pwc-archive/papers-with-abstracts huggingface dataset:
Cyber Anomaly Detection Using Graph-node Role-dynamics
Authors: Anthony Palladino, Christopher J. Thissen
Date: 2018-12-06 00:00:00
Tasks: Anomaly Detection, Intrusion Detection

Intrusion detection systems (IDSs) generate valuable knowledge about network
security, but an abundance of false alarms and a lack of methods to capture the
interdependence among alerts hampers their utility for network defense. Here,
we explore a graph-based approach for fusing alerts generated by multiple IDSs
(e.g., Snort, OSSEC, and Bro). Our approach generates a weighted graph of alert
fields (not network topology) that makes explicit the connections between
multiple alerts, IDS systems, and other cyber artifacts. We use this
multi-modal graph to identify anomalous changes in the alert patterns of a
network. To detect the anomalies, we apply the role-dynamics approach, which
has successfully identified anomalies in social media, email, and IP
communication graphs. In the cyber domain, each node (alert field) in the fused
IDS alert graph is assigned a probability distribution across a small set of
roles based on that node's features. A cyber attack should trigger IDS alerts
and cause changes in the node features, but rather than track every feature for
every alert-field node individually, roles provide a succinct, integrated
summary of those feature changes. We measure changes in each node's
probabilistic role assignment over time, and identify anomalies as deviations
from expected roles. We test our approach using simulations including three
weeks of normal background traffic, as well as cyber attacks that occur near
the end of the simulations. This paper presents a novel approach to multi-modal
data fusion and a novel application of role dynamics within the cyber-security
domain. Our results show a drastic decrease in the false-positive rate when
considering our anomaly indicator instead of the IDS alerts themselves, thereby
reducing alarm fatigue and providing a promising avenue for threat intelligence
in network defense.
Fooling Network Interpretation in Image Classification
Authors: Akshayvarun Subramanya, Vipin Pillai, Hamed Pirsiavash
Date: 2018-12-06 00:00:00
Proceeding: ICCV 2019 10
Tasks: Adversarial Attack, Classification, Decision Making, General Classification, image-classification, Image Classification, Network Interpretation

Deep neural networks have been shown to be fooled rather easily using adversarial attack algorithms. Practical methods such as adversarial patches have been shown to be extremely effective in causing misclassification. However, these patches are highlighted using standard network interpretation algorithms, thus revealing the identity of the adversary. We show that it is possible to create adversarial patches which not only fool the prediction, but also change what we interpret regarding the cause of the prediction. Moreover, we introduce our attack as a controlled setting to measure the accuracy of interpretation algorithms. We show this using extensive experiments for Grad-CAM interpretation that transfers to occluding patch interpretation as well. We believe our algorithms can facilitate developing more robust network interpretation tools that truly explain the network's underlying decision making process.
High-Quality Face Capture Using Anatomical Muscles
Authors: Michael Bao, Matthew Cong, Stéphane Grabli, Ronald Fedkiw
Date: 2018-12-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Vocal Bursts Intensity Prediction

Muscle-based systems have the potential to provide both anatomical accuracy
and semantic interpretability as compared to blendshape models; however, a lack
of expressivity and differentiability has limited their impact. Thus, we
propose modifying a recently developed rather expressive muscle-based system in
order to make it fully-differentiable; in fact, our proposed modifications
allow this physically robust and anatomically accurate muscle model to
conveniently be driven by an underlying blendshape basis. Our formulation is
intuitive, natural, as well as monolithically and fully coupled such that one
can differentiate the model from end to end, which makes it viable for both
optimization and learning-based approaches for a variety of applications. We
illustrate this with a number of examples including both shape matching of
three-dimensional geometry as as well as the automatic determination of a
three-dimensional facial pose from a single two-dimensional RGB image without
using markers or depth information.
Disentangling Disentanglement in Variational Autoencoders
Authors: Emile Mathieu, Tom Rainforth, N. Siddharth, Yee Whye Teh
Date: 2018-12-06 00:00:00
Tasks: Clustering, Disentanglement

