pwc-archive/papers-with-abstracts huggingface dataset:
Imbalanced Malware Images Classification: a CNN based Approach
Authors: Songqing Yue, Tianyang Wang
Date: 2017-08-27 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Deep convolutional neural networks (CNNs) can be applied to malware binary detection via image classification. The performance, however, is degraded due to the imbalance of malware families (classes). To mitigate this issue, we propose a simple yet effective weighted softmax loss which can be employed as the final layer of deep CNNs. The original softmax loss is weighted, and the weight value can be determined according to class size. A scaling parameter is also included in computing the weight. Proper selection of this parameter is studied and an empirical option is suggested. The weighted loss aims at alleviating the impact of data imbalance in an end-to-end learning fashion. To validate the efficacy, we deploy the proposed weighted loss in a pre-trained deep CNN model and fine-tune it to achieve promising results on malware images classification. Extensive experiments also demonstrate that the new loss function can well fit other typical CNNs, yielding an improved classification performance.
On the Protection of Private Information in Machine Learning Systems: Two Recent Approaches
Authors: Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Nicolas Papernot, Kunal Talwar, Li Zhang
Date: 2017-08-26 00:00:00
Tasks: BIG-bench Machine Learning, valid

The recent, remarkable growth of machine learning has led to intense interest
in the privacy of the data on which machine learning relies, and to new
techniques for preserving privacy. However, older ideas about privacy may well
remain valid and useful. This note reviews two recent works on privacy in the
light of the wisdom of some of the early literature, in particular the
principles distilled by Saltzer and Schroeder in the 1970s.
Facial Expression Recognition using Visual Saliency and Deep Learning
Authors: Viraj Mavani, Shanmuganathan Raman, Krishna P. Miyapuram
Date: 2017-08-26 00:00:00
Tasks: Deep Learning, Facial Expression Recognition, Facial Expression Recognition (FER), Saliency Prediction

We have developed a convolutional neural network for the purpose of
recognizing facial expressions in human beings. We have fine-tuned the existing
convolutional neural network model trained on the visual recognition dataset
used in the ILSVRC2012 to two widely used facial expression datasets - CFEE and
RaFD, which when trained and tested independently yielded test accuracies of
74.79% and 95.71%, respectively. Generalization of results was evident by
training on one dataset and testing on the other. Further, the image product of
the cropped faces and their visual saliency maps were computed using Deep
Multi-Layer Network for saliency prediction and were fed to the facial
expression recognition CNN. In the most generalized experiment, we observed the
top-1 accuracy in the test set to be 65.39%. General confusion trends between
different facial expressions as exhibited by humans were also observed.
AGA: Attribute Guided Augmentation
Authors: Mandar Dixit, Roland Kwitt, Marc Niethammer, Nuno Vasconcelos
Date: 2016-12-08 00:00:00
Tasks: Attribute, Data Augmentation, Decoder, Object Recognition, Scene Recognition, Transfer Learning

We consider the problem of data augmentation, i.e., generating artificial
samples to extend a given corpus of training data. Specifically, we propose
attributed-guided augmentation (AGA) which learns a mapping that allows to
synthesize data such that an attribute of a synthesized sample is at a desired
value or strength. This is particularly interesting in situations where little
data with no attribute annotation is available for learning, but we have access
to a large external corpus of heavily annotated samples. While prior works
primarily augment in the space of images, we propose to perform augmentation in
feature space instead. We implement our approach as a deep encoder-decoder
architecture that learns the synthesis function in an end-to-end manner. We
demonstrate the utility of our approach on the problems of (1) one-shot object
recognition in a transfer-learning setting where we have no prior knowledge of
the new classes, as well as (2) object-based one-shot scene recognition. As
external data, we leverage 3D depth and pose information from the SUN RGB-D
dataset. Our experiments show that attribute-guided augmentation of high-level
CNN features considerably improves one-shot recognition performance on both
problems.
Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity
Authors: Feiyun Zhu, Ying Wang, Bin Fan, Gaofeng Meng, Chunhong Pan
Date: 2014-09-02 00:00:00
Tasks: Hyperspectral Unmixing

