pwc-archive/papers-with-abstracts huggingface dataset:
Construction Safety Risk Modeling and Simulation
Authors: Antoine J. -P. Tixier, Matthew R. Hallowell, Balaji Rajagopalan
Date: 2016-09-26 00:00:00
Tasks: Attribute, Decision Making, Decision Making Under Uncertainty, Management

By building on a recently introduced genetic-inspired attribute-based
conceptual framework for safety risk analysis, we propose a novel methodology
to compute construction univariate and bivariate construction safety risk at a
situational level. Our fully data-driven approach provides construction
practitioners and academicians with an easy and automated way of extracting
valuable empirical insights from databases of unstructured textual injury
reports. By applying our methodology on an attribute and outcome dataset
directly obtained from 814 injury reports, we show that the frequency-magnitude
distribution of construction safety risk is very similar to that of natural
phenomena such as precipitation or earthquakes. Motivated by this observation,
and drawing on state-of-the-art techniques in hydroclimatology and insurance,
we introduce univariate and bivariate nonparametric stochastic safety risk
generators, based on Kernel Density Estimators and Copulas. These generators
enable the user to produce large numbers of synthetic safety risk values
faithfully to the original data, allowing safetyrelated decision-making under
uncertainty to be grounded on extensive empirical evidence. Just like the
accurate modeling and simulation of natural phenomena such as wind or
streamflow is indispensable to successful structure dimensioning or water
reservoir management, we posit that improving construction safety calls for the
accurate modeling, simulation, and assessment of safety risk. The underlying
assumption is that like natural phenomena, construction safety may benefit from
being studied in an empirical and quantitative way rather than qualitatively
which is the current industry standard. Finally, a side but interesting finding
is that attributes related to high energy levels and to human error emerge as
strong risk shapers on the dataset we used to illustrate our methodology.
Adaptive and Scalable Android Malware Detection through Online Learning
Authors: Annamalai Narayanan, Liu Yang, Lihui Chen, Liu Jinliang
Date: 2016-06-23 00:00:00
Tasks: Android Malware Detection, BIG-bench Machine Learning, Malware Detection

It is well-known that malware constantly evolves so as to evade detection and
this causes the entire malware population to be non-stationary. Contrary to
this fact, prior works on machine learning based Android malware detection have
assumed that the distribution of the observed malware characteristics (i.e.,
features) do not change over time. In this work, we address the problem of
malware population drift and propose a novel online machine learning based
framework, named DroidOL to handle it and effectively detect malware. In order
to perform accurate detection, security-sensitive behaviors are captured from
apps in the form of inter-procedural control-flow sub-graph features using a
state-of-the-art graph kernel. In order to perform scalable detection and to
adapt to the drift and evolution in malware population, an online
passive-aggressive classifier is used.
  In a large-scale comparative analysis with more than 87,000 apps, DroidOL
achieves 84.29% accuracy outperforming two state-of-the-art malware techniques
by more than 20% in their typical batch learning setting and more than 3% when
they are continuously re-trained. Our experimental findings strongly indicate
that online learning based approaches are highly suitable for real-world
malware detection.
Linear Support Tensor Machine: Pedestrian Detection in Thermal Infrared Images
Authors: Sujoy Kumar Biswas, Peyman Milanfar
Date: 2016-09-26 00:00:00
Tasks: Attribute, Pedestrian Detection

Pedestrian detection in thermal infrared images poses unique challenges
because of the low resolution and noisy nature of the image. Here we propose a
mid-level attribute in the form of multidimensional template, or tensor, using
Local Steering Kernel (LSK) as low-level descriptors for detecting pedestrians
in far infrared images. LSK is specifically designed to deal with intrinsic
image noise and pixel level uncertainty by capturing local image geometry
succinctly instead of collecting local orientation statistics (e.g., histograms
in HOG). Our second contribution is the introduction of a new image similarity
kernel in the popular maximum margin framework of support vector machines that
results in a relatively short and simple training phase for building a rigid
pedestrian detector. Our third contribution is to replace the sluggish but de
facto sliding window based detection methodology with multichannel discrete
Fourier transform, facilitating very fast and efficient pedestrian
localization. The experimental studies on publicly available thermal infrared
images justify our proposals and model assumptions. In addition, the proposed
work also involves the release of our in-house annotations of pedestrians in
more than 17000 frames of OSU Color Thermal database for the purpose of sharing
with the research community.
Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation
Authors: Taehwan Kim, Jonathan Keane, Weiran Wang, Hao Tang, Jason Riggle, Gregory Shakhnarovich, Diane Brentari, Karen Livescu
Date: 2016-09-26 00:00:00

