pwc-archive/papers-with-abstracts huggingface dataset:
Sequence Mining and Pattern Analysis in Drilling Reports with Deep Natural Language Processing
Authors: Júlio Hoffimann, Youli Mao, Avinash Wesley, Aimee Taylor
Date: 2017-12-05 00:00:00
Tasks: General Classification, Information Retrieval, Retrieval

Drilling activities in the oil and gas industry have been reported over
decades for thousands of wells on a daily basis, yet the analysis of this text
at large-scale for information retrieval, sequence mining, and pattern analysis
is very challenging. Drilling reports contain interpretations written by
drillers from noting measurements in downhole sensors and surface equipment,
and can be used for operation optimization and accident mitigation. In this
initial work, a methodology is proposed for automatic classification of
sentences written in drilling reports into three relevant labels (EVENT,
SYMPTOM and ACTION) for hundreds of wells in an actual field. Some of the main
challenges in the text corpus were overcome, which include the high frequency
of technical symbols, mistyping/abbreviation of technical terms, and the
presence of incomplete sentences in the drilling reports. We obtain
state-of-the-art classification accuracy within this technical language and
illustrate advanced queries enabled by the tool.
Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciences
Authors: Tim Miller, Piers Howe, Liz Sonenberg
Date: 2017-12-02 00:00:00
Tasks: Philosophy

In his seminal book `The Inmates are Running the Asylum: Why High-Tech
Products Drive Us Crazy And How To Restore The Sanity' [2004, Sams
Indianapolis, IN, USA], Alan Cooper argues that a major reason why software is
often poorly designed (from a user perspective) is that programmers are in
charge of design decisions, rather than interaction designers. As a result,
programmers design software for themselves, rather than for their target
audience, a phenomenon he refers to as the `inmates running the asylum'. This
paper argues that explainable AI risks a similar fate. While the re-emergence
of explainable AI is positive, this paper argues most of us as AI researchers
are building explanatory agents for ourselves, rather than for the intended
users. But explainable AI is more likely to succeed if researchers and
practitioners understand, adopt, implement, and improve models from the vast
and valuable bodies of research in philosophy, psychology, and cognitive
science, and if evaluation of these models is focused more on people than on
technology. From a light scan of literature, we demonstrate that there is
considerable scope to infuse more results from the social and behavioural
sciences into explainable AI, and present some key results from these fields
that are relevant to explainable AI.
Deep linear neural networks with arbitrary loss: All local minima are global
Authors: Thomas Laurent, James Von Brecht
Date: 2017-12-05 00:00:00
Tasks: All

We consider deep linear networks with arbitrary convex differentiable loss.
We provide a short and elementary proof of the fact that all local minima are
global minima if the hidden layers are either 1) at least as wide as the input
layer, or 2) at least as wide as the output layer. This result is the strongest
possible in the following sense: If the loss is convex and Lipschitz but not
differentiable then deep linear networks can have sub-optimal local minima.
AWE-CM Vectors: Augmenting Word Embeddings with a Clinical Metathesaurus
Authors: Willie Boag, Hassan Kané
Date: 2017-12-05 00:00:00
Tasks: Word Embeddings

In recent years, word embeddings have been surprisingly effective at
capturing intuitive characteristics of the words they represent. These vectors
achieve the best results when training corpora are extremely large, sometimes
billions of words. Clinical natural language processing datasets, however, tend
to be much smaller. Even the largest publicly-available dataset of medical
notes is three orders of magnitude smaller than the dataset of the oft-used
"Google News" word vectors. In order to make up for limited training data
sizes, we encode expert domain knowledge into our embeddings. Building on a
previous extension of word2vec, we show that generalizing the notion of a
word's "context" to include arbitrary features creates an avenue for encoding
domain knowledge into word embeddings. We show that the word vectors produced
by this method outperform their text-only counterparts across the board in
correlation with clinical experts.
Data Dropout in Arbitrary Basis for Deep Network Regularization
Authors: Mostafa Rahmani, George Atia
Date: 2017-12-04 00:00:00

