pwc-archive/papers-with-abstracts huggingface dataset:
On the Linearity of Semantic Change: Investigating Meaning Variation via Dynamic Graph Models
Authors: Steffen Eger, Alexander Mehler
Date: 2017-04-08 00:00:00
Proceeding: ACL 2016 8
Tasks: Time Series, Time Series Analysis

We consider two graph models of semantic change. The first is a time-series
model that relates embedding vectors from one time period to embedding vectors
of previous time periods. In the second, we construct one graph for each word:
nodes in this graph correspond to time points and edge weights to the
similarity of the word's meaning across two time points. We apply our two
models to corpora across three different languages. We find that semantic
change is linear in two senses. Firstly, today's embedding vectors (= meaning)
of words can be derived as linear combinations of embedding vectors of their
neighbors in previous time periods. Secondly, self-similarity of words decays
linearly in time. We consider both findings as new laws/hypotheses of semantic
change.
A Multi-view Context-aware Approach to Android Malware Detection and Malicious Code Localization
Authors: Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu
Date: 2017-04-06 00:00:00
Tasks: Android Malware Detection, Malware Detection, MULTI-VIEW LEARNING

Existing Android malware detection approaches use a variety of features such
as security sensitive APIs, system calls, control-flow structures and
information flows in conjunction with Machine Learning classifiers to achieve
accurate detection. Each of these feature sets provides a unique semantic
perspective (or view) of apps' behaviours with inherent strengths and
limitations. Meaning, some views are more amenable to detect certain attacks
but may not be suitable to characterise several other attacks. Most of the
existing malware detection approaches use only one (or a selected few) of the
aforementioned feature sets which prevent them from detecting a vast majority
of attacks. Addressing this limitation, we propose MKLDroid, a unified
framework that systematically integrates multiple views of apps for performing
comprehensive malware detection and malicious code localisation. The rationale
is that, while a malware app can disguise itself in some views, disguising in
every view while maintaining malicious intent will be much harder.
  MKLDroid uses a graph kernel to capture structural and contextual information
from apps' dependency graphs and identify malice code patterns in each view.
Subsequently, it employs Multiple Kernel Learning (MKL) to find a weighted
combination of the views which yields the best detection accuracy. Besides
multi-view learning, MKLDroid's unique and salient trait is its ability to
locate fine-grained malice code portions in dependency graphs (e.g.,
methods/classes). Through our large-scale experiments on several datasets
(incl. wild apps), we demonstrate that MKLDroid outperforms three
state-of-the-art techniques consistently, in terms of accuracy while
maintaining comparable efficiency. In our malicious code localisation
experiments on a dataset of repackaged malware, MKLDroid was able to identify
all the malice classes with 94% average recall.
Basic Formal Properties of A Relational Model of The Mathematical Theory of Evidence
Authors: Mieczysław A. Kłopotek, Sławomir T. Wierzchoń
Date: 2017-04-08 00:00:00
Tasks: Diversity

The paper presents a novel view of the Dempster-Shafer belief function as a
measure of diversity in relational data bases. It is demonstrated that under
the interpretation The Dempster rule of evidence combination corresponds to the
join operator of the relational database theory. This rough-set based
interpretation is qualitative in nature and can represent a number of belief
function operators.
  The interpretation has the property that Given a definition of the belief
measure of objects in the interpretation domain we can perform operations in
this domain and the measure of the resulting object is derivable from measures
of component objects via belief operator. We demonstrated this property for
Dempster rule of combination, marginalization, Shafer's conditioning,
independent variables, Shenoy's notion of conditional independence of
variables.
  The interpretation is based on rough sets (in connection with decision
tables), but differs from previous interpretations of this type in that it
counts the diversity rather than frequencies in a decision table.
A New Pseudo-color Technique Based on Intensity Information Protection for Passive Sensor Imagery
Authors: Mohammad Reza Khosravi, Habib Rostami, Gholam Reza Ahmadi, Suleiman Mansouri, Ahmad Keshavarz
Date: 2017-04-08 00:00:00