We study the problem of recognizing video sequences of fingerspelled letters
in American Sign Language (ASL). Fingerspelling comprises a significant but
relatively understudied part of ASL. Recognizing fingerspelling is challenging
for a number of reasons: It involves quick, small motions that are often highly
coarticulated; it exhibits significant variation between signers; and there has
been a dearth of continuous fingerspelling data collected. In this work we
collect and annotate a new data set of continuous fingerspelling videos,
compare several types of recognizers, and explore the problem of signer
variation. Our best-performing models are segmental (semi-Markov) conditional
random fields using deep neural network-based features. In the signer-dependent
setting, our recognizers achieve up to about 92% letter accuracy. The
multi-signer setting is much more challenging, but with neural network
adaptation we achieve up to 83% letter accuracies in this setting.
Interactive Image Segmentation From A Feedback Control Perspective
Authors: Liangjia Zhu, Peter Karasev, Ivan Kolesov, Romeil Sandhu, Allen Tannenbaum
Date: 2016-06-26 00:00:00
Tasks: Image Segmentation, Interactive Segmentation, Segmentation, Semantic Segmentation

Image segmentation is a fundamental problem in computational vision and
medical imaging. Designing a generic, automated method that works for various
objects and imaging modalities is a formidable task. Instead of proposing a new
specific segmentation algorithm, we present a general design principle on how
to integrate user interactions from the perspective of feedback control theory.
Impulsive control and Lyapunov stability analysis are employed to design and
analyze an interactive segmentation system. Then stabilization conditions are
derived to guide algorithm design. Finally, the effectiveness and robustness of
proposed method are demonstrated.
Resolving Language and Vision Ambiguities Together: Joint Segmentation & Prepositional Attachment Resolution in Captioned Scenes
Authors: Gordon Christie, Ankit Laddha, Aishwarya Agrawal, Stanislaw Antol, Yash Goyal, Kevin Kochersberger, Dhruv Batra
Date: 2016-04-07 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Common Sense Reasoning, Prepositional Phrase Attachment, Segmentation, Semantic Segmentation, Sentence

We present an approach to simultaneously perform semantic segmentation and
prepositional phrase attachment resolution for captioned images. Some
ambiguities in language cannot be resolved without simultaneously reasoning
about an associated image. If we consider the sentence "I shot an elephant in
my pajamas", looking at language alone (and not using common sense), it is
unclear if it is the person or the elephant wearing the pajamas or both. Our
approach produces a diverse set of plausible hypotheses for both semantic
segmentation and prepositional phrase attachment resolution that are then
jointly reranked to select the most consistent pair. We show that our semantic
segmentation and prepositional phrase attachment resolution modules have
complementary strengths, and that joint reasoning produces more accurate
results than any module operating in isolation. Multiple hypotheses are also
shown to be crucial to improved multiple-module reasoning. Our vision and
language approach significantly outperforms the Stanford Parser (De Marneffe et
al., 2006) by 17.91% (28.69% relative) and 12.83% (25.28% relative) in two
different experiments. We also make small improvements over DeepLab-CRF (Chen
et al., 2015).
Pointer Sentinel Mixture Models
Authors: Stephen Merity, Caiming Xiong, James Bradbury, Richard Socher
Date: 2016-09-26 00:00:00
Tasks: Language Modeling, Language Modelling

