pwc-archive/papers-with-abstracts huggingface dataset:
A Fuzzy Brute Force Matching Method for Binary Image Features
Authors: Erkan Bostanci, Nadia Kanwal, Betul Bostanci, Mehmet Serdar Guzel
Date: 2017-04-20 00:00:00

Matching of binary image features is an important step in many different
computer vision applications. Conventionally, an arbitrary threshold is used to
identify a correct match from incorrect matches using Hamming distance which
may improve or degrade the matching results for different input images. This is
mainly due to the image content which is affected by the scene, lighting and
imaging conditions. This paper presents a fuzzy logic based approach for brute
force matching of image features to overcome this situation. The method was
tested using a well-known image database with known ground truth. The approach
is shown to produce a higher number of correct matches when compared against
constant distance thresholds. The nature of fuzzy logic which allows the
vagueness of information and tolerance to errors has been successfully
exploited in an image processing context. The uncertainty arising from the
imaging conditions has been overcome with the use of compact fuzzy matching
membership functions.
Genetic Algorithm Based Floor Planning System
Authors: Hamide Ozlem Dalgic, Erkan Bostanci, Mehmet Serdar Guzel
Date: 2017-04-20 00:00:00
Tasks: Layout Design

Genetic Algorithms are widely used in many different optimization problems
including layout design. The layout of the shelves play an important role in
the total sales metrics for superstores since this affects the customers'
shopping behaviour. This paper employed a genetic algorithm based approach to
design shelf layout of superstores. The layout design problem was tackled by
using a novel chromosome representation which takes many different parameters
to prevent dead-ends and improve shelf visibility into consideration. Results
show that the approach can produce reasonably good layout designs in very short
amounts of time.
Integrating Additional Knowledge Into Estimation of Graphical Models
Authors: Yunqi Bu, Johannes Lederer
Date: 2017-04-10 00:00:00

In applications of graphical models, we typically have more information than
just the samples themselves. A prime example is the estimation of brain
connectivity networks based on fMRI data, where in addition to the samples
themselves, the spatial positions of the measurements are readily available.
With particular regard for this application, we are thus interested in ways to
incorporate additional knowledge most effectively into graph estimation. Our
approach to this is to make neighborhood selection receptive to additional
knowledge by strengthening the role of the tuning parameters. We demonstrate
that this concept (i) can improve reproducibility, (ii) is computationally
convenient and efficient, and (iii) carries a lucid Bayesian interpretation. We
specifically show that the approach provides effective estimations of brain
connectivity graphs from fMRI data. However, providing a general scheme for the
inclusion of additional knowledge, our concept is expected to have applications
in a wide range of domains.
Fast Generation for Convolutional Autoregressive Models
Authors: Prajit Ramachandran, Tom Le Paine, Pooya Khorrami, Mohammad Babaeizadeh, Shiyu Chang, Yang Zhang, Mark A. Hasegawa-Johnson, Roy H. Campbell, Thomas S. Huang
Date: 2017-04-20 00:00:00

Convolutional autoregressive models have recently demonstrated
state-of-the-art performance on a number of generation tasks. While fast,
parallel training methods have been crucial for their success, generation is
typically implemented in a na\"{i}ve fashion where redundant computations are
unnecessarily repeated. This results in slow generation, making such models
infeasible for production environments. In this work, we describe a method to
speed up generation in convolutional autoregressive models. The key idea is to
cache hidden states to avoid redundant computation. We apply our fast
generation method to the Wavenet and PixelCNN++ models and achieve up to
$21\times$ and $183\times$ speedups respectively.
Image Aesthetic Assessment: An Experimental Survey
Authors: Yubin Deng, Chen Change Loy, Xiaoou Tang
Date: 2016-10-04 00:00:00
Tasks: Binary Classification, Survey