An important problem in training deep networks with high capacity is to
ensure that the trained network works well when presented with new inputs
outside the training dataset. Dropout is an effective regularization technique
to boost the network generalization in which a random subset of the elements of
the given data and the extracted features are set to zero during the training
process. In this paper, a new randomized regularization technique in which we
withhold a random part of the data without necessarily turning off the
neurons/data-elements is proposed. In the proposed method, of which the
conventional dropout is shown to be a special case, random data dropout is
performed in an arbitrary basis, hence the designation Generalized Dropout. We
also present a framework whereby the proposed technique can be applied
efficiently to convolutional neural networks. The presented numerical
experiments demonstrate that the proposed technique yields notable performance
gain. Generalized Dropout provides new insight into the idea of dropout, shows
that we can achieve different performance gains by using different bases
matrices, and opens up a new research question as of how to choose optimal
bases matrices that achieve maximal performance gain.
Learning to Fuse Music Genres with Generative Adversarial Dual Learning
Authors: Zhiqian Chen, Chih-Wei Wu, Yen-Cheng Lu, Alexander Lerch, Chang-Tien Lu
Date: 2017-12-05 00:00:00
Tasks: Music Generation

FusionGAN is a novel genre fusion framework for music generation that
integrates the strengths of generative adversarial networks and dual learning.
In particular, the proposed method offers a dual learning extension that can
effectively integrate the styles of the given domains. To efficiently quantify
the difference among diverse domains and avoid the vanishing gradient issue,
FusionGAN provides a Wasserstein based metric to approximate the distance
between the target domain and the existing domains. Adopting the Wasserstein
distance, a new domain is created by combining the patterns of the existing
domains using adversarial learning. Experimental results on public music
datasets demonstrated that our approach could effectively merge two genres.
Multimodal Storytelling via Generative Adversarial Imitation Learning
Authors: Zhiqian Chen, Xuchao Zhang, Arnold P. Boedihardjo, Jing Dai, Chang-Tien Lu
Date: 2017-12-05 00:00:00
Tasks: Imitation Learning

Deriving event storylines is an effective summarization method to succinctly
organize extensive information, which can significantly alleviate the pain of
information overload. The critical challenge is the lack of widely recognized
definition of storyline metric. Prior studies have developed various approaches
based on different assumptions about users' interests. These works can extract
interesting patterns, but their assumptions do not guarantee that the derived
patterns will match users' preference. On the other hand, their exclusiveness
of single modality source misses cross-modality information. This paper
proposes a method, multimodal imitation learning via generative adversarial
networks(MIL-GAN), to directly model users' interests as reflected by various
data. In particular, the proposed model addresses the critical challenge by
imitating users' demonstrated storylines. Our proposed model is designed to
learn the reward patterns given user-provided storylines and then applies the
learned policy to unseen data. The proposed approach is demonstrated to be
capable of acquiring the user's implicit intent and outperforming competing
methods by a substantial margin with a user study.
Automatic detection and decoding of honey bee waggle dances
Authors: Fernando Wario, Benjamin Wild, Raúl Rojas, Tim Landgraf
Date: 2017-08-22 00:00:00

The waggle dance is one of the most popular examples of animal communication.
Forager bees direct their nestmates to profitable resources via a complex motor
display. Essentially, the dance encodes the polar coordinates to the resource
in the field. Unemployed foragers follow the dancer's movements and then search
for the advertised spots in the field. Throughout the last decades, biologists
have employed different techniques to measure key characteristics of the waggle
dance and decode the information it conveys. Early techniques involved the use
of protractors and stopwatches to measure the dance orientation and duration
directly from the observation hive. Recent approaches employ digital video
recordings and manual measurements on screen. However, manual approaches are
very time-consuming. Most studies, therefore, regard only small numbers of
animals in short periods of time. We have developed a system capable of
automatically detecting, decoding and mapping communication dances in
real-time. In this paper, we describe our recording setup, the image processing
steps performed for dance detection and decoding and an algorithm to map dances
to the field. The proposed system performs with a detection accuracy of
90.07\%. The decoded waggle orientation has an average error of -2.92{\deg}
($\pm$ 7.37{\deg} ), well within the range of human error. To evaluate and
exemplify the system's performance, a group of bees was trained to an
artificial feeder, and all dances in the colony were automatically detected,
decoded and mapped. The system presented here is the first of this kind made
publicly available, including source code and hardware specifications. We hope
this will foster quantitative analyses of the honey bee waggle dance.
Zone-based Keyword Spotting in Bangla and Devanagari Documents
Authors: Ayan Kumar Bhunia, Partha Pratim Roy, Umapada Pal
Date: 2017-12-05 00:00:00
Tasks: Keyword Spotting, Segmentation

