pwc-archive/papers-with-abstracts huggingface dataset:
Poisoning Attacks to Graph-Based Recommender Systems
Authors: Minghong Fang, Guolei Yang, Neil Zhenqiang Gong, Jia Liu
Date: 2018-09-11 00:00:00
Tasks: Recommendation Systems

Recommender system is an important component of many web services to help
users locate items that match their interests. Several studies showed that
recommender systems are vulnerable to poisoning attacks, in which an attacker
injects fake data to a given system such that the system makes recommendations
as the attacker desires. However, these poisoning attacks are either agnostic
to recommendation algorithms or optimized to recommender systems that are not
graph-based. Like association-rule-based and matrix-factorization-based
recommender systems, graph-based recommender system is also deployed in
practice, e.g., eBay, Huawei App Store. However, how to design optimized
poisoning attacks for graph-based recommender systems is still an open problem.
In this work, we perform a systematic study on poisoning attacks to graph-based
recommender systems. Due to limited resources and to avoid detection, we assume
the number of fake users that can be injected into the system is bounded. The
key challenge is how to assign rating scores to the fake users such that the
target item is recommended to as many normal users as possible. To address the
challenge, we formulate the poisoning attacks as an optimization problem,
solving which determines the rating scores for the fake users. We also propose
techniques to solve the optimization problem. We evaluate our attacks and
compare them with existing attacks under white-box (recommendation algorithm
and its parameters are known), gray-box (recommendation algorithm is known but
its parameters are unknown), and black-box (recommendation algorithm is
unknown) settings using two real-world datasets. Our results show that our
attack is effective and outperforms existing attacks for graph-based
recommender systems. For instance, when 1% fake users are injected, our attack
can make a target item recommended to 580 times more normal users in certain
scenarios.
Cartesian Neural Network Constitutive Models for Data-driven Elasticity Imaging
Authors: Cameron Hoerig, Jamshid Ghaboussi, Michael F. Insana
Date: 2018-09-11 00:00:00

Elasticity images map biomechanical properties of soft tissues to aid in the
detection and diagnosis of pathological states. In particular, quasi-static
ultrasonic (US) elastography techniques use force-displacement measurements
acquired during an US scan to parameterize the spatio-temporal stress-strain
behavior. Current methods use a model-based inverse approach to estimate the
parameters associated with a chosen constitutive model. However, model-based
methods rely on simplifying assumptions of tissue biomechanical properties,
often limiting elastography to imaging one or two linear-elastic parameters.
  We previously described a data-driven method for building neural network
constitutive models (NNCMs) that learn stress-strain relationships from
force-displacement data. Using measurements acquired on gelatin phantoms, we
demonstrated the ability of NNCMs to characterize linear-elastic mechanical
properties without an initial model assumption and thus circumvent the
mathematical constraints typically encountered in classic model-based
approaches to the inverse problem. While successful, we were required to use a
priori knowledge of the internal object shape to define the spatial
distribution of regions exhibiting different material properties.
  Here, we introduce Cartesian neural network constitutive models (CaNNCMs)
that are capable of using data to model both linear-elastic mechanical
properties and their distribution in space. We demonstrate the ability of
CaNNCMs to capture arbitrary material property distributions using
stress-strain data from simulated phantoms. Furthermore, we show that a trained
CaNNCM can be used to reconstruct a Young's modulus image. CaNNCMs are an
important step toward data-driven modeling and imaging the complex mechanical
properties of soft tissues.
Humans can decipher adversarial images
Authors: Zhenglong Zhou, Chaz Firestone
Date: 2018-09-11 00:00:00
Tasks: Autonomous Vehicles, Classification, General Classification, Object Categorization