We develop a generalisation of disentanglement in VAEs---decomposition of the latent representation---characterising it as the fulfilment of two factors: a) the latent encodings of the data having an appropriate level of overlap, and b) the aggregate encoding of the data conforming to a desired structure, represented through the prior. Decomposition permits disentanglement, i.e. explicit independence between latents, as a special case, but also allows for a much richer class of properties to be imposed on the learnt representation, such as sparsity, clustering, independent subspaces, or even intricate hierarchical dependency relationships. We show that the $\beta$-VAE varies from the standard VAE predominantly in its control of latent overlap and that for the standard choice of an isotropic Gaussian prior, its objective is invariant to rotations of the latent representation. Viewed from the decomposition perspective, breaking this invariance with simple manipulations of the prior can yield better disentanglement with little or no detriment to reconstructions. We further demonstrate how other choices of prior can assist in producing different decompositions and introduce an alternative training objective that allows the control of both decomposition factors in a principled manner.
Neural Image Decompression: Learning to Render Better Image Previews
Authors: Shumeet Baluja, Dave Marwood, Nick Johnston, Michele Covell
Date: 2018-12-06 00:00:00
Tasks: Decoder, SSIM

A rapidly increasing portion of Internet traffic is dominated by requests
from mobile devices with limited- and metered-bandwidth constraints. To satisfy
these requests, it has become standard practice for websites to transmit small
and extremely compressed image previews as part of the initial page-load
process. Recent work, based on an adaptive triangulation of the target image,
has shown the ability to generate thumbnails of full images at extreme
compression rates: 200 bytes or less with impressive gains (in terms of PSNR
and SSIM) over both JPEG and WebP standards. However, qualitative assessments
and preservation of semantic content can be less favorable. We present a novel
method to significantly improve the reconstruction quality of the original
image with no changes to the encoded information. Our neural-based decoding not
only achieves higher PSNR and SSIM scores than the original methods, but also
yields a substantial increase in semantic-level content preservation. In
addition, by keeping the same encoding stream, our solution is completely
inter-operable with the original decoder. The end result is suitable for a
range of small-device deployments, as it involves only a single forward-pass
through a small, scalable network.
Learning Implicit Fields for Generative Shape Modeling
Authors: Zhiqin Chen, Hao Zhang
Date: 2018-12-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Reconstruction, 3D Shape Representation, Decoder, Representation Learning, Single-View 3D Reconstruction

We advocate the use of implicit fields for learning generative models of shapes and introduce an implicit field decoder, called IM-NET, for shape generation, aimed at improving the visual quality of the generated shapes. An implicit field assigns a value to each point in 3D space, so that a shape can be extracted as an iso-surface. IM-NET is trained to perform this assignment by means of a binary classifier. Specifically, it takes a point coordinate, along with a feature vector encoding a shape, and outputs a value which indicates whether the point is outside the shape or not. By replacing conventional decoders by our implicit decoder for representation learning (via IM-AE) and shape generation (via IM-GAN), we demonstrate superior results for tasks such as generative shape modeling, interpolation, and single-view 3D reconstruction, particularly in terms of visual quality. Code and supplementary material are available at https://github.com/czq142857/implicit-decoder.
Tri-axial Self-Attention for Concurrent Activity Recognition
Authors: Yanyi Zhang, Xinyu Li, Kaixiang Huang, Yehan Wang, Shuhong Chen, Ivan Marsic
Date: 2018-12-06 00:00:00
Tasks: Action Detection, Activity Detection, Activity Prediction, Activity Recognition, Concurrent Activity Recognition, Decision Making, General Classification

