pwc-archive/papers-with-abstracts huggingface dataset:
Null Dynamical State Models of Human Cognitive Dysfunction
Authors: M. J. Gagen
Date: 2017-12-25 00:00:00

The hard problem in artificial intelligence asks how the shuffling of
syntactical symbols in a program can lead to systems which experience semantics
and qualia. We address this question in three stages. First, we introduce a new
class of human semantic symbols which appears when unexpected and drastic
environmental change causes humans to become surprised, confused, uncertain,
and in extreme cases, unresponsive, passive and dysfunctional. For this class
of symbols, pre-learned programs become inoperative so these syntactical
programs cannot be the source of experienced qualia. Second, we model the
dysfunctional human response to a radically changed environment as being the
natural response of any learning machine facing novel inputs from well outside
its previous training set. In this situation, learning machines are unable to
extract information from their input and will typically enter a dynamical state
characterized by null outputs and a lack of response. This state immediately
predicts and explains the characteristics of the semantic experiences of humans
in similar circumstances. In the third stage, we consider learning machines
trained to implement multiple functions in simple sequential programs using
environmental data to specify subroutine names, control flow instructions,
memory calls, and so on. Drastic change in any of these environmental inputs
can again lead to inoperative programs. By examining changes specific to people
or locations we can model human cognitive symbols featuring these dependencies,
such as attachment and grief. Our approach links known dynamical machines
states with human qualia and thus offers new insight into the hard problem of
artificial intelligence.
Efficient Algorithms for t-distributed Stochastic Neighborhood Embedding
Authors: George C. Linderman, Manas Rachh, Jeremy G. Hoskins, Stefan Steinerberger, Yuval Kluger
Date: 2017-12-25 00:00:00
Tasks: Dimensionality Reduction

t-distributed Stochastic Neighborhood Embedding (t-SNE) is a method for
dimensionality reduction and visualization that has become widely popular in
recent years. Efficient implementations of t-SNE are available, but they scale
poorly to datasets with hundreds of thousands to millions of high dimensional
data-points. We present Fast Fourier Transform-accelerated Interpolation-based
t-SNE (FIt-SNE), which dramatically accelerates the computation of t-SNE. The
most time-consuming step of t-SNE is a convolution that we accelerate by
interpolating onto an equispaced grid and subsequently using the fast Fourier
transform to perform the convolution. We also optimize the computation of input
similarities in high dimensions using multi-threaded approximate nearest
neighbors. We further present a modification to t-SNE called "late
exaggeration," which allows for easier identification of clusters in t-SNE
embeddings. Finally, for datasets that cannot be loaded into the memory, we
present out-of-core randomized principal component analysis (oocPCA), so that
the top principal components of a dataset can be computed without ever fully
loading the matrix, hence allowing for t-SNE of large datasets to be computed
on resource-limited machines.
Kernel Regression with Sparse Metric Learning
Authors: Rongqing Huang, Shiliang Sun
Date: 2017-12-25 00:00:00
Tasks: Dimensionality Reduction, Metric Learning, regression

Kernel regression is a popular non-parametric fitting technique. It aims at
learning a function which estimates the targets for test inputs as precise as
possible. Generally, the function value for a test input is estimated by a
weighted average of the surrounding training examples. The weights are
typically computed by a distance-based kernel function and they strongly depend
on the distances between examples. In this paper, we first review the latest
developments of sparse metric learning and kernel regression. Then a novel
kernel regression method involving sparse metric learning, which is called
kernel regression with sparse metric learning (KR$\_$SML), is proposed. The
sparse kernel regression model is established by enforcing a mixed $(2,1)$-norm
regularization over the metric matrix. It learns a Mahalanobis distance metric
by a gradient descent procedure, which can simultaneously conduct
dimensionality reduction and lead to good prediction results. Our work is the
first to combine kernel regression with sparse metric learning. To verify the
effectiveness of the proposed method, it is evaluated on 19 data sets for
regression. Furthermore, the new method is also applied to solving practical
problems of forecasting short-term traffic flows. In the end, we compare the
proposed method with other three related kernel regression methods on all test
data sets under two criterions. Experimental results show that the proposed
method is much more competitive.
Android Malware Detection using Deep Learning on API Method Sequences
Authors: ElMouatez Billah Karbab, Mourad Debbabi, Abdelouahid Derhab, Djedjiga Mouheb
Date: 2017-12-25 00:00:00
Tasks: Android Malware Detection, Attribute, Deep Learning, Malware Detection

