pwc-archive/papers-with-abstracts huggingface dataset:
The Many Faces of Link Fraud
Authors: Neil Shah, Hemank Lamba, Alex Beutel, Christos Faloutsos
Date: 2017-04-05 00:00:00
Tasks: Fraud Detection

Most past work on social network link fraud detection tries to separate
genuine users from fraudsters, implicitly assuming that there is only one type
of fraudulent behavior. But is this assumption true? And, in either case, what
are the characteristics of such fraudulent behaviors? In this work, we set up
honeypots ("dummy" social network accounts), and buy fake followers (after
careful IRB approval). We report the signs of such behaviors including oddities
in local network connectivity, account attributes, and similarities and
differences across fraud providers. Most valuably, we discover and characterize
several types of fraud behaviors. We discuss how to leverage our insights in
practice by engineering strongly performing entropy-based features and
demonstrating high classification accuracy. Our contributions are (a)
instrumentation: we detail our experimental setup and carefully engineered data
collection process to scrape Twitter data while respecting API rate-limits, (b)
observations on fraud multimodality: we analyze our honeypot fraudster
ecosystem and give surprising insights into the multifaceted behaviors of these
fraudster types, and (c) features: we propose novel features that give strong
(>0.95 precision/recall) discriminative power on ground-truth Twitter data.
A Planning Approach to Monitoring Behavior of Computer Programs
Authors: Alexandre Cukier, Ronen I. Brafman, Yotam Perkal, David Tolpin
Date: 2017-09-11 00:00:00

We describe a novel approach to monitoring high level behaviors using
concepts from AI planning. Our goal is to understand what a program is doing
based on its system call trace. This ability is particularly important for
detecting malware. We approach this problem by building an abstract model of
the operating system using the STRIPS planning language, casting system calls
as planning operators. Given a system call trace, we simulate the corresponding
operators on our model and by observing the properties of the state reached, we
learn about the nature of the original program and its behavior. Thus, unlike
most statistical detection methods that focus on syntactic features, our
approach is semantic in nature. Therefore, it is more robust against
obfuscation techniques used by malware that change the outward appearance of
the trace but not its effect. We demonstrate the efficacy of our approach by
evaluating it on actual system call traces.
Justifications in Constraint Handling Rules for Logical Retraction in Dynamic Algorithms
Authors: Thom Fruehwirth
Date: 2017-06-24 00:00:00

We present a straightforward source-to-source transformation that introduces
justifications for user-defined constraints into the CHR programming language.
Then a scheme of two rules suffices to allow for logical retraction (deletion,
removal) of constraints during computation. Without the need to recompute from
scratch, these rules remove not only the constraint but also undo all
consequences of the rule applications that involved the constraint. We prove a
confluence result concerning the rule scheme and show its correctness. When
algorithms are written in CHR, constraints represent both data and operations.
CHR is already incremental by nature, i.e. constraints can be added at runtime.
Logical retraction adds decrementality. Hence any algorithm written in CHR with
justifications will become fully dynamic. Operations can be undone and data can
be removed at any point in the computation without compromising the correctness
of the result. We present two classical examples of dynamic algorithms, written
in our prototype implementation of CHR with justifications that is available
online: maintaining the minimum of a changing set of numbers and shortest paths
in a graph whose edges change.
Picasso: A Modular Framework for Visualizing the Learning Process of Neural Network Image Classifiers
Authors: Ryan Henderson, Rasmus Rothe
Date: 2017-05-16 00:00:00
Tasks: Deep Learning, General Classification

Picasso is a free open-source (Eclipse Public License) web application
written in Python for rendering standard visualizations useful for analyzing
convolutional neural networks. Picasso ships with occlusion maps and saliency
maps, two visualizations which help reveal issues that evaluation metrics like
loss and accuracy might hide: for example, learning a proxy classification
task. Picasso works with the Tensorflow deep learning framework, and Keras
(when the model can be loaded into the Tensorflow backend). Picasso can be used
with minimal configuration by deep learning researchers and engineers alike
across various neural network architectures. Adding new visualizations is
simple: the user can specify their visualization code and HTML template
separately from the application code.
Deep learning: Technical introduction
Authors: Thomas Epelbaum
Date: 2017-09-05 00:00:00
Tasks: Deep Learning