We present a system for concurrent activity recognition. To extract features
associated with different activities, we propose a feature-to-activity
attention that maps the extracted global features to sub-features associated
with individual activities. To model the temporal associations of individual
activities, we propose a transformer-network encoder that models independent
temporal associations for each activity. To make the concurrent activity
prediction aware of the potential associations between activities, we propose
self-attention with an association mask. Our system achieved state-of-the-art
or comparable performance on three commonly used concurrent activity detection
datasets. Our visualizations demonstrate that our system is able to locate the
important spatial-temporal features for final decision making. We also showed
that our system can be applied to general multilabel classification problems.
Light Propagation Prediction through Multimode Optical Fibers with a Deep Neural Network
Authors: Pengfei Fan, Liang Deng, Lei Su
Date: 2018-12-06 00:00:00
Tasks: SSIM

This work demonstrates a computational method for predicting the light
propagation through a single multimode fiber using a deep neural network. The
experiment for gathering training and testing data is performed with a digital
micro-mirror device that enables the spatial light modulation. The modulated
patterns on the device and the captured intensity-only images by the camera
form the aligned data pairs. This sufficiently-trained deep neural network
frame has very excellent performance for directly inferring the intensity-only
output delivered though a multimode fiber. The model is validated by three
standards: the mean squared error (MSE), the correlation coefficient (corr) and
the structural similarity index (SSIM).
End-to-End Streaming Keyword Spotting
Authors: Alvarez Raziel, Park Hyun-Jin
Date: 2018-12-06 00:00:00
Tasks: Keyword Spotting

We present a system for keyword spotting that, except for a frontend
component for feature generation, it is entirely contained in a deep neural
network (DNN) model trained "end-to-end" to predict the presence of the keyword
in a stream of audio. The main contributions of this work are, first, an
efficient memoized neural network topology that aims at making better use of
the parameters and associated computations in the DNN by holding a memory of
previous activations distributed over the depth of the DNN. The second
contribution is a method to train the DNN, end-to-end, to produce the keyword
spotting score. This system significantly outperforms previous approaches both
in terms of quality of detection as well as size and computation.
Verification of deep probabilistic models
Authors: Krishnamurthy Dvijotham, Marta Garnelo, Alhussein Fawzi, Pushmeet Kohli
Date: 2018-12-06 00:00:00
Tasks: Machine Translation, Translation

Probabilistic models are a critical part of the modern deep learning toolbox
- ranging from generative models (VAEs, GANs), sequence to sequence models used
in machine translation and speech processing to models over functional spaces
(conditional neural processes, neural processes). Given the size and complexity
of these models, safely deploying them in applications requires the development
of tools to analyze their behavior rigorously and provide some guarantees that
these models are consistent with a list of desirable properties or
specifications. For example, a machine translation model should produce
semantically equivalent outputs for innocuous changes in the input to the
model. A functional regression model that is learning a distribution over
monotonic functions should predict a larger value at a larger input.
Verification of these properties requires a new framework that goes beyond
notions of verification studied in deterministic feedforward networks, since
requiring worst-case guarantees in probabilistic models is likely to produce
conservative or vacuous results. We propose a novel formulation of verification
for deep probabilistic models that take in conditioning inputs and sample
latent variables in the course of producing an output: We require that the
output of the model satisfies a linear constraint with high probability over
the sampling of latent variables and for every choice of conditioning input to
the model. We show that rigorous lower bounds on the probability that the
constraint is satisfied can be obtained efficiently. Experiments with neural
processes show that several properties of interest while modeling functional
spaces can be modeled within this framework (monotonicity, convexity) and
verified efficiently using our algorithms
Generation of Synthetic Electronic Medical Record Text
Authors: Jiaqi Guan, Runzhe Li, Sheng Yu, Xuegong Zhang
Date: 2018-12-06 00:00:00
Tasks: Data Augmentation, Generative Adversarial Network