Android OS experiences a blazing popularity since the last few years. This
predominant platform has established itself not only in the mobile world but
also in the Internet of Things (IoT) devices. This popularity, however, comes
at the expense of security, as it has become a tempting target of malicious
apps. Hence, there is an increasing need for sophisticated, automatic, and
portable malware detection solutions. In this paper, we propose MalDozer, an
automatic Android malware detection and family attribution framework that
relies on sequences classification using deep learning techniques. Starting
from the raw sequence of the app's API method calls, MalDozer automatically
extracts and learns the malicious and the benign patterns from the actual
samples to detect Android malware. MalDozer can serve as a ubiquitous malware
detection system that is not only deployed on servers, but also on mobile and
even IoT devices. We evaluate MalDozer on multiple Android malware datasets
ranging from 1K to 33K malware apps, and 38K benign apps. The results show that
MalDozer can correctly detect malware and attribute them to their actual
families with an F1-Score of 96%-99% and a false positive rate of 0.06%-2%,
under all tested datasets and settings.
Network-Scale Traffic Modeling and Forecasting with Graphical Lasso and Neural Networks
Authors: Shiliang Sun, Rongqing Huang, Ya Gao
Date: 2017-12-25 00:00:00
Tasks: GPR, Multi-Task Learning, regression

Traffic flow forecasting, especially the short-term case, is an important
topic in intelligent transportation systems (ITS). This paper does a lot of
research on network-scale modeling and forecasting of short-term traffic flows.
Firstly, we propose the concepts of single-link and multi-link models of
traffic flow forecasting. Secondly, we construct four prediction models by
combining the two models with single-task learning and multi-task learning. The
combination of the multi-link model and multi-task learning not only improves
the experimental efficiency but also the prediction accuracy. Moreover, a new
multi-link single-task approach that combines graphical lasso (GL) with neural
network (NN) is proposed. GL provides a general methodology for solving
problems involving lots of variables. Using L1 regularization, GL builds a
sparse graphical model making use of the sparse inverse covariance matrix. In
addition, Gaussian process regression (GPR) is a classic regression algorithm
in Bayesian machine learning. Although there is wide research on GPR, there are
few applications of GPR in traffic flow forecasting. In this paper, we apply
GPR to traffic flow forecasting and show its potential. Through sufficient
experiments, we compare all of the proposed approaches and make an overall
assessment at last.
Leveraging Native Language Speech for Accent Identification using Deep Siamese Networks
Authors: Aditya Siddhant, Preethi Jyothi, Sriram Ganapathy
Date: 2017-12-25 00:00:00
Tasks: Speaker Profiling, speech-recognition, Speech Recognition

The problem of automatic accent identification is important for several
applications like speaker profiling and recognition as well as for improving
speech recognition systems. The accented nature of speech can be primarily
attributed to the influence of the speaker's native language on the given
speech recording. In this paper, we propose a novel accent identification
system whose training exploits speech in native languages along with the
accented speech. Specifically, we develop a deep Siamese network-based model
which learns the association between accented speech recordings and the native
language speech recordings. The Siamese networks are trained with i-vector
features extracted from the speech recordings using either an unsupervised
Gaussian mixture model (GMM) or a supervised deep neural network (DNN) model.
We perform several accent identification experiments using the CSLU Foreign
Accented English (FAE) corpus. In these experiments, our proposed approach
using deep Siamese networks yield significant relative performance improvements
of 15.4 percent on a 10-class accent identification task, over a baseline
DNN-based classification system that uses GMM i-vectors. Furthermore, we
present a detailed error analysis of the proposed accent identification system.
Learning to Run with Actor-Critic Ensemble
Authors: Zhewei Huang, Shuchang Zhou, BoEr Zhuang, Xinyu Zhou
Date: 2017-12-25 00:00:00