Hyperspectral unmixing (HU) plays a fundamental role in a wide range of
hyperspectral applications. It is still challenging due to the common presence
of outlier channels and the large solution space. To address the above two
issues, we propose a novel model by emphasizing both robust representation and
learning-based sparsity. Specifically, we apply the $\ell_{2,1}$-norm to
measure the representation error, preventing outlier channels from dominating
our objective. In this way, the side effects of outlier channels are greatly
relieved. Besides, we observe that the mixed level of each pixel varies over
image grids. Based on this observation, we exploit a learning-based sparsity
method to simultaneously learn the HU results and a sparse guidance map. Via
this guidance map, the sparsity constraint in the $\ell_{p}\!\left(\!0\!<\!
p\!\leq\!1\right)$-norm is adaptively imposed according to the learnt mixed
level of each pixel. Compared with state-of-the-art methods, our model is
better suited to the real situation, thus expected to achieve better HU
results. The resulted objective is highly non-convex and non-smooth, and so it
is hard to optimize. As a profound theoretical contribution, we propose an
efficient algorithm to solve it. Meanwhile, the convergence proof and the
computational complexity analysis are systematically provided. Extensive
evaluations verify that our method is highly promising for the HU task---it
achieves very accurate guidance maps and much better HU results compared with
state-of-the-art methods.
Overcoming Language Variation in Sentiment Analysis with Social Attention
Authors: Yi Yang, Jacob Eisenstein
Date: 2015-11-19 00:00:00
Proceeding: TACL 2017 1
Tasks: Sentiment Analysis

Variation in language is ubiquitous, particularly in newer forms of writing
such as social media. Fortunately, variation is not random, it is often linked
to social properties of the author. In this paper, we show how to exploit
social networks to make sentiment analysis more robust to social language
variation. The key idea is linguistic homophily: the tendency of socially
linked individuals to use language in similar ways. We formalize this idea in a
novel attention-based neural network architecture, in which attention is
divided among several basis models, depending on the author's position in the
social network. This has the effect of smoothing the classification function
across the social network, and makes it possible to induce personalized
classifiers even for authors for whom there is no labeled data or demographic
metadata. This model significantly improves the accuracies of sentiment
analysis on Twitter and on review data.
Synthesising Wider Field Images from Narrow-Field Retinal Video Acquired Using a Low-Cost Direct Ophthalmoscope (Arclight) Attached to a Smartphone
Authors: Keylor Daniel Chaves Viquez, Ognjen Arandjelovic, Andrew Blaikie, In Ae Hwang
Date: 2017-08-26 00:00:00

Access to low cost retinal imaging devices in low and middle income countries
is limited, compromising progress in preventing needless blindness. The
Arclight is a recently developed low-cost solar powered direct ophthalmoscope
which can be attached to the camera of a smartphone to acquire retinal images
and video. However, the acquired data is inherently limited by the optics of
direct ophthalmoscopy, resulting in a narrow field of view with associated
corneal reflections, limiting its usefulness. In this work we describe the
first fully automatic method utilizing videos acquired using the Arclight
attached to a mobile phone camera to create wider view, higher quality still
images comparable with images obtained using much more expensive and bulky
dedicated traditional retinal cameras.
Fast Low-Rank Bayesian Matrix Completion with Hierarchical Gaussian Prior Models
Authors: Linxiao Yang, Jun Fang, Huiping Duan, Hongbin Li, Bing Zeng
Date: 2017-08-08 00:00:00
Tasks: Bayesian Inference, Low-Rank Matrix Completion, Matrix Completion

The problem of low rank matrix completion is considered in this paper. To
exploit the underlying low-rank structure of the data matrix, we propose a
hierarchical Gaussian prior model, where columns of the low-rank matrix are
assumed to follow a Gaussian distribution with zero mean and a common precision
matrix, and a Wishart distribution is specified as a hyperprior over the
precision matrix. We show that such a hierarchical Gaussian prior has the
potential to encourage a low-rank solution. Based on the proposed hierarchical
prior model, a variational Bayesian method is developed for matrix completion,
where the generalized approximate massage passing (GAMP) technique is embedded
into the variational Bayesian inference in order to circumvent cumbersome
matrix inverse operations. Simulation results show that our proposed method
demonstrates superiority over existing state-of-the-art matrix completion
methods.
Plausible Deniability for Privacy-Preserving Data Synthesis
Authors: Vincent Bindschaedler, Reza Shokri, Carl A. Gunter
Date: 2017-08-26 00:00:00
Tasks: De-identification, Privacy Preserving