Remote sensing image processing is so important in geo-sciences. Images which
are obtained by different types of sensors might initially be unrecognizable.
To make an acceptable visual perception in the images, some pre-processing
steps (for removing noises and etc) are preformed which they affect the
analysis of images. There are different types of processing according to the
types of remote sensing images. The method that we are going to introduce in
this paper is to use virtual colors to colorize the gray-scale images of
satellite sensors. This approach helps us to have a better analysis on a sample
single-band image which has been taken by Landsat-8 (OLI) sensor (as a
multi-band sensor with natural color bands, its images' natural color can be
compared to synthetic color by our approach). A good feature of this method is
the original image reversibility in order to keep the suitable resolution of
output images.
Seismic facies recognition based on prestack data using deep convolutional autoencoder
Authors: Feng Qian, Miao Yin, Ming-Jun Su, Yaojun Wang, Guangmin Hu
Date: 2017-04-08 00:00:00
Tasks: Clustering, valid

Prestack seismic data carries much useful information that can help us find
more complex atypical reservoirs. Therefore, we are increasingly inclined to
use prestack seismic data for seis- mic facies recognition. However, due to the
inclusion of ex- cessive redundancy, effective feature extraction from prestack
seismic data becomes critical. In this paper, we consider seis- mic facies
recognition based on prestack data as an image clus- tering problem in computer
vision (CV) by thinking of each prestack seismic gather as a picture. We
propose a convo- lutional autoencoder (CAE) network for deep feature learn- ing
from prestack seismic data, which is more effective than principal component
analysis (PCA) in redundancy removing and valid information extraction. Then,
using conventional classification or clustering techniques (e.g. K-means or
self- organizing maps) on the extracted features, we can achieve seismic facies
recognition. We applied our method to the prestack data from physical model and
LZB region. The re- sult shows that our approach is superior to the
conventionals.
3D seismic data denoising using two-dimensional sparse coding scheme
Authors: Ming-Jun Su, Jingbo Chang, Feng Qian, Guangmin Hu, Xiao-Yang Liu
Date: 2017-04-08 00:00:00
Tasks: Denoising, Vocal Bursts Valence Prediction

Seismic data denoising is vital to geophysical applications and the
transform-based function method is one of the most widely used techniques.
However, it is challenging to design a suit- able sparse representation to
express a transform-based func- tion group due to the complexity of seismic
data. In this paper, we apply a seismic data denoising method based on
learning- type overcomplete dictionaries which uses two-dimensional sparse
coding (2DSC). First, we model the input seismic data and dictionaries as
third-order tensors and introduce tensor- linear combinations for data
approximation. Second, we ap- ply learning-type overcomplete dictionary, i.e.,
optimal sparse data representation is achieved through learning and training.
Third, we exploit the alternating minimization algorithm to solve the
optimization problem of seismic denoising. Finally we evaluate its denoising
performance on synthetic seismic data and land data survey. Experiment results
show that the two-dimensional sparse coding scheme reduces computational costs
and enhances the signal-to-noise ratio.
SelfieBoost: A Boosting Algorithm for Deep Learning
Authors: Shai Shalev-Shwartz
Date: 2014-11-13 00:00:00
Tasks: Deep Learning

We describe and analyze a new boosting algorithm for deep learning called
SelfieBoost. Unlike other boosting algorithms, like AdaBoost, which construct
ensembles of classifiers, SelfieBoost boosts the accuracy of a single network.
We prove a $\log(1/\epsilon)$ convergence rate for SelfieBoost under some "SGD
success" assumption which seems to hold in practice.
Training a Subsampling Mechanism in Expectation
Authors: Colin Raffel, Dieterich Lawson
Date: 2017-02-22 00:00:00

We describe a mechanism for subsampling sequences and show how to compute its
expected output so that it can be trained with standard backpropagation. We
test this approach on a simple toy problem and discuss its shortcomings.
Optimal change point detection in Gaussian processes
Authors: Hossein Keshavarz, Clayton Scott, XuanLong Nguyen
Date: 2015-06-03 00:00:00
Tasks: Change Point Detection, Gaussian Processes, Time Series, Time Series Analysis