In this paper we present a word spotting system in text lines for offline
Indic scripts such as Bangla (Bengali) and Devanagari. Recently, it was shown
that zone-wise recognition method improves the word recognition performance
than conventional full word recognition system in Indic scripts. Inspired with
this idea we consider the zone segmentation approach and use middle zone
information to improve the traditional word spotting performance. To avoid the
problem of zone segmentation using heuristic approach, we propose here an HMM
based approach to segment the upper and lower zone components from the text
line images. The candidate keywords are searched from a line without segmenting
characters or words. Also, we propose a novel feature combining foreground and
background information of text line images for keyword-spotting by character
filler models. A significant improvement in performance is noted by using both
foreground and background information than their individual one. Pyramid
Histogram of Oriented Gradient (PHOG) feature has been used in our word
spotting framework. From the experiment, it has been noted that the proposed
zone-segmentation based system outperforms traditional approaches of word
spotting.
AI Oriented Large-Scale Video Management for Smart City: Technologies, Standards and Beyond
Authors: Ling-Yu Duan, Yihang Lou, Shiqi Wang, Wen Gao, Yong Rui
Date: 2017-12-05 00:00:00
Tasks: Deep Learning, Management

Deep learning has achieved substantial success in a series of tasks in
computer vision. Intelligent video analysis, which can be broadly applied to
video surveillance in various smart city applications, can also be driven by
such powerful deep learning engines. To practically facilitate deep neural
network models in the large-scale video analysis, there are still unprecedented
challenges for the large-scale video data management. Deep feature coding,
instead of video coding, provides a practical solution for handling the
large-scale video surveillance data. To enable interoperability in the context
of deep feature coding, standardization is urgent and important. However, due
to the explosion of deep learning algorithms and the particularity of feature
coding, there are numerous remaining problems in the standardization process.
This paper envisions the future deep feature coding standard for the AI
oriented large-scale video management, and discusses existing techniques,
standards and possible solutions for these open problems.
Human activity recognition from mobile inertial sensors using recurrence plots
Authors: Otávio A. B. Penatti, Milton F. S. Santos
Date: 2017-12-05 00:00:00
Tasks: Activity Recognition, General Classification, Human Activity Recognition, Texture Classification

Inertial sensors are present in most mobile devices nowadays and such devices
are used by people during most of their daily activities. In this paper, we
present an approach for human activity recognition based on inertial sensors by
employing recurrence plots (RP) and visual descriptors. The pipeline of the
proposed approach is the following: compute RPs from sensor data, compute
visual features from RPs and use them in a machine learning protocol. As RPs
generate texture visual patterns, we transform the problem of sensor data
classification to a problem of texture classification. Experiments for
classifying human activities based on accelerometer data showed that the
proposed approach obtains the highest accuracies, outperforming time- and
frequency-domain features directly extracted from sensor data. The best results
are obtained when using RGB RPs, in which each RGB channel corresponds to the
RP of an independent accelerometer axis.
Vehicle Traffic Driven Camera Placement for Better Metropolis Security Surveillance
Authors: Yihui He, Xiaobo Ma, Xiapu Luo, Jianfeng Li, Mengchen Zhao, Bo An, Xiaohong Guan
Date: 2017-04-01 00:00:00
Tasks: Decision Making

