pwc-archive/papers-with-abstracts huggingface dataset:
Max-Information, Differential Privacy, and Post-Selection Hypothesis Testing
Authors: Ryan Rogers, Aaron Roth, Adam Smith, Om Thakkar
Date: 2016-04-13 00:00:00
Tasks: Two-sample testing, valid

In this paper, we initiate a principled study of how the generalization
properties of approximate differential privacy can be used to perform adaptive
hypothesis testing, while giving statistically valid $p$-value corrections. We
do this by observing that the guarantees of algorithms with bounded approximate
max-information are sufficient to correct the $p$-values of adaptively chosen
hypotheses, and then by proving that algorithms that satisfy
$(\epsilon,\delta)$-differential privacy have bounded approximate max
information when their inputs are drawn from a product distribution. This
substantially extends the known connection between differential privacy and
max-information, which previously was only known to hold for (pure)
$(\epsilon,0)$-differential privacy. It also extends our understanding of
max-information as a partially unifying measure controlling the generalization
properties of adaptive data analyses. We also show a lower bound, proving that
(despite the strong composition properties of max-information), when data is
drawn from a product distribution, $(\epsilon,\delta)$-differentially private
algorithms can come first in a composition with other algorithms satisfying
max-information bounds, but not necessarily second if the composition is
required to itself satisfy a nontrivial max-information bound. This, in
particular, implies that the connection between
$(\epsilon,\delta)$-differential privacy and max-information holds only for
inputs drawn from product distributions, unlike the connection between
$(\epsilon,0)$-differential privacy and max-information.
Learning Covariant Feature Detectors
Authors: Karel Lenc, Andrea Vedaldi
Date: 2016-05-04 00:00:00
Tasks: Translation

Local covariant feature detection, namely the problem of extracting viewpoint
invariant features from images, has so far largely resisted the application of
machine learning techniques. In this paper, we propose the first fully general
formulation for learning local covariant feature detectors. We propose to cast
detection as a regression problem, enabling the use of powerful regressors such
as deep neural networks. We then derive a covariance constraint that can be
used to automatically learn which visual structures provide stable anchors for
local feature detection. We support these ideas theoretically, proposing a
novel analysis of local features in term of geometric transformations, and we
show that all common and many uncommon detectors can be derived in this
framework. Finally, we present empirical results on translation and rotation
covariant detectors on standard feature benchmarks, showing the power and
flexibility of the framework.
Harassment detection: a benchmark on the #HackHarassment dataset
Authors: Alexei Bastidas, Edward Dixon, Chris Loo, John Ryan
Date: 2016-09-09 00:00:00
Tasks: BIG-bench Machine Learning, text-classification, Text Classification

Online harassment has been a problem to a greater or lesser extent since the
early days of the internet. Previous work has applied anti-spam techniques like
machine-learning based text classification (Reynolds, 2011) to detecting
harassing messages. However, existing public datasets are limited in size, with
labels of varying quality. The #HackHarassment initiative (an alliance of 1
tech companies and NGOs devoted to fighting bullying on the internet) has begun
to address this issue by creating a new dataset superior to its predecssors in
terms of both size and quality. As we (#HackHarassment) complete further rounds
of labelling, later iterations of this dataset will increase the available
samples by at least an order of magnitude, enabling corresponding improvements
in the quality of machine learning models for harassment detection. In this
paper, we introduce the first models built on the #HackHarassment dataset v1.0
(a new open dataset, which we are delighted to share with any interested
researcherss) as a benchmark for future research.
Automated detection of smuggled high-risk security threats using Deep Learning
Authors: Nicolas Jaccard, Thomas W. Rogers, Edward J. Morton, Lewis D. Griffin
Date: 2016-09-09 00:00:00
Tasks: Deep Learning, Vocal Bursts Intensity Prediction

The security infrastructure is ill-equipped to detect and deter the smuggling
of non-explosive devices that enable terror attacks such as those recently
perpetrated in western Europe. The detection of so-called "small metallic
threats" (SMTs) in cargo containers currently relies on statistical risk
analysis, intelligence reports, and visual inspection of X-ray images by
security officers. The latter is very slow and unreliable due to the difficulty
of the task: objects potentially spanning less than 50 pixels have to be
detected in images containing more than 2 million pixels against very complex
and cluttered backgrounds. In this contribution, we demonstrate for the first
time the use of Convolutional Neural Networks (CNNs), a type of Deep Learning,
to automate the detection of SMTs in fullsize X-ray images of cargo containers.
Novel approaches for dataset augmentation allowed to train CNNs from-scratch
despite the scarcity of data available. We report fewer than 6% false alarms
when detecting 90% SMTs synthetically concealed in stream-of-commerce images,
which corresponds to an improvement of over an order of magnitude over
conventional approaches such as Bag-of-Words (BoWs). The proposed scheme offers
potentially super-human performance for a fraction of the time it would take
for a security officers to carry out visual inspection (processing time is
approximately 3.5s per container image).
An empirical study on the effects of different types of noise in image classification tasks
Authors: Gabriel B. Paranhos da Costa, Welinton A. Contato, Tiago S. Nazare, João E. S. Batista Neto, Moacir Ponti
Date: 2016-09-09 00:00:00
Tasks: Classification, Denoising, General Classification, image-classification, Image Classification