We study the problem of detecting a change in the mean of one-dimensional
Gaussian process data. This problem is investigated in the setting of
increasing domain (customarily employed in time series analysis) and in the
setting of fixed domain (typically arising in spatial data analysis). We
propose a detection method based on the generalized likelihood ratio test
(GLRT), and show that our method achieves nearly asymptotically optimal rate in
the minimax sense, in both settings. The salient feature of the proposed method
is that it exploits in an efficient way the data dependence captured by the
Gaussian process covariance structure. When the covariance is not known, we
propose the plug-in GLRT method and derive conditions under which the method
remains asymptotically near optimal. By contrast, the standard CUSUM method,
which does not account for the covariance structure, is shown to be
asymptotically optimal only in the increasing domain. Our algorithms and
accompanying theory are applicable to a wide variety of covariance structures,
including the Matern class, the powered exponential class, and others. The
plug-in GLRT method is shown to perform well for maximum likelihood estimators
with a dense covariance matrix.
Stein Variational Policy Gradient
Authors: Yang Liu, Prajit Ramachandran, Qiang Liu, Jian Peng
Date: 2017-04-07 00:00:00
Tasks: Bayesian Inference, continuous-control, Continuous Control, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Policy gradient methods have been successfully applied to many complex
reinforcement learning problems. However, policy gradient methods suffer from
high variance, slow convergence, and inefficient exploration. In this work, we
introduce a maximum entropy policy optimization framework which explicitly
encourages parameter exploration, and show that this framework can be reduced
to a Bayesian inference problem. We then propose a novel Stein variational
policy gradient method (SVPG) which combines existing policy gradient methods
and a repulsive functional to generate a set of diverse but well-behaved
policies. SVPG is robust to initialization and can easily be implemented in a
parallel manner. On continuous control problems, we find that implementing SVPG
on top of REINFORCE and advantage actor-critic algorithms improves both average
return and data efficiency.
Learning Where to Look: Data-Driven Viewpoint Set Selection for 3D Scenes
Authors: Kyle Genova, Manolis Savva, Angel X. Chang, Thomas Funkhouser
Date: 2017-04-07 00:00:00
Tasks: Segmentation, Semantic Segmentation

The use of rendered images, whether from completely synthetic datasets or
from 3D reconstructions, is increasingly prevalent in vision tasks. However,
little attention has been given to how the selection of viewpoints affects the
performance of rendered training sets. In this paper, we propose a data-driven
approach to view set selection. Given a set of example images, we extract
statistics describing their contents and generate a set of views matching the
distribution of those statistics. Motivated by semantic segmentation tasks, we
model the spatial distribution of each semantic object category within an image
view volume. We provide a search algorithm that generates a sampling of likely
candidate views according to the example distribution, and a set selection
algorithm that chooses a subset of the candidates that jointly cover the
example distribution. Results of experiments with these algorithms on SUNCG
indicate that they are indeed able to produce view distributions similar to an
example set from NYUDv2 according to the earth mover's distance. Furthermore,
the selected views improve performance on semantic segmentation compared to
alternative view selection algorithms.
Pixelwise Instance Segmentation with a Dynamically Instantiated Network
Authors: Anurag Arnab, Philip H. S. Torr
Date: 2017-04-07 00:00:00
Proceeding: CVPR 2017 7
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Panoptic Segmentation, Segmentation, Semantic Segmentation

Semantic segmentation and object detection research have recently achieved
rapid progress. However, the former task has no notion of different instances
of the same object, and the latter operates at a coarse, bounding-box level. We
propose an Instance Segmentation system that produces a segmentation map where
each pixel is assigned an object class and instance identity label. Most
approaches adapt object detectors to produce segments instead of boxes. In
contrast, our method is based on an initial semantic segmentation module, which
feeds into an instance subnetwork. This subnetwork uses the initial
category-level segmentation, along with cues from the output of an object
detector, within an end-to-end CRF to predict instances. This part of our model
is dynamically instantiated to produce a variable number of instances per
image. Our end-to-end approach requires no post-processing and considers the
image holistically, instead of processing independent proposals. Therefore,
unlike some related work, a pixel cannot belong to multiple instances.
Furthermore, far more precise segmentations are achieved, as shown by our
state-of-the-art results (particularly at high IoU thresholds) on the Pascal
VOC and Cityscapes datasets.
A Trolling Hierarchy in Social Media and A Conditional Random Field For Trolling Detection
Authors: Luis Gerardo Mojica
Date: 2017-04-07 00:00:00