Recent neural network sequence models with softmax classifiers have achieved
their best language modeling performance only with very large hidden states and
large vocabularies. Even then they struggle to predict rare or unseen words
even if the context makes the prediction unambiguous. We introduce the pointer
sentinel mixture architecture for neural sequence models which has the ability
to either reproduce a word from the recent context or produce a word from a
standard softmax classifier. Our pointer sentinel-LSTM model achieves state of
the art language modeling performance on the Penn Treebank (70.9 perplexity)
while using far fewer parameters than a standard softmax LSTM. In order to
evaluate how well language models can exploit longer contexts and deal with
more realistic vocabularies and larger corpora we also introduce the freely
available WikiText corpus.
The emotional arcs of stories are dominated by six basic shapes
Authors: Andrew J. Reagan, Lewis Mitchell, Dilan Kiley, Christopher M. Danforth, Peter Sheridan Dodds
Date: 2016-06-24 00:00:00

Advances in computing power, natural language processing, and digitization of
text now make it possible to study a culture's evolution through its texts
using a "big data" lens. Our ability to communicate relies in part upon a
shared emotional experience, with stories often following distinct emotional
trajectories and forming patterns that are meaningful to us. Here, by
classifying the emotional arcs for a filtered subset of 1,327 stories from
Project Gutenberg's fiction collection, we find a set of six core emotional
arcs which form the essential building blocks of complex emotional
trajectories. We strengthen our findings by separately applying Matrix
decomposition, supervised learning, and unsupervised learning. For each of
these six core emotional arcs, we examine the closest characteristic stories in
publication today and find that particular emotional arcs enjoy greater
success, as measured by downloads.
Generalizing and Hybridizing Count-based and Neural Language Models
Authors: Graham Neubig, Chris Dyer
Date: 2016-06-01 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Language Modeling, Language Modelling

Language models (LMs) are statistical models that calculate probabilities
over sequences of words or other discrete symbols. Currently two major
paradigms for language modeling exist: count-based n-gram models, which have
advantages of scalability and test-time speed, and neural LMs, which often
achieve superior modeling performance. We demonstrate how both varieties of
models can be unified in a single modeling framework that defines a set of
probability distributions over the vocabulary of words, and then dynamically
calculates mixture weights over these distributions. This formulation allows us
to create novel hybrid models that combine the desirable features of
count-based and neural LMs, and experiments demonstrate the advantages of these
approaches.
Multiview RGB-D Dataset for Object Instance Detection
Authors: Georgios Georgakis, Md. Alimoor Reza, Arsalan Mousavian, Phi-Hung Le, Jana Kosecka
Date: 2016-09-26 00:00:00
Tasks: Object, object-detection, Object Detection

This paper presents a new multi-view RGB-D dataset of nine kitchen scenes,
each containing several objects in realistic cluttered environments including a
subset of objects from the BigBird dataset. The viewpoints of the scenes are
densely sampled and objects in the scenes are annotated with bounding boxes and
in the 3D point cloud. Also, an approach for detection and recognition is
presented, which is comprised of two parts: i) a new multi-view 3D proposal
generation method and ii) the development of several recognition baselines
using AlexNet to score our proposals, which is trained either on crops of the
dataset or on synthetically composited training images. Finally, we compare the
performance of the object proposals and a detection baseline to the Washington
RGB-D Scenes (WRGB-D) dataset and demonstrate that our Kitchen scenes dataset
is more challenging for object detection and recognition. The dataset is
available at: http://cs.gmu.edu/~robot/gmu-kitchens.html.
Mixtures of Bivariate von Mises Distributions with Applications to Modelling of Protein Dihedral Angles
Authors: Parthan Kasarapu
Date: 2016-07-05 00:00:00

