pwc-archive/papers-with-abstracts huggingface dataset:
Evolutionary forces in language change
Authors: Christopher A. Ahern, Mitchell G. Newberry, Robin Clark, Joshua B. Plotkin
Date: 2016-08-02 00:00:00
Tasks: Negation

Languages and genes are both transmitted from generation to generation, with
opportunity for differential reproduction and survivorship of forms. Here we
apply a rigorous inference framework, drawn from population genetics, to
distinguish between two broad mechanisms of language change: drift and
selection. Drift is change that results from stochasticity in transmission and
it may occur in the absence of any intrinsic difference between linguistic
forms; whereas selection is truly an evolutionary force arising from intrinsic
differences -- for example, when one form is preferred by members of the
population. Using large corpora of parsed texts spanning the 12th century to
the 21st century, we analyze three examples of grammatical changes in English:
the regularization of past-tense verbs, the rise of the periphrastic `do', and
syntactic variation in verbal negation. We show that we can reject stochastic
drift in favor of a selective force driving some of these language changes, but
not others. The strength of drift depends on a word's frequency, and so drift
provides an alternative explanation for why some words are more prone to change
than others. Our results suggest an important role for stochasticity in
language change, and they provide a null model against which selective theories
of language evolution must be compared.
Efficient Segmental Cascades for Speech Recognition
Authors: Hao Tang, Weiran Wang, Kevin Gimpel, Karen Livescu
Date: 2016-08-02 00:00:00
Tasks: speech-recognition, Speech Recognition

Discriminative segmental models offer a way to incorporate flexible feature
functions into speech recognition. However, their appeal has been limited by
their computational requirements, due to the large number of possible segments
to consider. Multi-pass cascades of segmental models introduce features of
increasing complexity in different passes, where in each pass a segmental model
rescores lattices produced by a previous (simpler) segmental model. In this
paper, we explore several ways of making segmental cascades efficient and
practical: reducing the feature set in the first pass, frame subsampling, and
various pruning approaches. In experiments on phonetic recognition, we find
that with a combination of such techniques, it is possible to maintain
competitive performance while greatly reducing decoding, pruning, and training
time.
Bounds on the Number of Measurements for Reliable Compressive Classification
Authors: Hugo Reboredo, Francesco Renna, Robert Calderbank, Miguel R. D. Rodrigues
Date: 2016-07-11 00:00:00
Tasks: Classification, General Classification

This paper studies the classification of high-dimensional Gaussian signals
from low-dimensional noisy, linear measurements. In particular, it provides
upper bounds (sufficient conditions) on the number of measurements required to
drive the probability of misclassification to zero in the low-noise regime,
both for random measurements and designed ones. Such bounds reveal two
important operational regimes that are a function of the characteristics of the
source: i) when the number of classes is less than or equal to the dimension of
the space spanned by signals in each class, reliable classification is possible
in the low-noise regime by using a one-vs-all measurement design; ii) when the
dimension of the spaces spanned by signals in each class is lower than the
number of classes, reliable classification is guaranteed in the low-noise
regime by using a simple random measurement design. Simulation results both
with synthetic and real data show that our analysis is sharp, in the sense that
it is able to gauge the number of measurements required to drive the
misclassification probability to zero in the low-noise regime.
Modeling Spatial and Temporal Cues for Multi-label Facial Action Unit Detection
Authors: Wen-Sheng Chu, Fernando de la Torre, Jeffrey F. Cohn
Date: 2016-08-02 00:00:00
Tasks: Action Unit Detection, Facial Action Unit Detection