How similar is the human mind to the sophisticated machine-learning systems
that mirror its performance? Models of object categorization based on
convolutional neural networks (CNNs) have achieved human-level benchmarks in
assigning known labels to novel images. These advances promise to support
transformative technologies such as autonomous vehicles and machine diagnosis;
beyond this, they also serve as candidate models for the visual system itself
-- not only in their output but perhaps even in their underlying mechanisms and
principles. However, unlike human vision, CNNs can be "fooled" by adversarial
examples -- carefully crafted images that appear as nonsense patterns to humans
but are recognized as familiar objects by machines, or that appear as one
object to humans and a different object to machines. This seemingly extreme
divergence between human and machine classification challenges the promise of
these new advances, both as applied image-recognition systems and also as
models of the human mind. Surprisingly, however, little work has empirically
investigated human classification of such adversarial stimuli: Does human and
machine performance fundamentally diverge? Or could humans decipher such images
and predict the machine's preferred labels? Here, we show that human and
machine classification of adversarial stimuli are robustly related: In eight
experiments on five prominent and diverse adversarial imagesets, human subjects
reliably identified the machine's chosen label over relevant foils. This
pattern persisted for images with strong antecedent identities, and even for
images described as "totally unrecognizable to human eyes". We suggest that
human intuition may be a more reliable guide to machine (mis)classification
than has typically been imagined, and we explore the consequences of this
result for minds and machines alike.
Detecting egregious responses in neural sequence-to-sequence models
Authors: Tianxing He, James Glass
Date: 2018-09-11 00:00:00
Proceeding: ICLR 2019 5
Tasks: Response Generation

In this work, we attempt to answer a critical question: whether there exists
some input sequence that will cause a well-trained discrete-space neural
network sequence-to-sequence (seq2seq) model to generate egregious outputs
(aggressive, malicious, attacking, etc.). And if such inputs exist, how to find
them efficiently. We adopt an empirical methodology, in which we first create
lists of egregious output sequences, and then design a discrete optimization
algorithm to find input sequences that will cause the model to generate them.
Moreover, the optimization algorithm is enhanced for large vocabulary search
and constrained to search for input sequences that are likely to be input by
real-world users. In our experiments, we apply this approach to dialogue
response generation models trained on three real-world dialogue data-sets:
Ubuntu, Switchboard and OpenSubtitles, testing whether the model can generate
malicious responses. We demonstrate that given the trigger inputs our algorithm
finds, a significant number of malicious sentences are assigned large
probability by the model, which reveals an undesirable consequence of standard
seq2seq training.
Joint Embedding of Meta-Path and Meta-Graph for Heterogeneous Information Networks
Authors: Lichao Sun, Lifang He, Zhipeng Huang, Bokai Cao, Congying Xia, Xiaokai Wei, Philip S. Yu
Date: 2018-09-11 00:00:00
Tasks: Network Embedding, Tensor Decomposition

Meta-graph is currently the most powerful tool for similarity search on
heterogeneous information networks,where a meta-graph is a composition of
meta-paths that captures the complex structural information. However, current
relevance computing based on meta-graph only considers the complex structural
information, but ignores its embedded meta-paths information. To address this
problem, we proposeMEta-GrAph-based network embedding models, called MEGA and
MEGA++, respectively. The MEGA model uses normalized relevance or similarity
measures that are derived from a meta-graph and its embedded meta-paths between
nodes simultaneously, and then leverages tensor decomposition method to perform
node embedding. The MEGA++ further facilitates the use of coupled tensor-matrix
decomposition method to obtain a joint embedding for nodes, which
simultaneously considers the hidden relations of all meta information of a
meta-graph.Extensive experiments on two real datasets demonstrate thatMEGA and
MEGA++ are more effective than state-of-the-art approaches.
ACM RecSys 2018 Late-Breaking Results Proceedings
Authors: Christoph Trattner, Vanessa Murdock, Steven Chang
Date: 2018-09-11 00:00:00
Tasks: Recommendation Systems

The ACM RecSys'18 Late-Breaking Results track (previously known as the Poster
track) is part of the main program of the 2018 ACM Conference on Recommender
Systems in Vancouver, Canada. The track attracted 48 submissions this year out
of which 18 papers could be accepted resulting in an acceptance rated of 37.5%.
On the Structural Sensitivity of Deep Convolutional Networks to the Directions of Fourier Basis Functions
Authors: Yusuke Tsuzuku, Issei Sato
Date: 2018-09-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Sensitivity