This note presents in a technical though hopefully pedagogical way the three
most common forms of neural network architectures: Feedforward, Convolutional
and Recurrent. For each network, their fundamental building blocks are
detailed. The forward pass and the update rules for the backpropagation
algorithm are then derived in full.
weedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming
Authors: Inkyu Sa, Zetao Chen, Marija Popovic, Raghav Khanna, Frank Liebisch, Juan Nieto, Roland Siegwart
Date: 2017-09-11 00:00:00
Tasks: Decoder, General Classification, GPU, Management

Selective weed treatment is a critical step in autonomous crop management as
related to crop health and yield. However, a key challenge is reliable, and
accurate weed detection to minimize damage to surrounding plants. In this
paper, we present an approach for dense semantic weed classification with
multispectral images collected by a micro aerial vehicle (MAV). We use the
recently developed encoder-decoder cascaded Convolutional Neural Network (CNN),
Segnet, that infers dense semantic classes while allowing any number of input
image channels and class balancing with our sugar beet and weed datasets. To
obtain training datasets, we established an experimental field with varying
herbicide levels resulting in field plots containing only either crop or weed,
enabling us to use the Normalized Difference Vegetation Index (NDVI) as a
distinguishable feature for automatic ground truth generation. We train 6
models with different numbers of input channels and condition (fine-tune) it to
achieve about 0.8 F1-score and 0.78 Area Under the Curve (AUC) classification
metrics. For model deployment, an embedded GPU system (Jetson TX2) is tested
for MAV integration. Dataset used in this paper is released to support the
community and future work.
Discriminant chronicles mining: Application to care pathways analytics
Authors: Yann Dauxais, Thomas Guyet, David Gross-Amblard, André Happe
Date: 2017-09-11 00:00:00
Tasks: Epidemiology

Pharmaco-epidemiology (PE) is the study of uses and effects of drugs in well
defined populations. As medico-administrative databases cover a large part of
the population, they have become very interesting to carry PE studies. Such
databases provide longitudinal care pathways in real condition containing
timestamped care events, especially drug deliveries. Temporal pattern mining
becomes a strategic choice to gain valuable insights about drug uses. In this
paper we propose DCM, a new discriminant temporal pattern mining algorithm. It
extracts chronicle patterns that occur more in a studied population than in a
control population. We present results on the identification of possible
associations between hospitalizations for seizure and anti-epileptic drug
switches in care pathway of epileptic patients.
Sequential Dirichlet Process Mixtures of Multivariate Skew t-distributions for Model-based Clustering of Flow Cytometry Data
Authors: Boris P. Hejblum, Chariff Alkhassim, Raphael Gottardo, François Caron, Rodolphe Thiébaut
Date: 2017-02-14 00:00:00
Tasks: Clustering, Model Selection

Flow cytometry is a high-throughput technology used to quantify multiple
surface and intracellular markers at the level of a single cell. This enables
to identify cell sub-types, and to determine their relative proportions.
Improvements of this technology allow to describe millions of individual cells
from a blood sample using multiple markers. This results in high-dimensional
datasets, whose manual analysis is highly time-consuming and poorly
reproducible. While several methods have been developed to perform automatic
recognition of cell populations, most of them treat and analyze each sample
independently. However, in practice, individual samples are rarely independent
(e.g. longitudinal studies). Here, we propose to use a Bayesian nonparametric
approach with Dirichlet process mixture (DPM) of multivariate skew
$t$-distributions to perform model based clustering of flow-cytometry data. DPM
models directly estimate the number of cell populations from the data, avoiding
model selection issues, and skew $t$-distributions provides robustness to
outliers and non-elliptical shape of cell populations. To accommodate repeated
measurements, we propose a sequential strategy relying on a parametric
approximation of the posterior. We illustrate the good performance of our
method on simulated data, on an experimental benchmark dataset, and on new
longitudinal data from the DALIA-1 trial which evaluates a therapeutic vaccine
against HIV. On the benchmark dataset, the sequential strategy outperforms all
other methods evaluated, and similarly, leads to improved performance on the
DALIA-1 data. We have made the method available for the community in the R
package NPflow.
Cellular Automaton Based Simulation of Large Pedestrian Facilities - A Case Study on the Staten Island Ferry Terminals
Authors: Luca Crociani, Gregor Lämmel, H. Joon Park, Giuseppe Vizzari
Date: 2017-09-11 00:00:00
Tasks: valid