We introduce an Actor-Critic Ensemble(ACE) method for improving the
performance of Deep Deterministic Policy Gradient(DDPG) algorithm. At inference
time, our method uses a critic ensemble to select the best action from
proposals of multiple actors running in parallel. By having a larger candidate
set, our method can avoid actions that have fatal consequences, while staying
deterministic. Using ACE, we have won the 2nd place in NIPS'17 Learning to Run
competition, under the name of "Megvii-hzwer".
Learning the Kernel for Classification and Regression
Authors: Chen Li, Luca Venturi, Ruitu Xu
Date: 2017-12-22 00:00:00
Tasks: Classification, General Classification, regression

We investigate a series of learning kernel problems with polynomial
combinations of base kernels, which will help us solve regression and
classification problems. We also perform some numerical experiments of
polynomial kernels with regression and classification tasks on different
datasets.
On Statistical Optimality of Variational Bayes
Authors: Debdeep Pati, Anirban Bhattacharya, Yun Yang
Date: 2017-12-25 00:00:00
Tasks: Bayesian Inference, parameter estimation

The article addresses a long-standing open problem on the justification of
using variational Bayes methods for parameter estimation. We provide general
conditions for obtaining optimal risk bounds for point estimates acquired from
mean-field variational Bayesian inference. The conditions pertain to the
existence of certain test functions for the distance metric on the parameter
space and minimal assumptions on the prior. A general recipe for verification
of the conditions is outlined which is broadly applicable to existing Bayesian
models with or without latent variables. As illustrations, specific
applications to Latent Dirichlet Allocation and Gaussian mixture models are
discussed.
Label-driven weakly-supervised learning for multimodal deformable image registration
Authors: Yipeng Hu, Marc Modat, Eli Gibson, Nooshin Ghavami, Ester Bonmati, Caroline M. Moore, Mark Emberton, J. Alison Noble, Dean C. Barratt, Tom Vercauteren
Date: 2017-11-05 00:00:00
Tasks: Image Registration, Weakly-supervised Learning

Spatially aligning medical images from different modalities remains a
challenging task, especially for intraoperative applications that require fast
and robust algorithms. We propose a weakly-supervised, label-driven formulation
for learning 3D voxel correspondence from higher-level label correspondence,
thereby bypassing classical intensity-based image similarity measures. During
training, a convolutional neural network is optimised by outputting a dense
displacement field (DDF) that warps a set of available anatomical labels from
the moving image to match their corresponding counterparts in the fixed image.
These label pairs, including solid organs, ducts, vessels, point landmarks and
other ad hoc structures, are only required at training time and can be
spatially aligned by minimising a cross-entropy function of the warped moving
label and the fixed label. During inference, the trained network takes a new
image pair to predict an optimal DDF, resulting in a fully-automatic,
label-free, real-time and deformable registration. For interventional
applications where large global transformation prevails, we also propose a
neural network architecture to jointly optimise the global- and local
displacements. Experiment results are presented based on cross-validating
registrations of 111 pairs of T2-weighted magnetic resonance images and 3D
transrectal ultrasound images from prostate cancer patients with a total of
over 4000 anatomical labels, yielding a median target registration error of 4.2
mm on landmark centroids and a median Dice of 0.88 on prostate glands.
Mean Field Residual Networks: On the Edge of Chaos
Authors: Greg Yang, Samuel S. Schoenholz
Date: 2017-12-24 00:00:00
Proceeding: NeurIPS 2017