Data-agnostic quasi-imperceptible perturbations on inputs are known to
degrade recognition accuracy of deep convolutional networks severely. This
phenomenon is considered to be a potential security issue. Moreover, some
results on statistical generalization guarantees indicate that the phenomenon
can be a key to improve the networks' generalization. However, the
characteristics of the shared directions of such harmful perturbations remain
unknown. Our primal finding is that convolutional networks are sensitive to the
directions of Fourier basis functions. We derived the property by specializing
a hypothesis of the cause of the sensitivity, known as the linearity of neural
networks, to convolutional networks and empirically validated it. As a
by-product of the analysis, we propose an algorithm to create shift-invariant
universal adversarial perturbations available in black-box settings.
Parallel Separable 3D Convolution for Video and Volumetric Data Understanding
Authors: Felix Gonda, Donglai Wei, Toufiq Parag, Hanspeter Pfister
Date: 2018-09-11 00:00:00
Tasks: Action Recognition, Brain Segmentation, Temporal Action Localization, Tensor Decomposition

For video and volumetric data understanding, 3D convolution layers are widely
used in deep learning, however, at the cost of increasing computation and
training time. Recent works seek to replace the 3D convolution layer with
convolution blocks, e.g. structured combinations of 2D and 1D convolution
layers. In this paper, we propose a novel convolution block, Parallel Separable
3D Convolution (PmSCn), which applies m parallel streams of n 2D and one 1D
convolution layers along different dimensions. We first mathematically justify
the need of parallel streams (Pm) to replace a single 3D convolution layer
through tensor decomposition. Then we jointly replace consecutive 3D
convolution layers, common in modern network architectures, with the multiple
2D convolution layers (Cn). Lastly, we empirically show that PmSCn is
applicable to different backbone architectures, such as ResNet, DenseNet, and
UNet, for different applications, such as video action recognition, MRI brain
segmentation, and electron microscopy segmentation. In all three applications,
we replace the 3D convolution layers in state-of-the art models with PmSCn and
achieve around 14% improvement in test performance and 40% reduction in model
size and on average.
FIVR: Fine-grained Incident Video Retrieval
Authors: Giorgos Kordopatis-Zilos, Symeon Papadopoulos, Ioannis Patras, Ioannis Kompatsiaris
Date: 2018-09-11 00:00:00
Tasks: Benchmarking, Retrieval, Video Retrieval

This paper introduces the problem of Fine-grained Incident Video Retrieval
(FIVR). Given a query video, the objective is to retrieve all associated
videos, considering several types of associations that range from duplicate
videos to videos from the same incident. FIVR offers a single framework that
contains several retrieval tasks as special cases. To address the benchmarking
needs of all such tasks, we construct and present a large-scale annotated video
dataset, which we call FIVR-200K, and it comprises 225,960 videos. To create
the dataset, we devise a process for the collection of YouTube videos based on
major news events from recent years crawled from Wikipedia and deploy a
retrieval pipeline for the automatic selection of query videos based on their
estimated suitability as benchmarks. We also devise a protocol for the
annotation of the dataset with respect to the four types of video associations
defined by FIVR. Finally, we report the results of an experimental study on the
dataset comparing five state-of-the-art methods developed based on a variety of
visual descriptors, highlighting the challenges of the current problem.
Quantum Algorithms for Structured Prediction
Authors: Behrooz Sepehry, Ehsan Iranmanesh, Michael P. Friedlander, Pooya Ronagh
Date: 2018-09-11 00:00:00
Tasks: BIG-bench Machine Learning, Prediction, Quantum Machine Learning, Structured Prediction