Security surveillance is one of the most important issues in smart cities,
especially in an era of terrorism. Deploying a number of (video) cameras is a
common surveillance approach. Given the never-ending power offered by vehicles
to metropolises, exploiting vehicle traffic to design camera placement
strategies could potentially facilitate security surveillance. This article
constitutes the first effort toward building the linkage between vehicle
traffic and security surveillance, which is a critical problem for smart
cities. We expect our study could influence the decision making of surveillance
camera placement, and foster more research of principled ways of security
surveillance beneficial to our physical-world life. Code has been made publicly
available.
Dependent relevance determination for smooth and structured sparse regression
Authors: Anqi Wu, Oluwasanmi Koyejo, Jonathan W. Pillow
Date: 2017-11-28 00:00:00
Tasks: regression

In many problem settings, parameter vectors are not merely sparse but
dependent in such a way that non-zero coefficients tend to cluster together. We
refer to this form of dependency as "region sparsity." Classical sparse
regression methods, such as the lasso and automatic relevance determination
(ARD), which model parameters as independent a priori, and therefore do not
exploit such dependencies. Here we introduce a hierarchical model for smooth,
region-sparse weight vectors and tensors in a linear regression setting. Our
approach represents a hierarchical extension of the relevance determination
framework, where we add a transformed Gaussian process to model the
dependencies between the prior variances of regression weights. We combine this
with a structured model of the prior variances of Fourier coefficients, which
eliminates unnecessary high frequencies. The resulting prior encourages weights
to be region-sparse in two different bases simultaneously. We develop Laplace
approximation and Monte Carlo Markov Chain (MCMC) sampling to provide efficient
inference for the posterior. Furthermore, a two-stage convex relaxation of the
Laplace approximation approach is also provided to relax the inevitable
non-convexity during the optimization. We finally show substantial improvements
over comparable methods for both simulated and real datasets from brain
imaging.
Nearly second-order asymptotic optimality of sequential change-point detection with one-sample updates
Authors: Yang Cao, Liyan Xie, Yao Xie, Huan Xu
Date: 2017-05-19 00:00:00
Tasks: Change Point Detection

Sequential change-point detection when the distribution parameters are
unknown is a fundamental problem in statistics and machine learning. When the
post-change parameters are unknown, we consider a set of detection procedures
based on sequential likelihood ratios with non-anticipating estimators
constructed using online convex optimization algorithms such as online mirror
descent, which provides a more versatile approach to tackle complex situations
where recursive maximum likelihood estimators cannot be found. When the
underlying distributions belong to a exponential family and the estimators
satisfy the logarithm regret property, we show that this approach is nearly
second-order asymptotically optimal. This means that the upper bound for the
false alarm rate of the algorithm (measured by the average-run-length) meets
the lower bound asymptotically up to a log-log factor when the threshold tends
to infinity. Our proof is achieved by making a connection between sequential
change-point and online convex optimization and leveraging the logarithmic
regret bound property of online mirror descent algorithm. Numerical and real
data examples validate our theory.
#anorexia, #anarexia, #anarexyia: Characterizing Online Community Practices with Orthographic Variation
Authors: Ian Stewart, Stevie Chancellor, Munmun De Choudhury, Jacob Eisenstein
Date: 2017-12-04 00:00:00