We study randomly initialized residual networks using mean field theory and
the theory of difference equations. Classical feedforward neural networks, such
as those with tanh activations, exhibit exponential behavior on the average
when propagating inputs forward or gradients backward. The exponential forward
dynamics causes rapid collapsing of the input space geometry, while the
exponential backward dynamics causes drastic vanishing or exploding gradients.
We show, in contrast, that by adding skip connections, the network will,
depending on the nonlinearity, adopt subexponential forward and backward
dynamics, and in many cases in fact polynomial. The exponents of these
polynomials are obtained through analytic methods and proved and verified
empirically to be correct. In terms of the "edge of chaos" hypothesis, these
subexponential and polynomial laws allow residual networks to "hover over the
boundary between stability and chaos," thus preserving the geometry of the
input space and the gradient information flow. In our experiments, for each
activation function we study here, we initialize residual networks with
different hyperparameters and train them on MNIST. Remarkably, our
initialization time theory can accurately predict test time performance of
these networks, by tracking either the expected amount of gradient explosion or
the expected squared distance between the images of two input vectors.
Importantly, we show, theoretically as well as empirically, that common
initializations such as the Xavier or the He schemes are not optimal for
residual networks, because the optimal initialization variances depend on the
depth. Finally, we have made mathematical contributions by deriving several new
identities for the kernels of powers of ReLU functions by relating them to the
zeroth Bessel function of the second kind.
Learning Interpretable Spatial Operations in a Rich 3D Blocks World
Authors: Yonatan Bisk, Kevin J. Shih, Yejin Choi, Daniel Marcu
Date: 2017-12-10 00:00:00

In this paper, we study the problem of mapping natural language instructions
to complex spatial actions in a 3D blocks world. We first introduce a new
dataset that pairs complex 3D spatial operations to rich natural language
descriptions that require complex spatial and pragmatic interpretations such as
"mirroring", "twisting", and "balancing". This dataset, built on the simulation
environment of Bisk, Yuret, and Marcu (2016), attains language that is
significantly richer and more complex, while also doubling the size of the
original dataset in the 2D environment with 100 new world configurations and
250,000 tokens. In addition, we propose a new neural architecture that achieves
competitive results while automatically discovering an inventory of
interpretable spatial operations (Figure 5)
Estimating the Probability of Meeting a Deadline in Hierarchical Plans
Authors: Liat Cohen, Solomon Eyal Shimony, Gera Weiss
Date: 2015-03-04 00:00:00

Given a hierarchical plan (or schedule) with uncertain task times, we propose
a deterministic polynomial (time and memory) algorithm for estimating the
probability that its meets a deadline, or, alternately, that its {\em makespan}
is less than a given duration. Approximation is needed as it is known that this
problem is NP-hard even for sequential plans (just, a sum of random variables).
In addition, we show two new complexity results: (1) Counting the number of
events that do not cross deadline is \#P-hard; (2)~Computing the expected
makespan of a hierarchical plan is NP-hard. For the proposed approximation
algorithm, we establish formal approximation bounds and show that the time and
memory complexities grow polynomially with the required accuracy, the number of
nodes in the plan, and with the size of the support of the random variables
that represent the durations of the primitive tasks. We examine these
approximation bounds empirically and demonstrate, using task networks taken
from the literature, how our scheme outperforms sampling techniques and exact
computation in terms of accuracy and run-time. As the empirical data shows much
better error bounds than guaranteed, we also suggest a method for tightening
the bounds in some cases.
Comparative Opinion Mining: A Review
Authors: Kasturi Dewi Varathan, Anastasia Giachanou, Fabio Crestani
Date: 2017-12-24 00:00:00
Tasks: Opinion Mining, Sentiment Analysis

Opinion mining refers to the use of natural language processing, text
analysis and computational linguistics to identify and extract subjective
information in textual material. Opinion mining, also known as sentiment
analysis, has received a lot of attention in recent times, as it provides a
number of tools to analyse the public opinion on a number of different topics.
Comparative opinion mining is a subfield of opinion mining that deals with
identifying and extracting information that is expressed in a comparative form
(e.g.~"paper X is better than the Y"). Comparative opinion mining plays a very
important role when ones tries to evaluate something, as it provides a
reference point for the comparison. This paper provides a review of the area of
comparative opinion mining. It is the first review that cover specifically this
topic as all previous reviews dealt mostly with general opinion mining. This
survey covers comparative opinion mining from two different angles. One from
perspective of techniques and the other from perspective of comparative opinion
elements. It also incorporates preprocessing tools as well as dataset that were
used by the past researchers that can be useful to the future researchers in
the field of comparative opinion mining.
Semi-automatic definite description annotation: a first report
Authors: Danillo da Silva Rocha, Alex Gwo Jen Lan, Ivandre Paraboni
Date: 2017-12-24 00:00:00
Tasks: Referring Expression, Referring expression generation