Image classification is one of the main research problems in computer vision
and machine learning. Since in most real-world image classification
applications there is no control over how the images are captured, it is
necessary to consider the possibility that these images might be affected by
noise (e.g. sensor noise in a low-quality surveillance camera). In this paper
we analyse the impact of three different types of noise on descriptors
extracted by two widely used feature extraction methods (LBP and HOG) and how
denoising the images can help to mitigate this problem. We carry out
experiments on two different datasets and consider several types of noise,
noise levels, and denoising methods. Our results show that noise can hinder
classification performance considerably and make classes harder to separate.
Although denoising methods were not able to reach the same performance of the
noise-free scenario, they improved classification results for noisy data.
Detection of concealed cars in complex cargo X-ray imagery using Deep Learning
Authors: Nicolas Jaccard, Thomas W. Rogers, Edward J. Morton, Lewis D. Griffin
Date: 2016-06-26 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection

Non-intrusive inspection systems based on X-ray radiography techniques are
routinely used at transport hubs to ensure the conformity of cargo content with
the supplied shipping manifest. As trade volumes increase and regulations
become more stringent, manual inspection by trained operators is less and less
viable due to low throughput. Machine vision techniques can assist operators in
their task by automating parts of the inspection workflow. Since cars are
routinely involved in trafficking, export fraud, and tax evasion schemes, they
represent an attractive target for automated detection and flagging for
subsequent inspection by operators. In this contribution, we describe a method
for the detection of cars in X-ray cargo images based on trained-from-scratch
Convolutional Neural Networks. By introducing an oversampling scheme that
suitably addresses the low number of car images available for training, we
achieved 100% car image classification rate for a false positive rate of
1-in-454. Cars that were partially or completely obscured by other goods, a
modus operandi frequently adopted by criminals, were correctly detected. We
believe that this level of performance suggests that the method is suitable for
deployment in the field. It is expected that the generic object detection
workflow described can be extended to other object classes given the
availability of suitable training data.
INSIGHT-1 at SemEval-2016 Task 4: Convolutional Neural Networks for Sentiment Classification and Quantification
Authors: Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Date: 2016-09-09 00:00:00
Proceeding: SEMEVAL 2016 6
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

This paper describes our deep learning-based approach to sentiment analysis
in Twitter as part of SemEval-2016 Task 4. We use a convolutional neural
network to determine sentiment and participate in all subtasks, i.e. two-point,
three-point, and five-point scale sentiment classification and two-point and
five-point scale sentiment quantification. We achieve competitive results for
two-point scale sentiment classification and quantification, ranking fifth and
a close fourth (third and second by alternative metrics) respectively despite
using only pre-trained embeddings that contain no sentiment information. We
achieve good performance on three-point scale sentiment classification, ranking
eighth out of 35, while performing poorly on five-point scale sentiment
classification and quantification. An error analysis reveals that this is due
to low expressiveness of the model to capture negative sentiment as well as an
inability to take into account ordinal information. We propose improvements in
order to address these and other issues.
A Hierarchical Model of Reviews for Aspect-based Sentiment Analysis
Authors: Sebastian Ruder, Parsa Ghaffari, John G. Breslin
Date: 2016-09-09 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), General Classification, Opinion Mining, Sentence, Sentiment Analysis

Opinion mining from customer reviews has become pervasive in recent years.
Sentences in reviews, however, are usually classified independently, even
though they form part of a review's argumentative structure. Intuitively,
sentences in a review build and elaborate upon each other; knowledge of the
review structure and sentential context should thus inform the classification
of each sentence. We demonstrate this hypothesis for the task of aspect-based
sentiment analysis by modeling the interdependencies of sentences in a review
with a hierarchical bidirectional LSTM. We show that the hierarchical model
outperforms two non-hierarchical baselines, obtains results competitive with
the state-of-the-art, and outperforms the state-of-the-art on five
multilingual, multi-domain datasets without any hand-engineered features or
external resources.
An Interactive Segmentation Tool for Quantifying Fat in Lumbar Muscles using Axial Lumbar-Spine MRI
Authors: Joseph Antony, Kevin McGuinness, Neil Welch, Joe Coyle, Andy Franklyn-Miller, Noel E. O'Connor, Kieran Moran
Date: 2016-09-09 00:00:00
Tasks: Interactive Segmentation, Variable Selection