The modelling of empirically observed data is commonly done using mixtures of
probability distributions. In order to model angular data, directional
probability distributions such as the bivariate von Mises (BVM) is typically
used. The critical task involved in mixture modelling is to determine the
optimal number of component probability distributions. We employ the Bayesian
information-theoretic principle of minimum message length (MML) to distinguish
mixture models by balancing the trade-off between the model's complexity and
its goodness-of-fit to the data. We consider the problem of modelling angular
data resulting from the spatial arrangement of protein structures using BVM
distributions. The main contributions of the paper include the development of
the mixture modelling apparatus along with the MML estimation of the parameters
of the BVM distribution. We demonstrate that statistical inference using the
MML framework supersedes the traditional methods and offers a mechanism to
objectively determine models that are of practical significance.
A Decomposable Attention Model for Natural Language Inference
Authors: Ankur P. Parikh, Oscar Täckström, Dipanjan Das, Jakob Uszkoreit
Date: 2016-06-06 00:00:00
Proceeding: EMNLP 2016 11
Tasks: model, Natural Language Inference, Sentence

We propose a simple neural architecture for natural language inference. Our
approach uses attention to decompose the problem into subproblems that can be
solved separately, thus making it trivially parallelizable. On the Stanford
Natural Language Inference (SNLI) dataset, we obtain state-of-the-art results
with almost an order of magnitude fewer parameters than previous work and
without relying on any word-order information. Adding intra-sentence attention
that takes a minimum amount of order into account yields further improvements.
Variational Neural Machine Translation
Authors: Biao Zhang, Deyi Xiong, Jinsong Su, Hong Duan, Min Zhang
Date: 2016-05-25 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Decoder, Machine Translation, Sentence, Translation

Models of neural machine translation are often from a discriminative family
of encoderdecoders that learn a conditional distribution of a target sentence
given a source sentence. In this paper, we propose a variational model to learn
this conditional distribution for neural machine translation: a variational
encoderdecoder model that can be trained end-to-end. Different from the vanilla
encoder-decoder model that generates target translations from hidden
representations of source sentences alone, the variational model introduces a
continuous latent variable to explicitly model underlying semantics of source
sentences and to guide the generation of target translations. In order to
perform efficient posterior inference and large-scale training, we build a
neural posterior approximator conditioned on both the source and the target
sides, and equip it with a reparameterization technique to estimate the
variational lower bound. Experiments on both Chinese-English and English-
German translation tasks show that the proposed variational neural machine
translation achieves significant improvements over the vanilla neural machine
translation baselines.
Variational Neural Discourse Relation Recognizer
Authors: Biao Zhang, Deyi Xiong, Jinsong Su, Qun Liu, Rongrong Ji, Hong Duan, Min Zhang
Date: 2016-03-12 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Relation

Implicit discourse relation recognition is a crucial component for automatic
discourselevel analysis and nature language understanding. Previous studies
exploit discriminative models that are built on either powerful manual features
or deep discourse representations. In this paper, instead, we explore
generative models and propose a variational neural discourse relation
recognizer. We refer to this model as VarNDRR. VarNDRR establishes a directed
probabilistic model with a latent continuous variable that generates both a
discourse and the relation between the two arguments of the discourse. In order
to perform efficient inference and learning, we introduce neural discourse
relation models to approximate the prior and posterior distributions of the
latent variable, and employ these approximated distributions to optimize a
reparameterized variational lower bound. This allows VarNDRR to be trained with
standard stochastic gradient methods. Experiments on the benchmark data set
show that VarNDRR can achieve comparable results against stateof- the-art
baselines without using any manual features.
Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text
Authors: John J. Nay
Date: 2016-09-21 00:00:00
Proceeding: WS 2016 11

We compare policy differences across institutions by embedding
representations of the entire legal corpus of each institution and the
vocabulary shared across all corpora into a continuous vector space. We apply
our method, Gov2Vec, to Supreme Court opinions, Presidential actions, and
official summaries of Congressional bills. The model discerns meaningful
differences between government branches. We also learn representations for more
fine-grained word sources: individual Presidents and (2-year) Congresses. The
similarities between learned representations of Congresses over time and
sitting Presidents are negatively correlated with the bill veto rate, and the
temporal ordering of Presidents and Congresses was implicitly learned from only
text. With the resulting vectors we answer questions such as: how does Obama
and the 113th House differ in addressing climate change and how does this vary
from environmental or economic perspectives? Our work illustrates
vector-arithmetic-based investigations of complex relationships between word
sources based on their texts. We are extending this to create a more
comprehensive legal semantic map.
Sampling Method for Fast Training of Support Vector Data Description
Authors: Arin Chaudhuri, Deovrat Kakde, Maria Jahja, Wei Xiao, Hansi Jiang, Seunghyun Kong, Sergiy Peredriy
Date: 2016-06-16 00:00:00
Tasks: Outlier Detection