Releasing full data records is one of the most challenging problems in data
privacy. On the one hand, many of the popular techniques such as data
de-identification are problematic because of their dependence on the background
knowledge of adversaries. On the other hand, rigorous methods such as the
exponential mechanism for differential privacy are often computationally
impractical to use for releasing high dimensional data or cannot preserve high
utility of original data due to their extensive data perturbation.
  This paper presents a criterion called plausible deniability that provides a
formal privacy guarantee, notably for releasing sensitive datasets: an output
record can be released only if a certain amount of input records are
indistinguishable, up to a privacy parameter. This notion does not depend on
the background knowledge of an adversary. Also, it can efficiently be checked
by privacy tests. We present mechanisms to generate synthetic datasets with
similar statistical properties to the input data and the same format. We study
this technique both theoretically and experimentally. A key theoretical result
shows that, with proper randomization, the plausible deniability mechanism
generates differentially private synthetic data. We demonstrate the efficiency
of this generative technique on a large dataset; it is shown to preserve the
utility of original data with respect to various statistical analysis and
machine learning measures.
Maximum A Posteriori Estimation of Distances Between Deep Features in Still-to-Video Face Recognition
Authors: Andrey V. Savchenko, Natalya S. Belova
Date: 2017-08-26 00:00:00
Tasks: Face Recognition, Video Recognition

The paper deals with the still-to-video face recognition for the small sample
size problem based on computation of distances between high-dimensional deep
bottleneck features. We present the novel statistical recognition method, in
which the still-to-video recognition task is casted into Maximum A Posteriori
estimation. In this method we maximize the joint probabilistic density of the
distances to all reference still images. It is shown that this likelihood can
be estimated with the known asymptotically normal distribution of the
Kullback-Leibler discriminations between nonnegative features. The experimental
study with the LFW (Labeled Faces in the Wild), YTF (YouTube Faces) and IJB-A
(IARPA Janus Benchmark A) datasets has been provided. We demonstrated, that the
proposed approach can be applied with the state-of-the-art deep features and
dissimilarity measures. Our algorithm achieves 3-5% higher accuracy when
compared with conventional aggregation of decisions obtained for all frames.
3D Object Reconstruction from a Single Depth View with Adversarial Learning
Authors: Bo Yang, Hongkai Wen, Sen Wang, Ronald Clark, Andrew Markham, Niki Trigoni
Date: 2017-08-26 00:00:00
Tasks: 3D geometry, 3D Object Reconstruction, Object, Object Reconstruction

In this paper, we propose a novel 3D-RecGAN approach, which reconstructs the
complete 3D structure of a given object from a single arbitrary depth view
using generative adversarial networks. Unlike the existing work which typically
requires multiple views of the same object or class labels to recover the full
3D geometry, the proposed 3D-RecGAN only takes the voxel grid representation of
a depth view of the object as input, and is able to generate the complete 3D
occupancy grid by filling in the occluded/missing regions. The key idea is to
combine the generative capabilities of autoencoders and the conditional
Generative Adversarial Networks (GAN) framework, to infer accurate and
fine-grained 3D structures of objects in high-dimensional voxel space.
Extensive experiments on large synthetic datasets show that the proposed
3D-RecGAN significantly outperforms the state of the art in single view 3D
object reconstruction, and is able to reconstruct unseen types of objects. Our
code and data are available at: https://github.com/Yang7879/3D-RecGAN.
Faster Clustering via Non-Backtracking Random Walks
Authors: Brian Rappaport, Anuththari Gamage, Shuchin Aeron
Date: 2017-08-26 00:00:00
Tasks: Clustering, Graph Clustering