We introduce two quantum algorithms for solving structured prediction problems. We first show that a stochastic gradient descent that uses the quantum minimum finding algorithm and takes its probabilistic failure into account solves the structured prediction problem with a runtime that scales with the square root of the size of the label space, and in $\widetilde O\left(1/\epsilon\right)$ with respect to the precision, $\epsilon$, of the solution. Motivated by robust inference techniques in machine learning, we then introduce another quantum algorithm that solves a smooth approximation of the structured prediction problem with a similar quantum speedup in the size of the label space and a similar scaling in the precision parameter. In doing so, we analyze a variant of stochastic gradient descent for convex optimization in the presence of an additive error in the calculation of the gradients, and show that its convergence rate does not deteriorate if the additive errors are of the order $O(\sqrt\epsilon)$. This algorithm uses quantum Gibbs sampling at temperature $\Omega (\epsilon)$ as a subroutine. Based on these theoretical observations, we propose a method for using quantum Gibbs samplers to combine feedforward neural networks with probabilistic graphical models for quantum machine learning. Our numerical results using Monte Carlo simulations on an image tagging task demonstrate the benefit of the approach.
DeepProteomics: Protein family classification using Shallow and Deep Networks
Authors: Anu Vazhayil, Vinayakumar R, Soman Kp
Date: 2018-09-11 00:00:00
Tasks: Classification, General Classification

The knowledge regarding the function of proteins is necessary as it gives a
clear picture of biological processes. Nevertheless, there are many protein
sequences found and added to the databases but lacks functional annotation. The
laboratory experiments take a considerable amount of time for annotation of the
sequences. This arises the need to use computational techniques to classify
proteins based on their functions. In our work, we have collected the data from
Swiss-Prot containing 40433 proteins which is grouped into 30 families. We pass
it to recurrent neural network(RNN), long short term memory(LSTM) and gated
recurrent unit(GRU) model and compare it by applying trigram with deep neural
network and shallow neural network on the same dataset. Through this approach,
we could achieve maximum of around 78% accuracy for the classification of
protein families.
Can a Compact Neuronal Circuit Policy be Re-purposed to Learn Simple Robotic Control?
Authors: Ramin Hasani, Mathias Lechner, Alexander Amini, Daniela Rus, Radu Grosu
Date: 2018-09-11 00:00:00

We propose a neural information processing system which is obtained by re-purposing the function of a biological neural circuit model, to govern simulated and real-world control tasks. Inspired by the structure of the nervous system of the soil-worm, C. elegans, we introduce Neuronal Circuit Policies (NCPs), defined as the model of biological neural circuits reparameterized for the control of an alternative task. We learn instances of NCPs to control a series of robotic tasks, including the autonomous parking of a real-world rover robot. For reconfiguration of the purpose of the neural circuit, we adopt a search-based optimization algorithm. Neuronal circuit policies perform on par and in some cases surpass the performance of contemporary deep learning models with the advantage leveraging significantly fewer learnable parameters and realizing interpretable dynamics at the cell-level.
Solving Sinhala Language Arithmetic Problems using Neural Networks
Authors: W. M. T. Chathurika, K. C. E De Silva, A. M. Raddella, E. M. R. S. Ekanayake, A. Nugaliyadde, Y. Mallawarachchi
Date: 2018-09-11 00:00:00
Tasks: General Classification

A methodology is presented to solve Arithmetic problems in Sinhala Language
using a Neural Network. The system comprises of (a) keyword identification, (b)
question identification, (c) mathematical operation identification and is
combined using a neural network. Naive Bayes Classification is used in order to
identify keywords and Conditional Random Field to identify the question and the
operation which should be performed on the identified keywords to achieve the
expected result. "One vs. all Classification" is done using a neural network
for sentences. All functions are combined through the neural network which
builds an equation to solve the problem. The paper compares each methodology in
ARIS and Mahoshadha to the method presented in the paper. Mahoshadha2 learns to
solve arithmetic problems with the accuracy of 76%.
Structured and Unstructured Outlier Identification for Robust PCA: A Non iterative, Parameter free Algorithm
Authors: Vishnu Menon, Sheetal Kalyani
Date: 2018-09-11 00:00:00