This survey aims at reviewing recent computer vision techniques used in the
assessment of image aesthetic quality. Image aesthetic assessment aims at
computationally distinguishing high-quality photos from low-quality ones based
on photographic rules, typically in the form of binary classification or
quality scoring. A variety of approaches has been proposed in the literature
trying to solve this challenging problem. In this survey, we present a
systematic listing of the reviewed approaches based on visual feature types
(hand-crafted features and deep features) and evaluation criteria (dataset
characteristics and evaluation metrics). Main contributions and novelties of
the reviewed approaches are highlighted and discussed. In addition, following
the emergence of deep learning techniques, we systematically evaluate recent
deep learning settings that are useful for developing a robust deep model for
aesthetic scoring. Experiments are conducted using simple yet solid baselines
that are competitive with the current state-of-the-arts. Moreover, we discuss
the possibility of manipulating the aesthetics of images through computational
approaches. We hope that our survey could serve as a comprehensive reference
source for future research on the study of image aesthetic assessment.
Retrospective Higher-Order Markov Processes for User Trails
Authors: Tao Wu, David Gleich
Date: 2017-04-20 00:00:00

Users form information trails as they browse the web, checkin with a
geolocation, rate items, or consume media. A common problem is to predict what
a user might do next for the purposes of guidance, recommendation, or
prefetching. First-order and higher-order Markov chains have been widely used
methods to study such sequences of data. First-order Markov chains are easy to
estimate, but lack accuracy when history matters. Higher-order Markov chains,
in contrast, have too many parameters and suffer from overfitting the training
data. Fitting these parameters with regularization and smoothing only offers
mild improvements. In this paper we propose the retrospective higher-order
Markov process (RHOMP) as a low-parameter model for such sequences. This model
is a special case of a higher-order Markov chain where the transitions depend
retrospectively on a single history state instead of an arbitrary combination
of history states. There are two immediate computational advantages: the number
of parameters is linear in the order of the Markov chain and the model can be
fit to large state spaces. Furthermore, by providing a specific structure to
the higher-order chain, RHOMPs improve the model accuracy by efficiently
utilizing history states without risks of overfitting the data. We demonstrate
how to estimate a RHOMP from data and we demonstrate the effectiveness of our
method on various real application datasets spanning geolocation data, review
sequences, and business locations. The RHOMP model uniformly outperforms
higher-order Markov chains, Kneser-Ney regularization, and tensor
factorizations in terms of prediction accuracy.
3D fully convolutional networks for subcortical segmentation in MRI: A large-scale study
Authors: J. Dolz, C. Desrosiers, I. Ben Ayed
Date: 2016-12-12 00:00:00
Tasks: 3D Medical Imaging Segmentation, Brain Segmentation, GPU, Medical Image Segmentation, Segmentation

This study investigates a 3D and fully convolutional neural network (CNN) for
subcortical brain structure segmentation in MRI. 3D CNN architectures have been
generally avoided due to their computational and memory requirements during
inference. We address the problem via small kernels, allowing deeper
architectures. We further model both local and global context by embedding
intermediate-layer outputs in the final prediction, which encourages
consistency between features extracted at different scales and embeds
fine-grained information directly in the segmentation process. Our model is
efficiently trained end-to-end on a graphics processing unit (GPU), in a single
stage, exploiting the dense inference capabilities of fully CNNs.
  We performed comprehensive experiments over two publicly available datasets.
First, we demonstrate a state-of-the-art performance on the ISBR dataset. Then,
we report a {\em large-scale} multi-site evaluation over 1112 unregistered
subject datasets acquired from 17 different sites (ABIDE dataset), with ages
ranging from 7 to 64 years, showing that our method is robust to various
acquisition protocols, demographics and clinical factors. Our method yielded
segmentations that are highly consistent with a standard atlas-based approach,
while running in a fraction of the time needed by atlas-based methods and
avoiding registration/normalization steps. This makes it convenient for massive
multi-site neuroanatomical imaging studies. To the best of our knowledge, our
work is the first to study subcortical structure segmentation on such
large-scale and heterogeneous data.
Syntax Aware LSTM Model for Chinese Semantic Role Labeling
Authors: Feng Qian, Lei Sha, Baobao Chang, Lu-chen Liu, Ming Zhang
Date: 2017-04-03 00:00:00
Tasks: Chinese Semantic Role Labeling, Dependency Parsing, Feature Engineering, Semantic Role Labeling