Machine learning (ML) and Natural Language Processing (NLP) have achieved
remarkable success in many fields and have brought new opportunities and high
expectation in the analyses of medical data. The most common type of medical
data is the massive free-text electronic medical records (EMR). It is widely
regarded that mining such massive data can bring up important information for
improving medical practices as well as for possible new discoveries on complex
diseases. However, the free EMR texts are lacking consistent standards, rich of
private information, and limited in availability. Also, as they are accumulated
from everyday practices, it is often hard to have a balanced number of samples
for the types of diseases under study. These problems hinder the development of
ML and NLP methods for EMR data analysis. To tackle these problems, we
developed a model to generate synthetic text of EMRs called Medical Text
Generative Adversarial Network or mtGAN. It is based on the GAN framework and
is trained by the REINFORCE algorithm. It takes disease features as inputs and
generates synthetic texts as EMRs for the corresponding diseases. We evaluate
the model from micro-level, macro-level and application-level on a Chinese EMR
text dataset. The results show that the method has a good capacity to fit real
data and can generate realistic and diverse EMR samples. This provides a novel
way to avoid potential leakage of patient privacy while still supply sufficient
well-controlled cohort data for developing downstream ML and NLP methods. It
can also be used as a data augmentation method to assist studies based on real
EMR data.
Beyond imitation: Zero-shot task transfer on robots by learning concepts as cognitive programs
Authors: Miguel Lázaro-Gredilla, Dianhuan Lin, J. Swaroop Guntupalli, Dileep George
Date: 2018-12-06 00:00:00
Tasks: Novel Concepts

Humans can infer concepts from image pairs and apply those in the physical
world in a completely different setting, enabling tasks like IKEA assembly from
diagrams. If robots could represent and infer high-level concepts, it would
significantly improve their ability to understand our intent and to transfer
tasks between different environments. To that end, we introduce a computational
framework that replicates aspects of human concept learning. Concepts are
represented as programs on a novel computer architecture consisting of a visual
perception system, working memory, and action controller. The instruction set
of this "cognitive computer" has commands for parsing a visual scene, directing
gaze and attention, imagining new objects, manipulating the contents of a
visual working memory, and controlling arm movement. Inferring a concept
corresponds to inducing a program that can transform the input to the output.
Some concepts require the use of imagination and recursion. Previously learned
concepts simplify the learning of subsequent more elaborate concepts, and
create a hierarchy of abstractions. We demonstrate how a robot can use these
abstractions to interpret novel concepts presented to it as schematic images,
and then apply those concepts in dramatically different situations. By bringing
cognitive science ideas on mental imagery, perceptual symbols, embodied
cognition, and deictic mechanisms into the realm of machine learning, our work
brings us closer to the goal of building robots that have interpretable
representations and commonsense.
StoryGAN: A Sequential Conditional GAN for Story Visualization
Authors: Yitong Li, Zhe Gan, Yelong Shen, Jingjing Liu, Yu Cheng, Yuexin Wu, Lawrence Carin, David Carlson, Jianfeng Gao
Date: 2018-12-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Sentence, Story Visualization, Video Generation

