pwc-archive/papers-with-abstracts huggingface dataset:
Attack Graph Obfuscation
Authors: Rami Puzis, Hadar Polad, Bracha Shapira
Date: 2019-03-06 00:00:00
Tasks: Combinatorial Optimization

Before executing an attack, adversaries usually explore the victim's network
in an attempt to infer the network topology and identify vulnerabilities in the
victim's servers and personal computers. Falsifying the information collected
by the adversary post penetration may significantly slower lateral movement and
increase the amount of noise generated within the victim's network. We
investigate the effect of fake vulnerabilities within a real enterprise network
on the attacker performance. We use the attack graphs to model the path of an
attacker making its way towards a target in a given network. We use
combinatorial optimization in order to find the optimal assignments of fake
vulnerabilities. We demonstrate the feasibility of our deception-based defense
by presenting results of experiments with a large scale real network. We show
that adding fake vulnerabilities forces the adversary to invest a significant
amount of effort, in terms of time and exploitability cost.
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
Authors: Wenhan Xiong, Jiawei Wu, Deren Lei, Mo Yu, Shiyu Chang, Xiaoxiao Guo, William Yang Wang
Date: 2019-03-06 00:00:00
Proceeding: NAACL 2019 6
Tasks: Entity Typing, Inductive Bias

Existing entity typing systems usually exploit the type hierarchy provided by
knowledge base (KB) schema to model label correlations and thus improve the
overall performance. Such techniques, however, are not directly applicable to
more open and practical scenarios where the type set is not restricted by KB
schema and includes a vast number of free-form types. To model the underly-ing
label correlations without access to manually annotated label structures, we
introduce a novel label-relational inductive bias, represented by a graph
propagation layer that effectively encodes both global label co-occurrence
statistics and word-level similarities.On a large dataset with over 10,000
free-form types, the graph-enhanced model equipped with an attention-based
matching module is able to achieve a much higher recall score while maintaining
a high-level precision. Specifically, it achieves a 15.3% relative F1
improvement and also less inconsistency in the outputs. We further show that a
simple modification of our proposed graph layer can also improve the
performance on a conventional and widely-tested dataset that only includes
KB-schema types.
Sentence Embedding Alignment for Lifelong Relation Extraction
Authors: Hong Wang, Wenhan Xiong, Mo Yu, Xiaoxiao Guo, Shiyu Chang, William Yang Wang
Date: 2019-03-06 00:00:00
Proceeding: NAACL 2019 6
Tasks: Incremental Learning, Lifelong learning, Relation, Relation Extraction, Sentence, Sentence Embedding, Sentence-Embedding

Conventional approaches to relation extraction usually require a fixed set of
pre-defined relations. Such requirement is hard to meet in many real
applications, especially when new data and relations are emerging incessantly
and it is computationally expensive to store all data and re-train the whole
model every time new data and relations come in. We formulate such a
challenging problem as lifelong relation extraction and investigate
memory-efficient incremental learning methods without catastrophically
forgetting knowledge learned from previous tasks. We first investigate a
modified version of the stochastic gradient methods with a replay memory, which
surprisingly outperforms recent state-of-the-art lifelong learning methods. We
further propose to improve this approach to alleviate the forgetting problem by
anchoring the sentence embedding space. Specifically, we utilize an explicit
alignment model to mitigate the sentence embedding distortion of the learned
model when training on new data and new relations. Experiment results on
multiple benchmarks show that our proposed method significantly outperforms the
state-of-the-art lifelong learning approaches.
GanDef: A GAN based Adversarial Training Defense for Neural Network Classifier
Authors: Guanxiong Liu, Issa Khalil, Abdallah Khreishah
Date: 2019-03-06 00:00:00
Tasks: feature selection, Overall - Test

Machine learning models, especially neural network (NN) classifiers, are
widely used in many applications including natural language processing,
computer vision and cybersecurity. They provide high accuracy under the
assumption of attack-free scenarios. However, this assumption has been defied
by the introduction of adversarial examples -- carefully perturbed samples of
input that are usually misclassified. Many researchers have tried to develop a
defense against adversarial examples; however, we are still far from achieving
that goal. In this paper, we design a Generative Adversarial Net (GAN) based
adversarial training defense, dubbed GanDef, which utilizes a competition game
to regulate the feature selection during the training. We analytically show
that GanDef can train a classifier so it can defend against adversarial
examples. Through extensive evaluation on different white-box adversarial
examples, the classifier trained by GanDef shows the same level of test
accuracy as those trained by state-of-the-art adversarial training defenses.
More importantly, GanDef-Comb, a variant of GanDef, could utilize the
discriminator to achieve a dynamic trade-off between correctly classifying
original and adversarial examples. As a result, it achieves the highest overall
test accuracy when the ratio of adversarial examples exceeds 41.7%.
Clear Skies Ahead: Towards Real-Time Automatic Sky Replacement in Video
Authors: Tavi Halperin, Harel Cain, Ofir Bibi, Michael Werman
Date: 2019-03-06 00:00:00