This paper presents VEC-NBT, a variation on the unsupervised graph clustering
technique VEC, which improves upon the performance of the original algorithm
significantly for sparse graphs. VEC employs a novel application of the
state-of-the-art word2vec model to embed a graph in Euclidean space via random
walks on the nodes of the graph. In VEC-NBT, we modify the original algorithm
to use a non-backtracking random walk instead of the normal backtracking random
walk used in VEC. We introduce a modification to a non-backtracking random
walk, which we call a begrudgingly-backtracking random walk, and show
empirically that using this model of random walks for VEC-NBT requires shorter
walks on the graph to obtain results with comparable or greater accuracy than
VEC, especially for sparser graphs.
Segmentation of retinal cysts from Optical Coherence Tomography volumes via selective enhancement
Authors: Karthik Gopinath, Jayanthi Sivaswamy
Date: 2017-08-21 00:00:00
Tasks: Clustering, Segmentation

Automated and accurate segmentation of cystoid structures in Optical
Coherence Tomography (OCT) is of interest in the early detection of retinal
diseases. It is, however, a challenging task. We propose a novel method for
localizing cysts in 3D OCT volumes. The proposed work is biologically inspired
and based on selective enhancement of the cysts, by inducing motion to a given
OCT slice. A Convolutional Neural Network (CNN) is designed to learn a mapping
function that combines the result of multiple such motions to produce a
probability map for cyst locations in a given slice. The final segmentation of
cysts is obtained via simple clustering of the detected cyst locations. The
proposed method is evaluated on two public datasets and one private dataset.
The public datasets include the one released for the OPTIMA Cyst segmentation
challenge (OCSC) in MICCAI 2015 and the DME dataset. After training on the OCSC
train set, the method achieves a mean Dice Coefficient (DC) of 0.71 on the OCSC
test set. The robustness of the algorithm was examined by cross-validation on
the DME and AEI (private) datasets and a mean DC values obtained were 0.69 and
0.79, respectively. Overall, the proposed system outperforms all benchmarks.
These results underscore the strengths of the proposed method in handling
variations in both data acquisition protocols and scanners.
Of the People: Voting Is More Effective with Representative Candidates
Authors: Yu Cheng, Shaddin Dughmi, David Kempe
Date: 2017-05-04 00:00:00

In light of the classic impossibility results of Arrow and Gibbard and
Satterthwaite regarding voting with ordinal rules, there has been recent
interest in characterizing how well common voting rules approximate the social
optimum. In order to quantify the quality of approximation, it is natural to
consider the candidates and voters as embedded within a common metric space,
and to ask how much further the chosen candidate is from the population as
compared to the socially optimal one. We use this metric preference model to
explore a fundamental and timely question: does the social welfare of a
population improve when candidates are representative of the population? If so,
then by how much, and how does the answer depend on the complexity of the
metric space?
  We restrict attention to the most fundamental and common social choice
setting: a population of voters, two independently drawn candidates, and a
majority rule election. When candidates are not representative of the
population, it is known that the candidate selected by the majority rule can be
thrice as far from the population as the socially optimal one. We examine how
this ratio improves when candidates are drawn independently from the population
of voters. Our results are two-fold: When the metric is a line, the ratio
improves from $3$ to $4-2\sqrt{2}$, roughly $1.1716$; this bound is tight. When
the metric is arbitrary, we show a lower bound of $1.5$ and a constant upper
bound strictly better than $2$ on the approximation ratio of the majority rule.
  The positive result depends in part on the assumption that candidates are
independent and identically distributed. However, we show that independence
alone is not enough to achieve the upper bound: even when candidates are drawn
independently, if the population of candidates can be different from the
voters, then an upper bound of $2$ on the approximation is tight.
Distributed Bundle Adjustment
Authors: Karthikeyan Natesan Ramamurthy, Chung-Ching Lin, Aleksandr Aravkin, Sharath Pankanti, Raphael Viguier
Date: 2017-08-26 00:00:00
Tasks: 3D Reconstruction