Current metropolises largely depend on a functioning transport infrastructure
and the increasing demand can only be satisfied by a well organized mass
transit. One example for a crucial mass transit system is New York City's
Staten Island Ferry, connecting the two boroughs of Staten Island and Manhattan
with a regular passenger service. Today's demand already exceeds 2500
passengers for a single cycle during peek hours, and future projections suggest
that it will further increase. One way to appraise how the system will cope
with future demand is by simulation. This contribution proposes an integrated
simulation approach to evaluate the system performance with respect to future
demand. The simulation relies on a multiscale modeling approach where the
terminal buildings are simulated by a microscopic and quantitatively valid
cellular automata (CA) and the journeys of the ferries themselves are modeled
by a mesoscopic queue simulation approach. Based on the simulation results
recommendations with respect to the future demand are given.
Exploring Temporal Preservation Networks for Precise Temporal Action Localization
Authors: Ke Yang, Peng Qiao, Dongsheng Li, Shaohe Lv, Yong Dou
Date: 2017-08-10 00:00:00
Tasks: Action Localization, Open-Ended Question Answering, Temporal Action Localization, Temporal Localization

Temporal action localization is an important task of computer vision. Though
a variety of methods have been proposed, it still remains an open question how
to predict the temporal boundaries of action segments precisely. Most works use
segment-level classifiers to select video segments pre-determined by action
proposal or dense sliding windows. However, in order to achieve more precise
action boundaries, a temporal localization system should make dense predictions
at a fine granularity. A newly proposed work exploits
Convolutional-Deconvolutional-Convolutional (CDC) filters to upsample the
predictions of 3D ConvNets, making it possible to perform per-frame action
predictions and achieving promising performance in terms of temporal action
localization. However, CDC network loses temporal information partially due to
the temporal downsampling operation. In this paper, we propose an elegant and
powerful Temporal Preservation Convolutional (TPC) Network that equips 3D
ConvNets with TPC filters. TPC network can fully preserve temporal resolution
and downsample the spatial resolution simultaneously, enabling frame-level
granularity action localization. TPC network can be trained in an end-to-end
manner. Experiment results on public datasets show that TPC network achieves
significant improvement on per-frame action prediction and competing results on
segment-level temporal action localization.
Segmentation and Classification of Cine-MR Images Using Fully Convolutional Networks and Handcrafted Features
Authors: M. Hossein Eybposh, Mohammad Haghir Ebrahim-Abadi, Mohammad Jalilpour-Monesi, Seyed Saman Saboksayr
Date: 2017-09-08 00:00:00
Tasks: Anatomy, Classification, Diagnostic, feature selection, General Classification, Segmentation