We propose a new task, called Story Visualization. Given a multi-sentence
paragraph, the story is visualized by generating a sequence of images, one for
each sentence. In contrast to video generation, story visualization focuses
less on the continuity in generated images (frames), but more on the global
consistency across dynamic scenes and characters -- a challenge that has not
been addressed by any single-image or video generation methods. We therefore
propose a new story-to-image-sequence generation model, StoryGAN, based on the
sequential conditional GAN framework. Our model is unique in that it consists
of a deep Context Encoder that dynamically tracks the story flow, and two
discriminators at the story and image levels, to enhance the image quality and
the consistency of the generated sequences. To evaluate the model, we modified
existing datasets to create the CLEVR-SV and Pororo-SV datasets. Empirically,
StoryGAN outperforms state-of-the-art models in image quality, contextual
consistency metrics, and human evaluation.
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents
Authors: Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, Tamer Başar
Date: 2018-12-06 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Despite the increasing interest in multi-agent reinforcement learning (MARL) in multiple communities, understanding its theoretical foundation has long been recognized as a challenging problem. In this work, we address this problem by providing a finite-sample analysis for decentralized batch MARL with networked agents. Specifically, we consider two decentralized MARL settings, where teams of agents are connected by time-varying communication networks, and either collaborate or compete in a zero-sum game setting, without any central controller. These settings cover many conventional MARL settings in the literature. For both settings, we develop batch MARL algorithms that can be implemented in a decentralized fashion, and quantify the finite-sample errors of the estimated action-value functions. Our error analysis captures how the function class, the number of samples within each iteration, and the number of iterations determine the statistical accuracy of the proposed algorithms. Our results, compared to the finite-sample bounds for single-agent RL, involve additional error terms caused by decentralized computation, which is inherent in our decentralized MARL setting. This work appears to be the first finite-sample analysis for batch MARL, a step towards rigorous theoretical understanding of general MARL algorithms in the finite-sample regime.
A dataset of continuous affect annotations and physiological signals for emotion analysis
Authors: Karan Sharma, Claudio Castellini, Egon L. van den Broek, Alin Albu-Schaeffer, Friedhelm Schwenker
Date: 2018-12-06 00:00:00
Tasks: Emotion Recognition

From a computational viewpoint, emotions continue to be intriguingly hard to
understand. In research, direct, real-time inspection in realistic settings is
not possible. Discrete, indirect, post-hoc recordings are therefore the norm.
As a result, proper emotion assessment remains a problematic issue. The
Continuously Annotated Signals of Emotion (CASE) dataset provides a solution as
it focusses on real-time continuous annotation of emotions, as experienced by
the participants, while watching various videos. For this purpose, a novel,
intuitive joystick-based annotation interface was developed, that allowed for
simultaneous reporting of valence and arousal, that are instead often annotated
independently. In parallel, eight high quality, synchronized physiological
recordings (1000 Hz, 16-bit ADC) were made of ECG, BVP, EMG (3x), GSR (or EDA),
respiration and skin temperature. The dataset consists of the physiological and
annotation data from 30 participants, 15 male and 15 female, who watched
several validated video-stimuli. The validity of the emotion induction, as
exemplified by the annotation and physiological data, is also presented.
ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape
Authors: Fabian Manhardt, Wadim Kehl, Adrien Gaidon
Date: 2018-12-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Object Detection, Data Augmentation, Monocular 3D Object Detection, object-detection, Object Detection, Retrieval

We present a deep learning method for end-to-end monocular 3D object
detection and metric shape retrieval. We propose a novel loss formulation by
lifting 2D detection, orientation, and scale estimation into 3D space. Instead
of optimizing these quantities separately, the 3D instantiation allows to
properly measure the metric misalignment of boxes. We experimentally show that
our 10D lifting of sparse 2D Regions of Interests (RoIs) achieves great results
both for 6D pose and recovery of the textured metric geometry of instances.
This further enables 3D synthetic data augmentation via inpainting recovered
meshes directly onto the 2D scenes. We evaluate on KITTI3D against other strong
monocular methods and demonstrate that our approach doubles the AP on the 3D
pose metrics on the official test set, defining the new state of the art.
Object Discovery in Videos as Foreground Motion Clustering
Authors: Christopher Xie, Yu Xiang, Zaid Harchaoui, Dieter Fox
Date: 2018-12-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Clustering, Motion Segmentation, Object, Object Discovery, Object Discovery In Videos, Segmentation

We consider the problem of providing dense segmentation masks for object
discovery in videos. We formulate the object discovery problem as foreground
motion clustering, where the goal is to cluster foreground pixels in videos
into different objects. We introduce a novel pixel-trajectory recurrent neural
network that learns feature embeddings of foreground pixel trajectories linked
across time. By clustering the pixel trajectories using the learned feature
embeddings, our method establishes correspondences between foreground object
masks across video frames. To demonstrate the effectiveness of our framework
for object discovery, we conduct experiments on commonly used datasets for
motion segmentation, where we achieve state-of-the-art performance.