Facial action units (AUs) are essential to decode human facial expressions.
Researchers have focused on training AU detectors with a variety of features
and classifiers. However, several issues remain. These are spatial
representation, temporal modeling, and AU correlation. Unlike most studies that
tackle these issues separately, we propose a hybrid network architecture to
jointly address them. Specifically, spatial representations are extracted by a
Convolutional Neural Network (CNN), which, as analyzed in this paper, is able
to reduce person-specific biases caused by hand-crafted features (eg, SIFT and
Gabor). To model temporal dependencies, Long Short-Term Memory (LSTMs) are
stacked on top of these representations, regardless of the lengths of input
videos. The outputs of CNNs and LSTMs are further aggregated into a fusion
network to produce per-frame predictions of 12 AUs. Our network naturally
addresses the three issues, and leads to superior performance compared to
existing methods that consider these issues independently. Extensive
experiments were conducted on two large spontaneous datasets, GFT and BP4D,
containing more than 400,000 frames coded with 12 AUs. On both datasets, we
report significant improvement over a standard multi-label CNN and
feature-based state-of-the-art. Finally, we provide visualization of the
learned AU models, which, to our best knowledge, reveal how machines see facial
AUs for the first time.
Towards Learning to Perceive and Reason About Liquids
Authors: Connor Schenck, Dieter Fox
Date: 2016-08-02 00:00:00
Tasks: Deep Learning

Recent advances in AI and robotics have claimed many incredible results with
deep learning, yet no work to date has applied deep learning to the problem of
liquid perception and reasoning. In this paper, we apply fully-convolutional
deep neural networks to the tasks of detecting and tracking liquids. We
evaluate three models: a single-frame network, multi-frame network, and a LSTM
recurrent network. Our results show that the best liquid detection results are
achieved when aggregating data over multiple frames and that the LSTM network
outperforms the other two in both tasks. This suggests that LSTM-based neural
networks have the potential to be a key component for enabling robots to handle
liquids using robust, closed-loop controllers.
Learning text representation using recurrent convolutional neural network with highway layers
Authors: Ying Wen, Wei-Nan Zhang, Rui Luo, Jun Wang
Date: 2016-06-22 00:00:00
Tasks: Sentiment Analysis

Recently, the rapid development of word embedding and neural networks has
brought new inspiration to various NLP and IR tasks. In this paper, we describe
a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN)
with highway layers. The highway network module is incorporated in the middle
takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module
in the first stage and provides the Convolutional Neural Network (CNN) module
in the last stage with the input. The experiment shows that our model
outperforms common neural network models (CNN, RNN, Bi-RNN) on a sentiment
analysis task. Besides, the analysis of how sequence length influences the RCNN
with highway layers shows that our model could learn good representation for
the long text.
Relational Similarity Machines
Authors: Ryan A. Rossi, Rong Zhou, Nesreen K. Ahmed
Date: 2016-08-02 00:00:00
Tasks: General Classification, Multi-class Classification, Relational Reasoning

This paper proposes Relational Similarity Machines (RSM): a fast, accurate,
and flexible relational learning framework for supervised and semi-supervised
learning tasks. Despite the importance of relational learning, most existing
methods are hard to adapt to different settings, due to issues with efficiency,
scalability, accuracy, and flexibility for handling a wide variety of
classification problems, data, constraints, and tasks. For instance, many
existing methods perform poorly for multi-class classification problems, graphs
that are sparsely labeled or network data with low relational autocorrelation.
In contrast, the proposed relational learning framework is designed to be (i)
fast for learning and inference at real-time interactive rates, and (ii)
flexible for a variety of learning settings (multi-class problems), constraints
(few labeled instances), and application domains. The experiments demonstrate
the effectiveness of RSM for a variety of tasks and data.
Fitting a Simplicial Complex using a Variation of k-means
Authors: Piotr Beben
Date: 2016-07-13 00:00:00
Tasks: Clustering, Dimensionality Reduction

We give a simple and effective two stage algorithm for approximating a point
cloud $\mathcal{S}\subset\mathbb{R}^m$ by a simplicial complex $K$. The first
stage is an iterative fitting procedure that generalizes k-means clustering,
while the second stage involves deleting redundant simplices. A form of
dimension reduction of $\mathcal{S}$ is obtained as a consequence.
PageRank in Malware Categorization
Authors: BooJoong Kang, Suleiman Y. Yerima, Kieran McLaughlin, Sakir Sezer
Date: 2016-08-02 00:00:00
Tasks: BIG-bench Machine Learning, Malware Analysis