An-ever increasing number of social media websites, electronic newspapers and
Internet forums allow visitors to leave comments for others to read and
interact. This exchange is not free from participants with malicious
intentions, which do not contribute with the written conversation. Among
different communities users adopt strategies to handle such users. In this
paper we present a comprehensive categorization of the trolling phenomena
resource, inspired by politeness research and propose a model that jointly
predicts four crucial aspects of trolling: intention, interpretation, intention
disclosure and response strategy. Finally, we present a new annotated dataset
containing excerpts of conversations involving trolls and the interactions with
other users that we hope will be a useful resource for the research community.
Uncovering Group Level Insights with Accordant Clustering
Authors: Amit Dhurandhar, Margareta Ackerman, Xiang Wang
Date: 2017-04-07 00:00:00
Tasks: Clustering

Clustering is a widely-used data mining tool, which aims to discover
partitions of similar items in data. We introduce a new clustering paradigm,
\emph{accordant clustering}, which enables the discovery of (predefined) group
level insights. Unlike previous clustering paradigms that aim to understand
relationships amongst the individual members, the goal of accordant clustering
is to uncover insights at the group level through the analysis of their
members. Group level insight can often support a call to action that cannot be
informed through previous clustering techniques. We propose the first accordant
clustering algorithm, and prove that it finds near-optimal solutions when data
possesses inherent cluster structure. The insights revealed by accordant
clusterings enabled experts in the field of medicine to isolate successful
treatments for a neurodegenerative disease, and those in finance to discover
patterns of unnecessary spending.
AppLP: A Dialogue on Applications of Logic Programming
Authors: David S. Warren, Yanhong A. Liu
Date: 2017-04-07 00:00:00

This document describes the contributions of the 2016 Applications of Logic
Programming Workshop (AppLP), which was held on October 17 and associated with
the International Conference on Logic Programming (ICLP) in Flushing, New York
City.
Pareto Optimality and Strategy Proofness in Group Argument Evaluation (Extended Version)
Authors: Edmond Awad, Martin Caminada, Gabriella Pigozzi, Mikołaj Podlaszewski, Iyad Rahwan
Date: 2016-04-03 00:00:00

An inconsistent knowledge base can be abstracted as a set of arguments and a
defeat relation among them. There can be more than one consistent way to
evaluate such an argumentation graph. Collective argument evaluation is the
problem of aggregating the opinions of multiple agents on how a given set of
arguments should be evaluated. It is crucial not only to ensure that the
outcome is logically consistent, but also satisfies measures of social
optimality and immunity to strategic manipulation. This is because agents have
their individual preferences about what the outcome ought to be. In the current
paper, we analyze three previously introduced argument-based aggregation
operators with respect to Pareto optimality and strategy proofness under
different general classes of agent preferences. We highlight fundamental
trade-offs between strategic manipulability and social optimality on one hand,
and classical logical criteria on the other. Our results motivate further
investigation into the relationship between social choice and argumentation
theory. The results are also relevant for choosing an appropriate aggregation
operator given the criteria that are considered more important, as well as the
nature of agents' preferences.
Three-Dimensional Segmentation of Vesicular Networks of Fungal Hyphae in Macroscopic Microscopy Image Stacks
Authors: P. Saponaro, W. Treible, A. Kolagunda, S. Rhein, J. Caplan, C. Kambhamettu, R. Wisser
Date: 2017-04-07 00:00:00