Digital videos such as those captured by a smartphone often exhibit exposure
inconsistencies, a poorly exposed sky, or simply suffer from an uninteresting
or plain looking sky. Professionals may edit these videos using advanced and
time-consuming tools unavailable to most users, to replace the sky with a more
expressive or imaginative sky. In this work, we propose an algorithm for
automatic replacement of the sky region in a video with a different sky,
providing nonprofessional users with a simple yet efficient tool to seamlessly
replace the sky. The method is fast, achieving close to real-time performance
on mobile devices and the user's involvement can remain as limited as simply
selecting the replacement sky.
Temporal Registration in Application to In-utero MRI Time Series
Authors: Ruizhi Liao, Esra A. Turk, Miaomiao Zhang, Jie Luo, Elfar Adalsteinsson, P. Ellen Grant, Polina Golland
Date: 2019-03-06 00:00:00
Tasks: Time Series, Time Series Alignment, Time Series Analysis

We present a robust method to correct for motion in volumetric in-utero MRI
time series. Time-course analysis for in-utero volumetric MRI time series often
suffers from substantial and unpredictable fetal motion. Registration provides
voxel correspondences between images and is commonly employed for motion
correction. Current registration methods often fail when aligning images that
are substantially different from a template (reference image). To achieve
accurate and robust alignment, we make a Markov assumption on the nature of
motion and take advantage of the temporal smoothness in the image data. Forward
message passing in the corresponding hidden Markov model (HMM) yields an
estimation algorithm that only has to account for relatively small motion
between consecutive frames. We evaluate the utility of the temporal model in
the context of in-utero MRI time series alignment by examining the accuracy of
propagated segmentation label maps. Our results suggest that the proposed model
captures accurately the temporal dynamics of transformations in in-utero MRI
time series.
SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA
Authors: Daniel Hershcovich, Zohar Aizenbud, Leshem Choshen, Elior Sulem, Ari Rappoport, Omri Abend
Date: 2019-03-06 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Semantic Parsing, UCCA Parsing

We present the SemEval 2019 shared task on UCCA parsing in English, German and French, and discuss the participating systems and results. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typological work and supports rapid annotation. UCCA poses a challenge for existing parsing techniques, as it exhibits reentrancy (resulting in DAG structures), discontinuous structures and non-terminal nodes corresponding to complex semantic units. The shared task has yielded improvements over the state-of-the-art baseline in all languages and settings. Full results can be found in the task's website \url{https://competitions.codalab.org/competitions/19160}.
Learning the dynamics of technical trading strategies
Authors: Nicholas Murphy, Tim Gebbie
Date: 2019-03-06 00:00:00
Tasks: Dimensionality Reduction, Time Series, Time Series Analysis

We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive historical back-testing as well as form an overall aggregated portfolio trading strategy from the set of underlying trading strategies implemented on daily and intraday Johannesburg Stock Exchange data. The resulting population time-series are investigated using unsupervised learning for dimensionality reduction and visualisation. A key contribution is that the overall aggregated trading strategies are tested for statistical arbitrage using a novel hypothesis test proposed by Jarrow et al. (2012) on both daily sampled and intraday time-scales. The (low frequency) daily sampled strategies fail the arbitrage tests after costs, while the (high frequency) intraday sampled strategies are not falsified as statistical arbitrages after costs. The estimates of trading strategy success, cost of trading and slippage are considered along with an online benchmark portfolio algorithm for performance comparison. In addition, the algorithms generalisation error is analysed by recovering a probability of back-test overfitting estimate using a nonparametric procedure introduced by Bailey et al. (2016). The work aims to explore and better understand the interplay between different technical trading strategies from a data-informed perspective.
Improving Generalization of Deep Networks for Inverse Reconstruction of Image Sequences
Authors: Sandesh Ghimire, Prashnna Kumar Gyawali, Jwala Dhamala, John L. Sapp, Milan Horacek, Linwei Wang
Date: 2019-03-05 00:00:00
Tasks: Decoder, Image Reconstruction, Learning Theory