As for semantic role labeling (SRL) task, when it comes to utilizing parsing
information, both traditional methods and recent recurrent neural network (RNN)
based methods use the feature engineering way. In this paper, we propose Syntax
Aware Long Short Time Memory(SA-LSTM). The structure of SA-LSTM modifies
according to dependency parsing information in order to model parsing
information directly in an architecture engineering way instead of feature
engineering way. We experimentally demonstrate that SA-LSTM gains more
improvement from the model architecture. Furthermore, SA-LSTM outperforms the
state-of-the-art on CPB 1.0 significantly according to Student t-test
($p<0.05$).
Call Attention to Rumors: Deep Attention Based Recurrent Neural Networks for Early Rumor Detection
Authors: Tong Chen, Lin Wu, Xue Li, Jun Zhang, Hongzhi Yin, Yang Wang
Date: 2017-04-20 00:00:00
Tasks: Deep Attention

The proliferation of social media in communication and information
dissemination has made it an ideal platform for spreading rumors. Automatically
debunking rumors at their stage of diffusion is known as \textit{early rumor
detection}, which refers to dealing with sequential posts regarding disputed
factual claims with certain variations and highly textual duplication over
time. Thus, identifying trending rumors demands an efficient yet flexible model
that is able to capture long-range dependencies among postings and produce
distinct representations for the accurate early detection. However, it is a
challenging task to apply conventional classification algorithms to rumor
detection in earliness since they rely on hand-crafted features which require
intensive manual efforts in the case of large amount of posts. This paper
presents a deep attention model on the basis of recurrent neural networks (RNN)
to learn \textit{selectively} temporal hidden representations of sequential
posts for identifying rumors. The proposed model delves soft-attention into the
recurrence to simultaneously pool out distinct features with particular focus
and produce hidden representations that capture contextual variations of
relevant posts over time. Extensive experiments on real datasets collected from
social media websites demonstrate that (1) the deep attention based RNN model
outperforms state-of-the-arts that rely on hand-crafted features; (2) the
introduction of soft attention mechanism can effectively distill relevant parts
to rumors from original posts in advance; (3) the proposed method detects
rumors more quickly and accurately than competitors.
SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
Authors: Leon Derczynski, Kalina Bontcheva, Maria Liakata, Rob Procter, Geraldine Wong Sak Hoi, Arkaitz Zubiaga
Date: 2017-04-20 00:00:00
Proceeding: SEMEVAL 2017 8
Tasks: Rumour Detection

Media is full of false claims. Even Oxford Dictionaries named "post-truth" as
the word of 2016. This makes it more important than ever to build systems that
can identify the veracity of a story, and the kind of discourse there is around
it. RumourEval is a SemEval shared task that aims to identify and handle
rumours and reactions to them, in text. We present an annotation scheme, a
large dataset covering multiple topics - each having their own families of
claims and replies - and use these to pose two concrete challenges as well as
the results achieved by participants on these challenges.
Image-Grounded Conversations: Multimodal Context for Natural Question and Response Generation
Authors: Nasrin Mostafazadeh, Chris Brockett, Bill Dolan, Michel Galley, Jianfeng Gao, Georgios P. Spithourakis, Lucy Vanderwende
Date: 2017-01-28 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Response Generation, Retrieval, Visual Grounding

The popularity of image sharing on social media and the engagement it creates
between users reflects the important role that visual context plays in everyday
conversations. We present a novel task, Image-Grounded Conversations (IGC), in
which natural-sounding conversations are generated about a shared image. To
benchmark progress, we introduce a new multiple-reference dataset of
crowd-sourced, event-centric conversations on images. IGC falls on the
continuum between chit-chat and goal-directed conversation models, where visual
grounding constrains the topic of conversation to event-driven utterances.
Experiments with models trained on social media data show that the combination
of visual and textual context enhances the quality of generated conversational
turns. In human evaluation, the gap between human performance and that of both
neural and retrieval architectures suggests that multi-modal IGC presents an
interesting challenge for dialogue research.
Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds
Authors: Daniel R. Jiang, Lina Al-Kanj, Warren B. Powell
Date: 2017-04-20 00:00:00
Tasks: Game of Go