Three-dimensional cine-MRI is of crucial importance for assessing the cardiac
function. Features that describe the anatomy and function of cardiac structures
(e.g. Left Ventricle (LV), Right Ventricle (RV), and Myocardium(MC)) are known
to have significant diagnostic value and can be computed from 3D cine-MR
images. However, these features require precise segmentation of cardiac
structures. Among the fully automated segmentation methods, Fully Convolutional
Networks (FCN) with Skip Connections have shown robustness in medical
segmentation problems. In this study, we develop a complete pipeline for
classification of subjects with cardiac conditions based on 3D cine-MRI. For
the segmentation task, we develop a 2D FCN and introduce Parallel Paths (PP) as
a way to exploit the 3D information of the cine-MR image. For the
classification task, 125 features were extracted from the segmented structures,
describing their anatomy and function. Next, a two-stage pipeline for feature
selection using the LASSO method is developed. A subset of 20 features is
selected for classification. Each subject is classified using an ensemble of
Logistic Regression, Multi-Layer Perceptron, and Support Vector Machine
classifiers through majority voting. The Dice Coefficient for segmentation was
0.95+-0.03, 0.89+-0.13, and 0.90+-0.03 for LV, RV, and MC respectively. The
8-fold cross validation accuracy for the classification task was 95.05% and
92.77% based on ground truth and the proposed methods segmentations
respectively. The results show that the PPs increase the segmentation accuracy,
by exploiting the spatial relations. Moreover, the classification algorithm and
the features showed discriminability while keeping the sensitivity to
segmentation error as low as possible.
Support Spinor Machine
Authors: Kabin Kanjamapornkul, Richard Pinčák, Sanphet Chunithpaisan, Erik Bartoš
Date: 2017-09-11 00:00:00
Tasks: General Classification, One-Class Classification, Time Series, Time Series Analysis

We generalize a support vector machine to a support spinor machine by using
the mathematical structure of wedge product over vector machine in order to
extend field from vector field to spinor field. The separated hyperplane is
extended to Kolmogorov space in time series data which allow us to extend a
structure of support vector machine to a support tensor machine and a support
tensor machine moduli space. Our performance test on support spinor machine is
done over one class classification of end point in physiology state of time
series data after empirical mode analysis and compared with support vector
machine test. We implement algorithm of support spinor machine by using
Holo-Hilbert amplitude modulation for fully nonlinear and nonstationary time
series data analysis.
Expert Opinion Extraction from a Biomedical Database
Authors: Ahmed Samet, Thomas Guyet, Benjamin Negrevergne, Tien-Tuan Dao, Tuan Nha Hoang, Marie-Christine Ho Ba Tho
Date: 2017-09-11 00:00:00
Tasks: Opinion Mining

In this paper, we tackle the problem of extracting frequent opinions from
uncertain databases. We introduce the foundation of an opinion mining approach
with the definition of pattern and support measure. The support measure is
derived from the commitment definition. A new algorithm called OpMiner that
extracts the set of frequent opinions modelled as a mass functions is detailed.
Finally, we apply our approach on a real-world biomedical database that stores
opinions of experts to evaluate the reliability level of biomedical data.
Performance analysis showed a better quality patterns for our proposed model in
comparison with literature-based methods.
Mining relevant interval rules
Authors: Thomas Guyet, René Quiniou, Véronique Masson
Date: 2017-09-11 00:00:00

This article extends the method of Garriga et al. for mining relevant rules
to numerical attributes by extracting interval-based pattern rules. We propose
an algorithm that extracts such rules from numerical datasets using the
interval-pattern approach from Kaytoue et al. This algorithm has been
implemented and evaluated on real datasets.
Evolution of Convolutional Highway Networks
Authors: Oliver Kramer
Date: 2017-09-11 00:00:00

Convolutional highways are deep networks based on multiple stacked
convolutional layers for feature preprocessing. We introduce an evolutionary
algorithm (EA) for optimization of the structure and hyperparameters of
convolutional highways and demonstrate the potential of this optimization
setting on the well-known MNIST data set. The (1+1)-EA employs Rechenberg's
mutation rate control and a niching mechanism to overcome local optima adapts
the optimization approach. An experimental study shows that the EA is capable
of improving the state-of-the-art network contribution and of evolving highway
networks from scratch.
Privacy Loss in Apple's Implementation of Differential Privacy on MacOS 10.12
Authors: Jun Tang, Aleksandra Korolova, Xiaolong Bai, Xueqiang Wang, Xiao-Feng Wang
Date: 2017-09-08 00:00:00
Tasks: Management