Distinctive linguistic practices help communities build solidarity and
differentiate themselves from outsiders. In an online community, one such
practice is variation in orthography, which includes spelling, punctuation, and
capitalization. Using a dataset of over two million Instagram posts, we
investigate orthographic variation in a community that shares pro-eating
disorder (pro-ED) content. We find that not only does orthographic variation
grow more frequent over time, it also becomes more profound or deep, with
variants becoming increasingly distant from the original: as, for example,
#anarexyia is more distant than #anarexia from the original spelling #anorexia.
These changes are driven by newcomers, who adopt the most extreme linguistic
practices as they enter the community. Moreover, this behavior correlates with
engagement: the newcomers who adopt deeper orthographic variants tend to remain
active for longer in the community, and the posts that contain deeper variation
receive more positive feedback in the form of "likes." Previous work has linked
community membership change with language change, and our work casts this
connection in a new light, with newcomers driving an evolving practice, rather
than adapting to it. We also demonstrate the utility of orthographic variation
as a new lens to study sociolinguistic change in online communities,
particularly when the change results from an exogenous force such as a content
ban.
Improved Neural Text Attribute Transfer with Non-parallel Data
Authors: Igor Melnyk, Cicero Nogueira dos santos, Kahini Wadhawan, Inkit Padhi, Abhishek Kumar
Date: 2017-11-26 00:00:00
Tasks: Attribute, Decoder, Disentanglement, Text Attribute Transfer

Text attribute transfer using non-parallel data requires methods that can
perform disentanglement of content and linguistic attributes. In this work, we
propose multiple improvements over the existing approaches that enable the
encoder-decoder framework to cope with the text attribute transfer from
non-parallel data. We perform experiments on the sentiment transfer task using
two datasets. For both datasets, our proposed method outperforms a strong
baseline in two of the three employed evaluation metrics.
Beyond Grand Theft Auto V for Training, Testing and Enhancing Deep Learning in Self Driving Cars
Authors: Mark Martinez, Chawin Sitawarin, Kevin Finch, Lennart Meincke, Alex Yablonski, Alain Kornhauser
Date: 2017-12-04 00:00:00
Tasks: Autonomous Driving, Self-Driving Cars, Unity

As an initial assessment, over 480,000 labeled virtual images of normal
highway driving were readily generated in Grand Theft Auto V's virtual
environment. Using these images, a CNN was trained to detect following distance
to cars/objects ahead, lane markings, and driving angle (angular heading
relative to lane centerline): all variables necessary for basic autonomous
driving. Encouraging results were obtained when tested on over 50,000 labeled
virtual images from substantially different GTA-V driving environments. This
initial assessment begins to define both the range and scope of the labeled
images needed for training as well as the range and scope of labeled images
needed for testing the definition of boundaries and limitations of trained
networks. It is the efficacy and flexibility of a "GTA-V"-like virtual
environment that is expected to provide an efficient well-defined foundation
for the training and testing of Convolutional Neural Networks for safe driving.
Additionally, described is the Princeton Virtual Environment (PVE) for the
training, testing and enhancement of safe driving AI, which is being developed
using the video-game engine Unity. PVE is being developed to recreate rare but
critical corner cases that can be used in re-training and enhancing machine
learning models and understanding the limitations of current self driving
models. The Florida Tesla crash is being used as an initial reference.
Regulating Greed Over Time in Multi-Armed Bandits
Authors: Stefano Tracà, Cynthia Rudin, Weiyu Yan
Date: 2015-05-21 00:00:00
Tasks: Multi-Armed Bandits, Time Series Analysis

In retail, there are predictable yet dramatic time-dependent patterns in customer behavior, such as periodic changes in the number of visitors, or increases in customers just before major holidays. The current paradigm of multi-armed bandit analysis does not take these known patterns into account. This means that for applications in retail, where prices are fixed for periods of time, current bandit algorithms will not suffice. This work provides a remedy that takes the time-dependent patterns into account, and we show how this remedy is implemented for the UCB, $\varepsilon$-greedy, and UCB-L algorithms, and also through a new policy called the variable arm pool algorithm. In the corrected methods, exploitation (greed) is regulated over time, so that more exploitation occurs during higher reward periods, and more exploration occurs in periods of low reward. In order to understand why regret is reduced with the corrected methods, we present a set of bounds that provide insight into why we would want to exploit during periods of high reward, and discuss the impact on regret. Our proposed methods perform well in experiments, and were inspired by a high-scoring entry in the Exploration and Exploitation 3 contest using data from Yahoo$!$ Front Page. That entry heavily used time-series methods to regulate greed over time, which was substantially more effective than other contextual bandit methods.