Monte Carlo Tree Search (MCTS), most famously used in game-play artificial
intelligence (e.g., the game of Go), is a well-known strategy for constructing
approximate solutions to sequential decision problems. Its primary innovation
is the use of a heuristic, known as a default policy, to obtain Monte Carlo
estimates of downstream values for states in a decision tree. This information
is used to iteratively expand the tree towards regions of states and actions
that an optimal policy might visit. However, to guarantee convergence to the
optimal action, MCTS requires the entire tree to be expanded asymptotically. In
this paper, we propose a new technique called Primal-Dual MCTS that utilizes
sampled information relaxation upper bounds on potential actions, creating the
possibility of "ignoring" parts of the tree that stem from highly suboptimal
choices. This allows us to prove that despite converging to a partial decision
tree in the limit, the recommended action from Primal-Dual MCTS is optimal. The
new approach shows significant promise when used to optimize the behavior of a
single driver navigating a graph while operating on a ride-sharing platform.
Numerical experiments on a real dataset of 7,000 trips in New Jersey suggest
that Primal-Dual MCTS improves upon standard MCTS by producing deeper decision
trees and exhibits a reduced sensitivity to the size of the action space.
SAFS: A Deep Feature Selection Approach for Precision Medicine
Authors: Milad Zafar Nezhad, Dongxiao Zhu, Xiangrui Li, Kai Yang, Phillip Levy
Date: 2017-04-20 00:00:00
Tasks: feature selection

In this paper, we propose a new deep feature selection method based on deep
architecture. Our method uses stacked auto-encoders for feature representation
in higher-level abstraction. We developed and applied a novel feature learning
approach to a specific precision medicine problem, which focuses on assessing
and prioritizing risk factors for hypertension (HTN) in a vulnerable
demographic subgroup (African-American). Our approach is to use deep learning
to identify significant risk factors affecting left ventricular mass indexed to
body surface area (LVMI) as an indicator of heart damage risk. The results show
that our feature learning and representation approach leads to better results
in comparison with others.
SLAM with Objects using a Nonparametric Pose Graph
Authors: Beipeng Mu, Shih-Yuan Liu, Liam Paull, John Leonard, Jonathan How
Date: 2017-04-19 00:00:00
Tasks: Simultaneous Localization and Mapping

Mapping and self-localization in unknown environments are fundamental
capabilities in many robotic applications. These tasks typically involve the
identification of objects as unique features or landmarks, which requires the
objects both to be detected and then assigned a unique identifier that can be
maintained when viewed from different perspectives and in different images. The
\textit{data association} and \textit{simultaneous localization and mapping}
(SLAM) problems are, individually, well-studied in the literature. But these
two problems are inherently tightly coupled, and that has not been
well-addressed. Without accurate SLAM, possible data associations are
combinatorial and become intractable easily. Without accurate data association,
the error of SLAM algorithms diverge easily. This paper proposes a novel
nonparametric pose graph that models data association and SLAM in a single
framework. An algorithm is further introduced to alternate between inferring
data association and performing SLAM. Experimental results show that our
approach has the new capability of associating object detections and localizing
objects at the same time, leading to significantly better performance on both
the data association and SLAM problems than achieved by considering only one
and ignoring imperfections in the other.
Diagonal RNNs in Symbolic Music Modeling
Authors: Y. Cem Subakan, Paris Smaragdis
Date: 2017-04-18 00:00:00
Tasks: Music Modeling

In this paper, we propose a new Recurrent Neural Network (RNN) architecture.
The novelty is simple: We use diagonal recurrent matrices instead of full. This
results in better test likelihood and faster convergence compared to regular
full RNNs in most of our experiments. We show the benefits of using diagonal
recurrent matrices with popularly used LSTM and GRU architectures as well as
with the vanilla RNN architecture, on four standard symbolic music datasets.
Unassisted Quantitative Evaluation Of Despeckling Filters
Authors: Luis Gomez, Raydonal Ospina, Alejandro C. Frery
Date: 2017-04-19 00:00:00