Automating the extraction and quantification of features from
three-dimensional (3-D) image stacks is a critical task for advancing computer
vision research. The union of 3-D image acquisition and analysis enables the
quantification of biological resistance of a plant tissue to fungal infection
through the analysis of attributes such as fungal penetration depth, fungal
mass, and branching of the fungal network of connected cells. From an image
processing perspective, these tasks reduce to segmentation of vessel-like
structures and the extraction of features from their skeletonization. In order
to sample multiple infection events for analysis, we have developed an approach
we refer to as macroscopic microscopy. However, macroscopic microscopy produces
high-resolution image stacks that pose challenges to routine approaches and are
difficult for a human to annotate to obtain ground truth data. We present a
synthetic hyphal network generator, a comparison of several vessel segmentation
methods, and a minimum spanning tree method for connecting small gaps resulting
from imperfections in imaging or incomplete skeletonization of hyphal networks.
Qualitative results are shown for real microscopic data. We believe the
comparison of vessel detectors on macroscopic microscopy data, the synthetic
vessel generator, and the gap closing technique are beneficial to the image
processing community.
Automated Unsupervised Segmentation of Liver Lesions in CT scans via Cahn-Hilliard Phase Separation
Authors: Jana Lipková, Markus Rempfler, Patrick Christ, John Lowengrub, Bjoern H. Menze
Date: 2017-04-07 00:00:00
Tasks: Lesion Detection, Segmentation

The segmentation of liver lesions is crucial for detection, diagnosis and
monitoring progression of liver cancer. However, design of accurate automated
methods remains challenging due to high noise in CT scans, low contrast between
liver and lesions, as well as large lesion variability. We propose a 3D
automatic, unsupervised method for liver lesions segmentation using a phase
separation approach. It is assumed that liver is a mixture of two phases:
healthy liver and lesions, represented by different image intensities polluted
by noise. The Cahn-Hilliard equation is used to remove the noise and separate
the mixture into two distinct phases with well-defined interfaces. This
simplifies the lesion detection and segmentation task drastically and enables
to segment liver lesions by thresholding the Cahn-Hilliard solution. The method
was tested on 3Dircadb and LITS dataset.
Fast Spectral Clustering Using Autoencoders and Landmarks
Authors: Ershad Banijamali, Ali Ghodsi
Date: 2017-04-07 00:00:00
Tasks: Clustering

In this paper, we introduce an algorithm for performing spectral clustering
efficiently. Spectral clustering is a powerful clustering algorithm that
suffers from high computational complexity, due to eigen decomposition. In this
work, we first build the adjacency matrix of the corresponding graph of the
dataset. To build this matrix, we only consider a limited number of points,
called landmarks, and compute the similarity of all data points with the
landmarks. Then, we present a definition of the Laplacian matrix of the graph
that enable us to perform eigen decomposition efficiently, using a deep
autoencoder. The overall complexity of the algorithm for eigen decomposition is
$O(np)$, where $n$ is the number of data points and $p$ is the number of
landmarks. At last, we evaluate the performance of the algorithm in different
experiments.
Evolutionary Many-Objective Optimization Based on Adversarial Decomposition
Authors: Mengyuan Wu, Ke Li, Sam Kwong, Qingfu Zhang
Date: 2017-04-07 00:00:00

The decomposition-based method has been recognized as a major approach for
multi-objective optimization. It decomposes a multi-objective optimization
problem into several single-objective optimization subproblems, each of which
is usually defined as a scalarizing function using a weight vector. Due to the
characteristics of the contour line of a particular scalarizing function, the
performance of the decomposition-based method strongly depends on the Pareto
front's shape by merely using a single scalarizing function, especially when
facing a large number of objectives. To improve the flexibility of the
decomposition-based method, this paper develops an adversarial decomposition
method that leverages the complementary characteristics of two different
scalarizing functions within a single paradigm. More specifically, we maintain
two co-evolving populations simultaneously by using different scalarizing
functions. In order to avoid allocating redundant computational resources to
the same region of the Pareto front, we stably match these two co-evolving
populations into one-one solution pairs according to their working regions of
the Pareto front. Then, each solution pair can at most contribute one mating
parent during the mating selection process. Comparing with nine
state-of-the-art many-objective optimizers, we have witnessed the competitive
performance of our proposed algorithm on 130 many-objective test instances with
various characteristics and Pareto front's shapes.