Support Vector Data Description (SVDD) is a popular outlier detection
technique which constructs a flexible description of the input data. SVDD
computation time is high for large training datasets which limits its use in
big-data process-monitoring applications. We propose a new iterative
sampling-based method for SVDD training. The method incrementally learns the
training data description at each iteration by computing SVDD on an independent
random sample selected with replacement from the training data set. The
experimental results indicate that the proposed method is extremely fast and
provides a good data description .
Commonsense Reasoning, Commonsense Knowledge, and The SP Theory of Intelligence
Authors: J. Gerard Wolff
Date: 2016-09-25 00:00:00

This paper describes how the "SP Theory of Intelligence" with the "SP
Computer Model", outlined in an Appendix, may throw light on aspects of
commonsense reasoning (CSR) and commonsense knowledge (CSK), as discussed in
another paper by Ernest Davis and Gary Marcus (DM). In four main sections, the
paper describes: 1) The main problems to be solved; 2) Other research on CSR
and CSK; 3) Why the SP system may prove useful with CSR and CSK 4) How examples
described by DM may be modelled in the SP system. With regard to successes in
the automation of CSR described by DM, the SP system's strengths in
simplification and integration may promote seamless integration across these
areas, and seamless integration of those area with other aspects of
intelligence. In considering challenges in the automation of CSR described by
DM, the paper describes in detail, with examples of SP-multiple-alignments. how
the SP system may model processes of interpretation and reasoning arising from
the horse's head scene in "The Godfather" film. A solution is presented to the
'long tail' problem described by DM. The SP system has some potentially useful
things to say about several of DM's objectives for research in CSR and CSK.
Random Forest for Malware Classification
Authors: Felan Carlo C. Garcia, Felix P. Muga II
Date: 2016-09-25 00:00:00
Tasks: Classification, General Classification, Malware Classification

The challenge in engaging malware activities involves the correct
identification and classification of different malware variants. Various
malwares incorporate code obfuscation methods that alters their code signatures
effectively countering antimalware detection techniques utilizing static
methods and signature database. In this study, we utilized an approach of
converting a malware binary into an image and use Random Forest to classify
various malware families. The resulting accuracy of 0.9562 exhibits the
effectivess of the method in detecting malware
Deep learning for detection of bird vocalisations
Authors: Ilyas Potamitis
Date: 2016-09-25 00:00:00
Tasks: Deep Learning

This work focuses on reliable detection of bird sound emissions as recorded
in the open field. Acoustic detection of avian sounds can be used for the
automatized monitoring of multiple bird taxa and querying in long-term
recordings for species of interest for researchers, conservation practitioners,
and decision makers. Recordings in the wild can be very noisy due to the
exposure of the microphones to a large number of audio sources originating from
all distances and directions, the number and identity of which cannot be known
a-priori. The co-existence of the target vocalizations with abiotic
interferences in an unconstrained environment is inefficiently treated by
current approaches of audio signal enhancement. A technique that would spot
only bird vocalization while ignoring other audio sources is of prime
importance. These difficulties are tackled in this work, presenting a deep
autoencoder that maps the audio spectrogram of bird vocalizations to its
corresponding binary mask that encircles the spectral blobs of vocalizations
while suppressing other audio sources. The procedure requires minimum human
attendance, it is very fast during execution, thus suitable to scan massive
volumes of data, in order to analyze them, evaluate insights and hypotheses,
identify patterns of bird activity that, hopefully, finally lead to design
policies on biodiversity issues.