Deep learning networks have shown state-of-the-art performance in many image
reconstruction problems. However, it is not well understood what properties of
representation and learning may improve the generalization ability of the
network. In this paper, we propose that the generalization ability of an
encoder-decoder network for inverse reconstruction can be improved in two
means. First, drawing from analytical learning theory, we theoretically show
that a stochastic latent space will improve the ability of a network to
generalize to test data outside the training distribution. Second, following
the information bottleneck principle, we show that a latent representation
minimally informative of the input data will help a network generalize to
unseen input variations that are irrelevant to the output reconstruction.
Therefore, we present a sequence image reconstruction network optimized by a
variational approximation of the information bottleneck principle with
stochastic latent space. In the application setting of reconstructing the
sequence of cardiac transmembrane potential from bodysurface potential, we
assess the two types of generalization abilities of the presented network
against its deterministic counterpart. The results demonstrate that the
generalization ability of an inverse reconstruction network can be improved by
stochasticity as well as the information bottleneck.
Deep Learning in Medical Image Registration: A Survey
Authors: Grant Haskins, Uwe Kruger, Pingkun Yan
Date: 2019-03-05 00:00:00
Tasks: Deep Learning, Image Registration, Medical Image Registration, Survey

The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring, and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning based approaches and achieved the state-of-the-art in many applications, including image registration. The rapid adoption of deep learning for image registration applications over the past few years necessitates a comprehensive summary and outlook, which is the main scope of this survey. This requires placing a focus on the different research areas as well as highlighting challenges that practitioners face. This survey, therefore, outlines the evolution of deep learning based medical image registration in the context of both research challenges and relevant innovations in the past few years. Further, this survey highlights future research directions to show how this field may be possibly moved forward to the next level.
DIALOG: A framework for modeling, analysis and reuse of digital forensic knowledge
Authors: Damir Kahvedzic, Tahar Kechadi
Date: 2019-02-21 00:00:00
Tasks: Management

This paper presents DIALOG (Digital Investigation Ontology); a framework for
the management, reuse, and analysis of Digital Investigation knowledge. DIALOG
provides a general, application independent vocabulary that can be used to
describe an investigation at different levels of detail. DIALOG is defined to
encapsulate all concepts of the digital forensics field and the relationships
between them. In particular, we concentrate on the Windows Registry, where
registry keys are modeled in terms of both their structure and function.
Registry analysis software tools are modeled in a similar manner and we
illustrate how the interpretation of their results can be done using the
reasoning capabilities of ontology
Performance study of distributed Apriori-like frequent itemsets mining
Authors: Lamine M. Aouad, Nhien-An Le-Khac, Tahar M. Kechadi
Date: 2019-02-21 00:00:00

In this article, we focus on distributed Apriori-based frequent itemsets
mining. We present a new distributed approach which takes into account inherent
characteristics of this algorithm. We study the distribution aspect of this
algorithm and give a comparison of the proposed approach with a classical
Apriori-like distributed algorithm, using both analytical and experimental
studies. We find that under a wide range of conditions and datasets, the
performance of a distributed Apriori-like algorithm is not related to global
strategies of pruning since the performance of the local Apriori generation is
usually characterized by relatively high success rates of candidate sets
frequency at low levels which switch to very low rates at some stage, and often
drops to zero. This means that the intermediate communication steps and remote
support counts computation and collection in classical distributed schemes are
computationally inefficient locally, and then constrains the global
performance. Our performance evaluation is done on a large cluster of
workstations using the Condor system and its workflow manager DAGMan. The
results show that the presented approach greatly enhances the performance and
achieves good scalability compared to a typical distributed Apriori founded
algorithm.
A machine learning framework for data driven acceleration of computations of differential equations
Authors: Siddhartha Mishra
Date: 2018-07-25 00:00:00
Tasks: BIG-bench Machine Learning, Computational Efficiency

We propose a machine learning framework to accelerate numerical computations
of time-dependent ODEs and PDEs. Our method is based on recasting
(generalizations of) existing numerical methods as artificial neural networks,
with a set of trainable parameters. These parameters are determined in an
offline training process by (approximately) minimizing suitable (possibly
non-convex) loss functions by (stochastic) gradient descent methods. The
proposed algorithm is designed to be always consistent with the underlying
differential equation. Numerical experiments involving both linear and
non-linear ODE and PDE model problems demonstrate a significant gain in
computational efficiency over standard numerical methods.
Enhanced Network Embedding with Text Information
Authors: Shuang Yang, Bo Yang
Date: 2018-11-29 00:00:00
Proceeding: 24th International Conference on Pattern Recognition (ICPR) 2018 11
Tasks: Clustering, Multi-class Classification, Network Embedding, Node Classification