In this paper, we propose a malware categorization method that models malware
behavior in terms of instructions using PageRank. PageRank computes ranks of
web pages based on structural information and can also compute ranks of
instructions that represent the structural information of the instructions in
malware analysis methods. Our malware categorization method uses the computed
ranks as features in machine learning algorithms. In the evaluation, we compare
the effectiveness of different PageRank algorithms and also investigate bagging
and boosting algorithms to improve the categorization accuracy.
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
Authors: Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao, Dahua Lin, Xiaoou Tang, Luc van Gool
Date: 2016-08-02 00:00:00
Tasks: Action Classification, Action Recognition, Action Recognition In Videos, Multimodal Activity Recognition, Temporal Action Localization

Deep convolutional networks have achieved great success for visual
recognition in still images. However, for action recognition in videos, the
advantage over traditional methods is not so evident. This paper aims to
discover the principles to design effective ConvNet architectures for action
recognition in videos and learn these models given limited training samples.
Our first contribution is temporal segment network (TSN), a novel framework for
video-based action recognition. which is based on the idea of long-range
temporal structure modeling. It combines a sparse temporal sampling strategy
and video-level supervision to enable efficient and effective learning using
the whole action video. The other contribution is our study on a series of good
practices in learning ConvNets on video data with the help of temporal segment
network. Our approach obtains the state-the-of-art performance on the datasets
of HMDB51 ( $ 69.4\% $) and UCF101 ($ 94.2\% $). We also visualize the learned
ConvNet models, which qualitatively demonstrates the effectiveness of temporal
segment network and the proposed good practices.
A study of the effect of JPG compression on adversarial images
Authors: Gintare Karolina Dziugaite, Zoubin Ghahramani, Daniel M. Roy
Date: 2016-08-02 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Neural network image classifiers are known to be vulnerable to adversarial
images, i.e., natural images which have been modified by an adversarial
perturbation specifically designed to be imperceptible to humans yet fool the
classifier. Not only can adversarial images be generated easily, but these
images will often be adversarial for networks trained on disjoint subsets of
data or with different architectures. Adversarial images represent a potential
security risk as well as a serious machine learning challenge---it is clear
that vulnerable neural networks perceive images very differently from humans.
Noting that virtually every image classification data set is composed of JPG
images, we evaluate the effect of JPG compression on the classification of
adversarial images. For Fast-Gradient-Sign perturbations of small magnitude, we
found that JPG compression often reverses the drop in classification accuracy
to a large extent, but not always. As the magnitude of the perturbations
increases, JPG recompression alone is insufficient to reverse the effect.
A New Android Malware Detection Approach Using Bayesian Classification
Authors: Suleiman Y. Yerima, Sakir Sezer, Gavin McWilliams, Igor Muttik
Date: 2016-08-02 00:00:00
Tasks: Android Malware Detection, Classification, General Classification, Malware Detection

Mobile malware has been growing in scale and complexity as smartphone usage
continues to rise. Android has surpassed other mobile platforms as the most
popular whilst also witnessing a dramatic increase in malware targeting the
platform. A worrying trend that is emerging is the increasing sophistication of
Android malware to evade detection by traditional signature-based scanners. As
such, Android app marketplaces remain at risk of hosting malicious apps that
could evade detection before being downloaded by unsuspecting users. Hence, in
this paper we present an effective approach to alleviate this problem based on
Bayesian classification models obtained from static code analysis. The models
are built from a collection of code and app characteristics that provide
indicators of potential malicious activities. The models are evaluated with
real malware samples in the wild and results of experiments are presented to
demonstrate the effectiveness of the proposed approach.
Mitochondria-based Renal Cell Carcinoma Subtyping: Learning from Deep vs. Flat Feature Representations
Authors: Peter J. Schüffler, Judy Sarungbam, Hassan Muhammad, Ed Reznik, Satish K. Tickoo, Thomas J. Fuchs
Date: 2016-08-02 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Accurate subtyping of renal cell carcinoma (RCC) is of crucial importance for
understanding disease progression and for making informed treatment decisions.
New discoveries of significant alterations to mitochondria between subtypes
make immunohistochemical (IHC) staining based image classification an
imperative. Until now, accurate quantification and subtyping was made
impossible by huge IHC variations, the absence of cell membrane staining for
cytoplasm segmentation as well as the complete lack of systems for robust and
reproducible image based classification. In this paper we present a
comprehensive classification framework to overcome these challenges for tissue
microarrays (TMA) of RCCs. We compare and evaluate models based on domain
specific hand-crafted "flat"-features versus "deep" feature representations
from various layers of a pre-trained convolutional neural network (CNN). The
best model reaches a cross-validation accuracy of 89%, which demonstrates for
the first time, that robust mitochondria-based subtyping of renal cancer is
feasible
Semantic Representations of Word Senses and Concepts
Authors: José Camacho-Collados, Ignacio Iacobacci, Roberto Navigli, Mohammad Taher Pilehvar
Date: 2016-08-02 00:00:00