Studies in Referring Expression Generation (REG) often make use of corpora of
definite descriptions produced by human subjects in controlled experiments.
Experiments of this kind, which are essential for the study of reference
phenomena and many others, may however include a considerable amount of noise.
Human subjects may easily lack attention, or may simply misunderstand the task
at hand and, as a result, the elicited data may include large proportions of
ambiguous or ill-formed descriptions. In addition to that, REG corpora are
usually collected for the study of semantics-related phenomena, and it is often
the case that the elicited descriptions (and their input contexts) need to be
annotated with their corresponding semantic properties. This, as in many other
fields, may require considerable time and skilled annotators. As a means to
tackle both kinds of difficulties - poor data quality and high annotation costs
- this work discusses a semi-automatic method for the annotation of definite
descriptions produced by human subjects in REG data collection experiments. The
method makes use of simple rules to establish associations between words and
meanings, and is intended to facilitate the design of experiments that produce
REG corpora.
Inference of Personal Attributes from Tweets Using Machine Learning
Authors: Take Yo, Kazutoshi Sasahara
Date: 2017-09-28 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

Using machine learning algorithms, including deep learning, we studied the
prediction of personal attributes from the text of tweets, such as gender,
occupation, and age groups. We applied word2vec to construct word vectors,
which were then used to vectorize tweet blocks. The resulting tweet vectors
were used as inputs for training models, and the prediction accuracy of those
models was examined as a function of the dimension of the tweet vectors and the
size of the tweet blacks. The results showed that the machine learning
algorithms could predict the three personal attributes of interest with 60-70%
accuracy.
Building a Sentiment Corpus of Tweets in Brazilian Portuguese
Authors: Henrico Bertini Brum, Maria das Graças Volpe Nunes
Date: 2017-12-24 00:00:00
Tasks: Binary Classification, Classification, General Classification, Sentiment Analysis

The large amount of data available in social media, forums and websites
motivates researches in several areas of Natural Language Processing, such as
sentiment analysis. The popularity of the area due to its subjective and
semantic characteristics motivates research on novel methods and approaches for
classification. Hence, there is a high demand for datasets on different domains
and different languages. This paper introduces TweetSentBR, a sentiment corpora
for Brazilian Portuguese manually annotated with 15.000 sentences on TV show
domain. The sentences were labeled in three classes (positive, neutral and
negative) by seven annotators, following literature guidelines for ensuring
reliability on the annotation. We also ran baseline experiments on polarity
classification using three machine learning methods, reaching 80.99% on
F-Measure and 82.06% on accuracy in binary classification, and 59.85% F-Measure
and 64.62% on accuracy on three point classification.
PuRe: Robust pupil detection for real-time pervasive eye tracking
Authors: Thiago Santini, Wolfgang Fuhl, Enkelejda Kasneci
Date: 2017-12-24 00:00:00
Tasks: Pupil Detection, Specificity

Real-time, accurate, and robust pupil detection is an essential prerequisite
to enable pervasive eye-tracking and its applications -- e.g., gaze-based human
computer interaction, health monitoring, foveated rendering, and advanced
driver assistance. However, automated pupil detection has proved to be an
intricate task in real-world scenarios due to a large mixture of challenges
such as quickly changing illumination and occlusions. In this paper, we
introduce the Pupil Reconstructor PuRe, a method for pupil detection in
pervasive scenarios based on a novel edge segment selection and conditional
segment combination schemes; the method also includes a confidence measure for
the detected pupil. The proposed method was evaluated on over 316,000 images
acquired with four distinct head-mounted eye tracking devices. Results show a
pupil detection rate improvement of over 10 percentage points w.r.t.
state-of-the-art algorithms in the two most challenging data sets (6.46 for all
data sets), further pushing the envelope for pupil detection. Moreover, we
advance the evaluation protocol of pupil detection algorithms by also
considering eye images in which pupils are not present. In this aspect, PuRe
improved precision and specificity w.r.t. state-of-the-art algorithms by 25.05
and 10.94 percentage points, respectively, demonstrating the meaningfulness of
PuRe's confidence measure. PuRe operates in real-time for modern eye trackers
(at 120 fps).