In this paper we present an interactive tool that can be used to quantify fat
infiltration in lumbar muscles, which is useful in studying fat infiltration
and lower back pain (LBP) in adults. Currently, a qualitative assessment by
visual grading via a 5-point scale is used to study fat infiltration in lumbar
muscles from an axial view of lumbar-spine MR Images. However, a quantitative
approach (on a continuous scale of 0-100\%) may provide a greater insight. In
this paper, we propose a method to precisely quantify the fat deposition /
infiltration in a user-defined region of the lumbar muscles, which may aid
better diagnosis and analysis. The key steps are interactively segmenting the
region of interest (ROI) from the lumbar muscles using the well known livewire
technique, identifying fatty regions in the segmented region based on
variable-selection of threshold and softness levels, automatically detecting
the center of the spinal column and fragmenting the lumbar muscles into smaller
regions with reference to the center of the spinal column, computing key
parameters [such as total and region-wise fat content percentage, total-cross
sectional area (TCSA) and functional cross-sectional area (FCSA)] and exporting
the computations and associated patient information from the MRI, into a
database. A standalone application using MATLAB R2014a was developed to perform
the required computations along with an intuitive graphical user interface
(GUI).
Predicting the future relevance of research institutions - The winning solution of the KDD Cup 2016
Authors: Vlad Sandulescu, Mihai Chiru
Date: 2016-09-09 00:00:00

The world's collective knowledge is evolving through research and new
scientific discoveries. It is becoming increasingly difficult to objectively
rank the impact research institutes have on global advancements. However, since
the funding, governmental support, staff and students quality all mirror the
projected quality of the institution, it becomes essential to measure the
affiliation's rating in a transparent and widely accepted way. We propose and
investigate several methods to rank affiliations based on the number of their
accepted papers at future academic conferences. We carry out our investigation
using publicly available datasets such as the Microsoft Academic Graph, a
heterogeneous graph which contains various information about academic papers.
We analyze several models, starting with a simple probabilities-based method
and then gradually expand our training dataset, engineer many more features and
use mixed models and gradient boosted decision trees models to improve our
predictions.
Detecting Singleton Review Spammers Using Semantic Similarity
Authors: Vlad Sandulescu, Martin Ester
Date: 2016-09-09 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Online reviews have increasingly become a very important resource for
consumers when making purchases. Though it is becoming more and more difficult
for people to make well-informed buying decisions without being deceived by
fake reviews. Prior works on the opinion spam problem mostly considered
classifying fake reviews using behavioral user patterns. They focused on
prolific users who write more than a couple of reviews, discarding one-time
reviewers. The number of singleton reviewers however is expected to be high for
many review websites. While behavioral patterns are effective when dealing with
elite users, for one-time reviewers, the review text needs to be exploited. In
this paper we tackle the problem of detecting fake reviews written by the same
person using multiple names, posting each review under a different name. We
propose two methods to detect similar reviews and show the results generally
outperform the vectorial similarity measures used in prior works. The first
method extends the semantic similarity between words to the reviews level. The
second method is based on topic modeling and exploits the similarity of the
reviews topic distributions using two models: bag-of-words and
bag-of-opinion-phrases. The experiments were conducted on reviews from three
different datasets: Yelp (57K reviews), Trustpilot (9K reviews) and Ott dataset
(800 reviews).
Automatic Selection of Stochastic Watershed Hierarchies
Authors: Amin Fehri, Santiago Velasco-Forero, Fernand Meyer
Date: 2016-09-09 00:00:00
Tasks: Segmentation

The segmentation, seen as the association of a partition with an image, is a
difficult task. It can be decomposed in two steps: at first, a family of
contours associated with a series of nested partitions (or hierarchy) is
created and organized, then pertinent contours are extracted. A coarser
partition is obtained by merging adjacent regions of a finer partition. The
strength of a contour is then measured by the level of the hierarchy for which
its two adjacent regions merge. We present an automatic segmentation strategy
using a wide range of stochastic watershed hierarchies. For a given set of
homogeneous images, our approach selects automatically the best hierarchy and
cut level to perform image simplification given an evaluation score.
Experimental results illustrate the advantages of our approach on several
real-life images datasets.
Efficient batch-sequential Bayesian optimization with moments of truncated Gaussian vectors
Authors: Sébastien Marmin, Clément Chevalier, David Ginsbourger
Date: 2016-09-09 00:00:00
Tasks: Bayesian Optimization, Form, global-optimization