SAR (Synthetic Aperture Radar) imaging plays a central role in Remote Sensing
due to, among other important features, its ability to provide high-resolution,
day-and-night and almost weather-independent images. SAR images are affected
from a granular contamination, speckle, that can be described by a
multiplicative model. Many despeckling techniques have been proposed in the
literature, as well as measures of the quality of the results they provide.
Assuming the multiplicative model, the observed image $Z$ is the product of two
independent fields: the backscatter $X$ and the speckle $Y$. The result of any
speckle filter is $\widehat X$, an estimator of the backscatter $X$, based
solely on the observed data $Z$. An ideal estimator would be the one for which
the ratio of the observed image to the filtered one $I=Z/\widehat X$ is only
speckle: a collection of independent identically distributed samples from Gamma
variates. We, then, assess the quality of a filter by the closeness of $I$ to
the hypothesis that it is adherent to the statistical properties of pure
speckle. We analyze filters through the ratio image they produce with regards
to first- and second-order statistics: the former check marginal properties,
while the latter verifies lack of structure. A new quantitative image-quality
index is then defined, and applied to state-of-the-art despeckling filters.
This new measure provides consistent results with commonly used quality
measures (equivalent number of looks, PSNR, MSSIM, $\beta$ edge correlation,
and preservation of the mean), and ranks the filters results also in agreement
with their visual analysis. We conclude our study showing that the proposed
measure can be successfully used to optimize the (often many) parameters that
define a speckle filter.
Feature Pyramid Networks for Object Detection
Authors: Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, Serge Belongie
Date: 2016-12-09 00:00:00
Proceeding: CVPR 2017 7
Tasks: GPU, Object, Object Detection, Pedestrian Detection, Semantic Segmentation

Feature pyramids are a basic component in recognition systems for detecting
objects at different scales. But recent deep learning object detectors have
avoided pyramid representations, in part because they are compute and memory
intensive. In this paper, we exploit the inherent multi-scale, pyramidal
hierarchy of deep convolutional networks to construct feature pyramids with
marginal extra cost. A top-down architecture with lateral connections is
developed for building high-level semantic feature maps at all scales. This
architecture, called a Feature Pyramid Network (FPN), shows significant
improvement as a generic feature extractor in several applications. Using FPN
in a basic Faster R-CNN system, our method achieves state-of-the-art
single-model results on the COCO detection benchmark without bells and
whistles, surpassing all existing single-model entries including those from the
COCO 2016 challenge winners. In addition, our method can run at 5 FPS on a GPU
and thus is a practical and accurate solution to multi-scale object detection.
Code will be made publicly available.
Semi-supervised classification for dynamic Android malware detection
Authors: Li Chen, Mingwei Zhang, Chih-Yuan Yang, Ravi Sahita
Date: 2017-04-19 00:00:00
Tasks: Android Malware Detection, Classification, General Classification, Malware Detection

A growing number of threats to Android phones creates challenges for malware
detection. Manually labeling the samples into benign or different malicious
families requires tremendous human efforts, while it is comparably easy and
cheap to obtain a large amount of unlabeled APKs from various sources.
Moreover, the fast-paced evolution of Android malware continuously generates
derivative malware families. These families often contain new signatures, which
can escape detection when using static analysis. These practical challenges can
also cause traditional supervised machine learning algorithms to degrade in
performance.
  In this paper, we propose a framework that uses model-based semi-supervised
(MBSS) classification scheme on the dynamic Android API call logs. The
semi-supervised approach efficiently uses the labeled and unlabeled APKs to
estimate a finite mixture model of Gaussian distributions via conditional
expectation-maximization and efficiently detects malwares during out-of-sample
testing. We compare MBSS with the popular malware detection classifiers such as
support vector machine (SVM), $k$-nearest neighbor (kNN) and linear
discriminant analysis (LDA). Under the ideal classification setting, MBSS has
competitive performance with 98\% accuracy and very low false positive rate for
in-sample classification. For out-of-sample testing, the out-of-sample test
data exhibit similar behavior of retrieving phone information and sending to
the network, compared with in-sample training set. When this similarity is
strong, MBSS and SVM with linear kernel maintain 90\% detection rate while
$k$NN and LDA suffer great performance degradation. When this similarity is
slightly weaker, all classifiers degrade in performance, but MBSS still
performs significantly better than other classifiers.