In June 2016, Apple announced that it will deploy differential privacy for
some user data collection in order to ensure privacy of user data, even from
Apple. The details of Apple's approach remained sparse. Although several
patents have since appeared hinting at the algorithms that may be used to
achieve differential privacy, they did not include a precise explanation of the
approach taken to privacy parameter choice. Such choice and the overall
approach to privacy budget use and management are key questions for
understanding the privacy protections provided by any deployment of
differential privacy.
  In this work, through a combination of experiments, static and dynamic code
analysis of macOS Sierra (Version 10.12) implementation, we shed light on the
choices Apple made for privacy budget management. We discover and describe
Apple's set-up for differentially private data processing, including the
overall data pipeline, the parameters used for differentially private
perturbation of each piece of data, and the frequency with which such data is
sent to Apple's servers.
  We find that although Apple's deployment ensures that the (differential)
privacy loss per each datum submitted to its servers is $1$ or $2$, the overall
privacy loss permitted by the system is significantly higher, as high as $16$
per day for the four initially announced applications of Emojis, New words,
Deeplinks and Lookup Hints. Furthermore, Apple renews the privacy budget
available every day, which leads to a possible privacy loss of 16 times the
number of days since user opt-in to differentially private data collection for
those four applications.
  We advocate that in order to claim the full benefits of differentially
private data collection, Apple must give full transparency of its
implementation, enable user choice in areas related to privacy loss, and set
meaningful defaults on the privacy loss permitted.
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent
Authors: Xiangru Lian, Ce Zhang, huan zhang, Cho-Jui Hsieh, Wei zhang, Ji Liu
Date: 2017-05-25 00:00:00
Proceeding: NeurIPS 2017 12

Most distributed machine learning systems nowadays, including TensorFlow and
CNTK, are built in a centralized fashion. One bottleneck of centralized
algorithms lies on high communication cost on the central node. Motivated by
this, we ask, can decentralized algorithms be faster than its centralized
counterpart?
  Although decentralized PSGD (D-PSGD) algorithms have been studied by the
control community, existing analysis and theory do not show any advantage over
centralized PSGD (C-PSGD) algorithms, simply assuming the application scenario
where only the decentralized network is available. In this paper, we study a
D-PSGD algorithm and provide the first theoretical analysis that indicates a
regime in which decentralized algorithms might outperform centralized
algorithms for distributed stochastic gradient descent. This is because D-PSGD
has comparable total computational complexities to C-PSGD but requires much
less communication cost on the busiest node. We further conduct an empirical
study to validate our theoretical analysis across multiple frameworks (CNTK and
Torch), different network configurations, and computation platforms up to 112
GPUs. On network configurations with low bandwidth or high latency, D-PSGD can
be up to one order of magnitude faster than its well-optimized centralized
counterparts.
Fairness Testing: Testing Software for Discrimination
Authors: Sainyam Galhotra, Yuriy Brun, Alexandra Meliou
Date: 2017-09-11 00:00:00
Tasks: Fairness, valid

This paper defines software fairness and discrimination and develops a
testing-based method for measuring if and how much software discriminates,
focusing on causality in discriminatory behavior. Evidence of software
discrimination has been found in modern software systems that recommend
criminal sentences, grant access to financial products, and determine who is
allowed to participate in promotions. Our approach, Themis, generates efficient
test suites to measure discrimination. Given a schema describing valid system
inputs, Themis generates discrimination tests automatically and does not
require an oracle. We evaluate Themis on 20 software systems, 12 of which come
from prior work with explicit focus on avoiding discrimination. We find that
(1) Themis is effective at discovering software discrimination, (2)
state-of-the-art techniques for removing discrimination from algorithms fail in
many situations, at times discriminating against as much as 98% of an input
subdomain, (3) Themis optimizations are effective at producing efficient test
suites for measuring discrimination, and (4) Themis is more efficient on
systems that exhibit more discrimination. We thus demonstrate that fairness
testing is a critical aspect of the software development cycle in domains with
possible discrimination and provide initial tools for measuring software
discrimination.