We deal with the efficient parallelization of Bayesian global optimization
algorithms, and more specifically of those based on the expected improvement
criterion and its variants. A closed form formula relying on multivariate
Gaussian cumulative distribution functions is established for a generalized
version of the multipoint expected improvement criterion. In turn, the latter
relies on intermediate results that could be of independent interest concerning
moments of truncated Gaussian vectors. The obtained expansion of the criterion
enables studying its differentiability with respect to point batches and
calculating the corresponding gradient in closed form. Furthermore , we derive
fast numerical approximations of this gradient and propose efficient batch
optimization strategies. Numerical experiments illustrate that the proposed
approaches enable computational savings of between one and two order of
magnitudes, hence enabling derivative-based batch-sequential acquisition
function maximization to become a practically implementable and efficient
standard.
Identifying Topology of Power Distribution Networks Based on Smart Meter Data
Authors: Jayadev P Satya, Nirav Bhatt, Ramkrishna Pasumarthy, Aravind Rajeswaran
Date: 2016-09-09 00:00:00
Tasks: Time Series, Time Series Analysis

In a power distribution network, the network topology information is
essential for an efficient operation of the network. This information of
network connectivity is not accurately available, at the low voltage level, due
to uninformed changes that happen from time to time. In this paper, we propose
a novel data--driven approach to identify the underlying network topology
including the load phase connectivity from time series of energy measurements.
The proposed method involves the application of Principal Component Analysis
(PCA) and its graph-theoretic interpretation to infer the topology from smart
meter energy measurements. The method is demonstrated through simulation on
randomly generated networks and also on IEEE recognized Roy Billinton
distribution test system.
Measuring Player's Behaviour Change over Time in Public Goods Game
Authors: Polla Fattah, Uwe Aickelin, Christian Wagner
Date: 2016-09-09 00:00:00
Tasks: Clustering

An important issue in public goods game is whether player's behaviour changes
over time, and if so, how significant it is. In this game players can be
classified into different groups according to the level of their participation
in the public good. This problem can be considered as a concept drift problem
by asking the amount of change that happens to the clusters of players over a
sequence of game rounds. In this study we present a method for measuring
changes in clusters with the same items over discrete time points using
external clustering validation indices and area under the curve. External
clustering indices were originally used to measure the difference between
suggested clusters in terms of clustering algorithms and ground truth labels
for items provided by experts. Instead of different cluster label comparison,
we use these indices to compare between clusters of any two consecutive time
points or between the first time point and the remaining time points to measure
the difference between clusters through time points. In theory, any external
clustering indices can be used to measure changes for any traditional
(non-temporal) clustering algorithm, due to the fact that any time point alone
is not carrying any temporal information. For the public goods game, our
results indicate that the players are changing over time but the change is
smooth and relatively constant between any two time points.
Machine Learning with Guarantees using Descriptive Complexity and SMT Solvers
Authors: Charles Jordan, Łukasz Kaiser
Date: 2016-09-09 00:00:00
Tasks: BIG-bench Machine Learning, Board Games, Descriptive, Learning Theory

Machine learning is a thriving part of computer science. There are many
efficient approaches to machine learning that do not provide strong theoretical
guarantees, and a beautiful general learning theory. Unfortunately, machine
learning approaches that give strong theoretical guarantees have not been
efficient enough to be applicable. In this paper we introduce a logical
approach to machine learning. Models are represented by tuples of logical
formulas and inputs and outputs are logical structures. We present our
framework together with several applications where we evaluate it using SAT and
SMT solvers. We argue that this approach to machine learning is particularly
suited to bridge the gap between efficiency and theoretical soundness. We
exploit results from descriptive complexity theory to prove strong theoretical
guarantees for our approach. To show its applicability, we present experimental
results including learning complexity-theoretic reductions rules for board
games. We also explain how neural networks fit into our framework, although the
current implementation does not scale to provide guarantees for real-world
neural networks.
Singularity structures and impacts on parameter estimation in finite mixtures of distributions
Authors: Nhat Ho, XuanLong Nguyen
Date: 2016-09-09 00:00:00
Tasks: parameter estimation

Singularities of a statistical model are the elements of the model's parameter space which make the corresponding Fisher information matrix degenerate. These are the points for which estimation techniques such as the maximum likelihood estimator and standard Bayesian procedures do not admit the root-$n$ parametric rate of convergence. We propose a general framework for the identification of singularity structures of the parameter space of finite mixtures, and study the impacts of the singularity structures on minimax lower bounds and rates of convergence for the maximum likelihood estimator over a compact parameter space. Our study makes explicit the deep links between model singularities, parameter estimation convergence rates and minimax lower bounds, and the algebraic geometry of the parameter space for mixtures of continuous distributions. The theory is applied to establish concrete convergence rates of parameter estimation for finite mixture of skew-normal distributions. This rich and increasingly popular mixture model is shown to exhibit a remarkably complex range of asymptotic behaviors which have not been hitherto reported in the literature.