Robust PCA, the problem of PCA in the presence of outliers has been
extensively investigated in the last few years. Here we focus on Robust PCA in
the outlier model where each column of the data matrix is either an inlier or
an outlier. Most of the existing methods for this model assumes either the
knowledge of the dimension of the lower dimensional subspace or the fraction of
outliers in the system. However in many applications knowledge of these
parameters is not available. Motivated by this we propose a parameter free
outlier identification method for robust PCA which a) does not require the
knowledge of outlier fraction, b) does not require the knowledge of the
dimension of the underlying subspace, c) is computationally simple and fast d)
can handle structured and unstructured outliers. Further, analytical guarantees
are derived for outlier identification and the performance of the algorithm is
compared with the existing state of the art methods in both real and synthetic
data for various outlier structures.
The Visual QA Devil in the Details: The Impact of Early Fusion and Batch Norm on CLEVR
Authors: Mateusz Malinowski, Carl Doersch
Date: 2018-09-11 00:00:00
Tasks: Question Answering, Relational Reasoning

Visual QA is a pivotal challenge for higher-level reasoning, requiring
understanding language, vision, and relationships between many objects in a
scene. Although datasets like CLEVR are designed to be unsolvable without such
complex relational reasoning, some surprisingly simple feed-forward, "holistic"
models have recently shown strong performance on this dataset. These models
lack any kind of explicit iterative, symbolic reasoning procedure, which are
hypothesized to be necessary for counting objects, narrowing down the set of
relevant objects based on several attributes, etc. The reason for this strong
performance is poorly understood. Hence, our work analyzes such models, and
finds that minor architectural elements are crucial to performance. In
particular, we find that \textit{early fusion} of language and vision provides
large performance improvements. This contrasts with the late fusion approaches
popular at the dawn of Visual QA. We propose a simple module we call Multimodal
Core, which we hypothesize performs the fundamental operations for multimodal
tasks. We believe that understanding why these elements are so important to
complex question answering will aid the design of better-performing algorithms
for Visual QA while minimizing hand-engineering effort.
Isolated and Ensemble Audio Preprocessing Methods for Detecting Adversarial Examples against Automatic Speech Recognition
Authors: Krishan Rajaratnam, Kunal Shah, Jugal Kalita
Date: 2018-09-11 00:00:00
Proceeding: ROCLINGIJCLCLP 2018 10
Tasks: Adversarial Attack, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

An adversarial attack is an exploitative process in which minute alterations
are made to natural inputs, causing the inputs to be misclassified by neural
models. In the field of speech recognition, this has become an issue of
increasing significance. Although adversarial attacks were originally
introduced in computer vision, they have since infiltrated the realm of speech
recognition. In 2017, a genetic attack was shown to be quite potent against the
Speech Commands Model. Limited-vocabulary speech classifiers, such as the
Speech Commands Model, are used in a variety of applications, particularly in
telephony; as such, adversarial examples produced by this attack pose as a
major security threat. This paper explores various methods of detecting these
adversarial examples with combinations of audio preprocessing. One particular
combined defense incorporating compressions, speech coding, filtering, and
audio panning was shown to be quite effective against the attack on the Speech
Commands Model, detecting audio adversarial examples with 93.5% precision and
91.2% recall.
An empirical learning-based validation procedure for simulation workflow
Authors: Zhuqing Liu, Liyuanjun Lai, Lin Zhang
Date: 2018-09-11 00:00:00

Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation workflow is of the most importance in a simulation system. However, the methods especially for validating simulation workflow is very limit. Many of the existing validation techniques are domain-dependent with cumbersome questionnaire design and expert scoring. Therefore, this paper present an empirical learning-based validation procedure to implement a semi-automated evaluation for simulation workflow. First, representative features of general simulation workflow and their relations with validation indices are proposed. The calculation process of workflow credibility based on Analytic Hierarchy Process (AHP) is then introduced. In order to make full use of the historical data and implement more efficient validation, four learning algorithms, including back propagation neural network (BPNN), extreme learning machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture model (FIGMN), are introduced for constructing the empirical relation between the workflow credibility and its features. A case study on a landing-process simulation workflow is established to test the feasibility of the proposed procedure. The experimental results also provide some useful overview of the state-of-the-art learning algorithms on the credibility evaluation of simulation models.