory outlier detection problem. In all the experiments, our model
gets highly competitive to reach or outperform those strong baselines.
Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon
Authors: Yoshua Bengio, Andrea Lodi, Antoine Prouvost
Date: 2018-11-15 00:00:00
Tasks: BIG-bench Machine Learning, Combinatorial Optimization

This paper surveys the recent attempts, both from the machine learning and operations research communities, at leveraging machine learning to solve combinatorial optimization problems. Given the hard nature of these problems, state-of-the-art algorithms rely on handcrafted heuristics for making decisions that are otherwise too expensive to compute or mathematically not well defined. Thus, machine learning looks like a natural candidate to make such decisions in a more principled and optimized way. We advocate for pushing further the integration of machine learning and combinatorial optimization and detail a methodology to do so. A main point of the paper is seeing generic optimization problems as data points and inquiring what is the relevant distribution of problems to use for learning on a given task.
Deep Learning in the Wavelet Domain
Authors: Fergal Cotter, Nick Kingsbury
Date: 2018-11-14 00:00:00
Tasks: Deep Learning

This paper examines the possibility of, and the possible advantages to
learning the filters of convolutional neural networks (CNNs) for image analysis
in the wavelet domain. We are stimulated by both Mallat's scattering transform
and the idea of filtering in the Fourier domain. It is important to explore new
spaces in which to learn, as these may provide inherent advantages that are not
available in the pixel space. However, the scattering transform is limited by
its inability to learn in between scattering orders, and any Fourier domain
filtering is limited by the large number of filter parameters needed to get
localized filters. Instead we consider filtering in the wavelet domain with
learnable filters. The wavelet space allows us to have local, smooth filters
with far fewer parameters, and learnability can give us flexibility. We present
a novel layer which takes CNN activations into the wavelet space, learns
parameters and returns to the pixel space. This allows it to be easily dropped
in to any neural network without affecting the structure. As part of this work,
we show how to pass gradients through a multirate system and give preliminary
results.
Predictive Modeling with Delayed Information: a Case Study in E-commerce Transaction Fraud Control
Authors: Junxuan Li, Yung-wen Liu, Yuting Jia, Yifei Ren, Jay Nanduri
Date: 2018-11-14 00:00:00
Tasks: Decision Making

In Business Intelligence, accurate predictive modeling is the key for
providing adaptive decisions. We studied predictive modeling problems in this
research which was motivated by real-world cases that Microsoft data scientists
encountered while dealing with e-commerce transaction fraud control decisions
using transaction streaming data in an uncertain probabilistic decision
environment. The values of most online transactions related features can return
instantly, while the true fraud labels only return after a stochastic delay.
Using partially mature data directly for predictive modeling in an uncertain
probabilistic decision environment would lead to significant inaccuracy on risk
decision-making. To improve accurate estimation of the probabilistic prediction
environment, which leads to more accurate predictive modeling, two frameworks,
Current Environment Inference (CEI) and Future Environment Inference (FEI), are
proposed. These frameworks generated decision environment related features
using long-term fully mature and short-term partially mature data, and the
values of those features were estimated using varies of learning methods,
including linear regression, random forest, gradient boosted tree, artificial
neural network, and recurrent neural network. Performance tests were conducted
using some e-commerce transaction data from Microsoft. Testing results
suggested that proposed frameworks significantly improved the accuracy of
decision environment estimation.
Multivariate Time-series Similarity Assessment via Unsupervised Representation Learning and Stratified Locality Sensitive Hashing: Application to Early Acute Hypotensive Episode Detection
Authors: Jwala Dhamala, Emmanuel Azuh, Abdullah Al-Dujaili, Jonathan Rubin, Una-May O'Reilly
Date: 2018-11-14 00:00:00
Tasks: General Classification, Representation Learning, Time Series, Time Series Analysis

Timely prediction of clinically critical events in Intensive Care Unit (ICU)
is important for improving care and survival rate. Most of the existing
approaches are based on the application of various classification methods on
explicitly extracted statistical features from vital signals. In this work, we
propose to eliminate the high cost of engineering hand-crafted features from
multivariate time-series of physiologic signals by learning their
representation with a sequence-to-sequence auto-encoder. We then propose to
hash the learned representations to enable signal similarity assessment for the
prediction of critical events. We apply this methodological framework to
predict Acute Hypotensive Episodes (AHE) on a large and diverse dataset of
vital signal recordings. Experiments demonstrate the ability of the presented
framework in accurately predicting an upcoming AHE.
Deep Neural Networks based Modrec: Some Results with Inter-Symbol Interference and Adversarial Examples
Authors: S. Asim Ahmed, Subhashish Chakravarty, Michael Newhouse
Date: 2018-11-14 00:00:00
Tasks: Automatic Modulation Recognition

Recent successes and advances in Deep Neural Networks (DNN) in machine vision
and Natural Language Processing (NLP) have motivated their use in traditional
signal processing and communications systems. In this paper, we present results
of such applications to the problem of automatic modulation recognition.
Variations in wireless communication channels are represented by statistical
channel models and their parameterization will increase with the advent of 5G.
In this paper, we report effect of simple two path channel model on our naive
deep neural network based implementation. We also report impact of adversarial
perturbation to the input signal.