Representing the semantics of linguistic items in a machine-interpretable
form has been a major goal of Natural Language Processing since its earliest
days. Among the range of different linguistic items, words have attracted the
most research attention. However, word representations have an important
limitation: they conflate different meanings of a word into a single vector.
Representations of word senses have the potential to overcome this inherent
limitation. Indeed, the representation of individual word senses and concepts
has recently gained in popularity with several experimental results showing
that a considerable performance improvement can be achieved across different
NLP applications upon moving from word level to the deeper sense and concept
levels. Another interesting point regarding the representation of concepts and
word senses is that these models can be seamlessly applied to other linguistic
items, such as words, phrases and sentences.
High Accuracy Android Malware Detection Using Ensemble Learning
Authors: Suleiman Y. Yerima, Sakir Sezer, Igor Muttik
Date: 2016-08-02 00:00:00
Tasks: Android Malware Detection, BIG-bench Machine Learning, Ensemble Learning, Malware Detection, Vocal Bursts Intensity Prediction

With over 50 billion downloads and more than 1.3 million apps in the Google
official market, Android has continued to gain popularity amongst smartphone
users worldwide. At the same time there has been a rise in malware targeting
the platform, with more recent strains employing highly sophisticated detection
avoidance techniques. As traditional signature based methods become less potent
in detecting unknown malware, alternatives are needed for timely zero-day
discovery. Thus this paper proposes an approach that utilizes ensemble learning
for Android malware detection. It combines advantages of static analysis with
the efficiency and performance of ensemble machine learning to improve Android
malware detection accuracy. The machine learning models are built using a large
repository of malware samples and benign apps from a leading antivirus vendor.
Experimental results and analysis presented shows that the proposed method
which uses a large feature space to leverage the power of ensemble learning is
capable of 97.3 to 99 percent detection accuracy with very low false positive
rates.
The DLVHEX System for Knowledge Representation: Recent Advances (System Description)
Authors: Christoph Redl
Date: 2016-07-29 00:00:00

The DLVHEX system implements the HEX-semantics, which integrates answer set
programming (ASP) with arbitrary external sources. Since its first release ten
years ago, significant advancements were achieved. Most importantly, the
exploitation of properties of external sources led to efficiency improvements
and flexibility enhancements of the language, and technical improvements on the
system side increased user's convenience. In this paper, we present the current
status of the system and point out the most important recent enhancements over
early versions. While existing literature focuses on theoretical aspects and
specific components, a bird's eye view of the overall system is missing. In
order to promote the system for real-world applications, we further present
applications which were already successfully realized on top of DLVHEX. This
paper is under consideration for acceptance in Theory and Practice of Logic
Programming.
CUHK & ETHZ & SIAT Submission to ActivityNet Challenge 2016
Authors: Yuanjun Xiong, Li-Min Wang, Zhe Wang, Bo-Wen Zhang, Hang Song, Wei Li, Dahua Lin, Yu Qiao, Luc van Gool, Xiaoou Tang
Date: 2016-08-02 00:00:00
Tasks: General Classification, Video Classification