Most methods for Bundle Adjustment (BA) in computer vision are either
centralized or operate incrementally. This leads to poor scaling and affects
the quality of solution as the number of images grows in large scale structure
from motion (SfM). Furthermore, they cannot be used in scenarios where image
acquisition and processing must be distributed. We address this problem with a
new distributed BA algorithm. Our distributed formulation uses alternating
direction method of multipliers (ADMM), and, since each processor sees only a
small portion of the data, we show that robust formulations improve
performance. We analyze convergence of the proposed algorithm, and illustrate
numerical performance, accuracy of the parameter estimates, and scalability of
the distributed implementation in the context of synthetic 3D datasets with
known camera position and orientation ground truth. The results are comparable
to an alternate state-of-the-art centralized bundle adjustment algorithm on
synthetic and real 3D reconstruction problems. The runtime of our
implementation scales linearly with the number of observed points.
Sales Forecast in E-commerce using Convolutional Neural Network
Authors: Kui Zhao, Can Wang
Date: 2017-08-26 00:00:00
Tasks: Feature Engineering

Sales forecast is an essential task in E-commerce and has a crucial impact on
making informed business decisions. It can help us to manage the workforce,
cash flow and resources such as optimizing the supply chain of manufacturers
etc. Sales forecast is a challenging problem in that sales is affected by many
factors including promotion activities, price changes, and user preferences
etc. Traditional sales forecast techniques mainly rely on historical sales data
to predict future sales and their accuracies are limited. Some more recent
learning-based methods capture more information in the model to improve the
forecast accuracy. However, these methods require case-by-case manual feature
engineering for specific commercial scenarios, which is usually a difficult,
time-consuming task and requires expert knowledge. To overcome the limitations
of existing methods, we propose a novel approach in this paper to learn
effective features automatically from the structured data using the
Convolutional Neural Network (CNN). When fed with raw log data, our approach
can automatically extract effective features from that and then forecast sales
using those extracted features. We test our method on a large real-world
dataset from CaiNiao.com and the experimental results validate the
effectiveness of our method.
m-TSNE: A Framework for Visualizing High-Dimensional Multivariate Time Series
Authors: Minh Nguyen, Sanjay Purushotham, Hien To, Cyrus Shahabi
Date: 2017-08-26 00:00:00
Tasks: Time Series, Time Series Analysis, Vocal Bursts Intensity Prediction

Multivariate time series (MTS) have become increasingly common in healthcare
domains where human vital signs and laboratory results are collected for
predictive diagnosis. Recently, there have been increasing efforts to visualize
healthcare MTS data based on star charts or parallel coordinates. However, such
techniques might not be ideal for visualizing a large MTS dataset, since it is
difficult to obtain insights or interpretations due to the inherent high
dimensionality of MTS. In this paper, we propose 'm-TSNE': a simple and novel
framework to visualize high-dimensional MTS data by projecting them into a
low-dimensional (2-D or 3-D) space while capturing the underlying data
properties. Our framework is easy to use and provides interpretable insights
for healthcare professionals to understand MTS data. We evaluate our
visualization framework on two real-world datasets and demonstrate that the
results of our m-TSNE show patterns that are easy to understand while the other
methods' visualization may have limitations in interpretability.
Navigation Objects Extraction for Better Content Structure Understanding
Authors: Kui Zhao, Bangpeng Li, Zilun Peng, Jiajun Bu, Can Wang
Date: 2017-08-26 00:00:00

Existing works for extracting navigation objects from webpages focus on
navigation menus, so as to reveal the information architecture of the site.
However, web 2.0 sites such as social networks, e-commerce portals etc. are
making the understanding of the content structure in a web site increasingly
difficult. Dynamic and personalized elements such as top stories, recommended
list in a webpage are vital to the understanding of the dynamic nature of web
2.0 sites. To better understand the content structure in web 2.0 sites, in this
paper we propose a new extraction method for navigation objects in a webpage.
Our method will extract not only the static navigation menus, but also the
dynamic and personalized page-specific navigation lists. Since the navigation
objects in a webpage naturally come in blocks, we first cluster hyperlinks into
different blocks by exploiting spatial locations of hyperlinks, the
hierarchical structure of the DOM-tree and the hyperlink density. Then we
identify navigation objects from those blocks using the SVM classifier with
novel features such as anchor text lengths etc. Experiments on real-world data
sets with webpages from various domains and styles verified the effectiveness
of our method.