Network embedding aims at learning the low-dimensional and continuous vector representation for each node in networks, which is useful in many real applications. While most existing network embedding methods only focus on the network structure, the rich text information associated with nodes, which is often closely related to network structure, is widely neglected. Thus, how to effectively incorporate text information into network embedding is a problem worth studying. To solve the problem, we propose a Text Enhanced Network Embedding (TENE) method under the framework of non-negative matrix factorization to integrate network structure and text information together. We explore the consistent relationship between node representations and text cluster structure to make the network embedding more informative and discriminative. TENE learns the representations of nodes under the guidance of both proximity matrix which captures the network structure and text cluster membership matrix derived from clustering for text information. We evaluate the quality of network embedding on the task of multi-class classification of nodes. Experimental results on all three real-world datasets show the superior performance of TENE compared with baselines.
Graph Classification using Structural Attention
Authors: John Boaz Lee, Ryan Rossi, Xiangnan Kong
Date: 2018-07-19 00:00:00
Proceeding: KDD 2018 7
Tasks: Classification, General Classification, Graph Attention, Graph Classification

Graph classification is a problem with practical applications in many different domains. To solve this problem, one usually calculates certain graph statistics (i.e., graph features) that help discriminate between graphs of different classes. When calculating such features, most existing approaches process the entire graph. In a graphlet-based approach, for instance, the entire graph is processed to get the total count of different graphlets or subgraphs. In many real-world applications, however, graphs can be noisy with discriminative patterns confined to certain regions in the graph only. In this work, we study the problem of attention-based graph classification. The use of attention allows us to focus on small but informative parts of the graph, avoiding noise in the rest of the graph. We present a novel RNN model, called the Graph Attention Model (GAM), that processes only a portion of the graph by adaptively selecting a sequence of “informative” nodes. Experimental results on multiple real-world datasets show that the proposed method is competitive against various well-known methods in graph classification even though our method is limited to only a portion of the graph.
Font Size: Community Preserving Network Embedding
Authors: Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, Shiqiang Yang
Date: 2017-02-10 00:00:00
Proceeding: AAAI 2017 2
Tasks: Community Detection, Network Embedding, Representation Learning

Network embedding, aiming to learn the low-dimensional representations of nodes in networks, is of paramount importance in many real applications. One basic requirement of network embedding is to preserve the structure and inherent properties of the networks. While previous network embedding methods primarily preserve the microscopic structure, such as the first- and second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored. In this paper, we propose a novel Modularized Nonnegative Matrix Factorization (M-NMF) model to incorporate the community structure into network embedding. We exploit the consensus relationship between the representations of nodes and community structure, and then jointly optimize NMF based representation learning model and modularity based community detection model in a unified framework, which enables the learned representations of nodes to preserve both of the microscopic and community structures. We also provide efficient updating rules to infer the parameters of our model, together with the correctness and convergence guarantees. Extensive experimental results on a variety of real-world networks show the superior performance of the proposed method over the state-of-the-arts.
Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection
Authors: Fanghua Ye, Chuan Chen, Zibin Zheng
Date: 2018-10-22 00:00:00
Proceeding: CIKM 2018 10
Tasks: Community Detection, Decoder, Local Community Detection, Network Community Partition, Node Classification, Representation Learning

Community structure is ubiquitous in real-world complex networks. The task of community detection over these networks is of paramount importance in a variety of applications. Recently, nonnegative matrix factorization (NMF) has been widely adopted for community detection due to its great interpretability and its natural fitness for capturing the community membership of nodes. However, the existing NMF-based community detection approaches are shallow methods. They learn the community assignment by mapping the original network to the community membership space directly. Considering the complicated and diversified topology structures of real-world networks, it is highly possible that the mapping between the original network and the community membership space contains rather complex hierarchical information, which cannot be interpreted by classic shallow NMF-based approaches. Inspired by the unique feature representation learning capability of deep autoencoder, we propose a novel model, named Deep Autoencoder-like NMF (DANMF), for community detection. Similar to deep autoencoder, DANMF consists of an encoder component and a decoder component. This architecture empowers DANMF to learn the hierarchical mappings between the original network and the final community assignment with implicit low-to-high level hidden attributes of the original network learnt in the intermediate layers. Thus, DANMF should be better suited to the community detection task. Extensive experiments on benchmark datasets demonstrate that DANMF can achieve better performance than the state-of-the-art NMF-based community detection approaches.