This paper presents the method that underlies our submission to the untrimmed
video classification task of ActivityNet Challenge 2016. We follow the basic
pipeline of temporal segment networks and further raise the performance via a
number of other techniques. Specifically, we use the latest deep model
architecture, e.g., ResNet and Inception V3, and introduce new aggregation
schemes (top-k and attention-weighted pooling). Additionally, we incorporate
the audio as a complementary channel, extracting relevant information via a CNN
applied to the spectrograms. With these techniques, we derive an ensemble of
deep models, which, together, attains a high classification accuracy (mAP
$93.23\%$) on the testing set and secured the first place in the challenge.
New word analogy corpus for exploring embeddings of Czech words
Authors: Lukáš Svoboda, Tomáš Brychcín
Date: 2016-08-02 00:00:00
Tasks: Word Embeddings

The word embedding methods have been proven to be very useful in many tasks
of NLP (Natural Language Processing). Much has been investigated about word
embeddings of English words and phrases, but only little attention has been
dedicated to other languages.
  Our goal in this paper is to explore the behavior of state-of-the-art word
embedding methods on Czech, the language that is characterized by very rich
morphology. We introduce new corpus for word analogy task that inspects
syntactic, morphosyntactic and semantic properties of Czech words and phrases.
We experiment with Word2Vec and GloVe algorithms and discuss the results on
this corpus. The corpus is available for the research community.
Shape and Centroid Independent Clustring Algorithm for Crowd Management Applications
Authors: Yasser Mohammad Seddiq, A. A. Alharbiy, Moayyad Hamza Ghunaim
Date: 2016-08-02 00:00:00
Tasks: Clustering, Management

Clustering techniques play an important role in data mining and its related
applications. Among the challenging applications that require robust and
real-time processing are crowd management and group trajectory applications. In
this paper, a robust and low-complexity clustering algorithm is proposed. It is
capable of processing data in a manner that is shape and centroid independent.
The algorithm is of low complexity due to the novel technique to compute the
matrix power. The algorithm was tested on real and synthetic data and test
results are reported.
Learning Neural Network Architectures using Backpropagation
Authors: Suraj Srinivas, R. Venkatesh Babu
Date: 2015-11-17 00:00:00

Deep neural networks with millions of parameters are at the heart of many
state of the art machine learning models today. However, recent works have
shown that models with much smaller number of parameters can also perform just
as well. In this work, we introduce the problem of architecture-learning, i.e;
learning the architecture of a neural network along with weights. We introduce
a new trainable parameter called tri-state ReLU, which helps in eliminating
unnecessary neurons. We also propose a smooth regularizer which encourages the
total number of neurons after elimination to be small. The resulting objective
is differentiable and simple to optimize. We experimentally validate our method
on both small and large networks, and show that it can learn models with a
considerably small number of parameters without affecting prediction accuracy.
Interactive Removal and Ground Truth for Difficult Shadow Scenes
Authors: Han Gong, Darren P. Cosker
Date: 2016-08-02 00:00:00
Tasks: Shadow Removal

A user-centric method for fast, interactive, robust and high-quality shadow
removal is presented. Our algorithm can perform detection and removal in a
range of difficult cases: such as highly textured and colored shadows. To
perform detection an on-the-fly learning approach is adopted guided by two
rough user inputs for the pixels of the shadow and the lit area. After
detection, shadow removal is performed by registering the penumbra to a
normalized frame which allows us efficient estimation of non-uniform shadow
illumination changes, resulting in accurate and robust removal. Another major
contribution of this work is the first validated and multi-scene category
ground truth for shadow removal algorithms. This data set containing 186 images
eliminates inconsistencies between shadow and shadow-free images and provides a
range of different shadow types such as soft, textured, colored and broken
shadow. Using this data, the most thorough comparison of state-of-the-art
shadow removal methods to date is performed, showing our proposed new algorithm
to outperform the state-of-the-art across several measures and shadow category.
To complement our dataset, an online shadow removal benchmark website is also
presented to encourage future open comparisons in this challenging field of
research.