pwc-archive/papers-with-abstracts huggingface dataset:
Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy games
Authors: Valentin Antuori, Florian Richoux
Date: 2019-01-03 00:00:00
Tasks: Combinatorial Optimization, Decision Making, Real-Time Strategy Games

Decision-making problems can be modeled as combinatorial optimization
problems with Constraint Programming formalisms such as Constrained
Optimization Problems. However, few Constraint Programming formalisms can deal
with both optimization and uncertainty at the same time, and none of them are
convenient to model problems we tackle in this paper.
  Here, we propose a way to deal with combinatorial optimization problems under
uncertainty within the classical Constrained Optimization Problems formalism by
injecting the Rank Dependent Utility from decision theory. We also propose a
proof of concept of our method to show it is implementable and can solve
concrete decision-making problems using a regular constraint solver, and
propose a bot that won the partially observable track of the 2018 {\mu}RTS AI
competition.
  Our result shows it is possible to handle uncertainty with regular Constraint
Programming solvers, without having to define a new formalism neither to
develop dedicated solvers. This brings new perspective to tackle uncertainty in
Constraint Programming.
Local Area Transform for Cross-Modality Correspondence Matching and Deep Scene Recognition
Authors: Seungchul Ryu
Date: 2019-01-03 00:00:00
Tasks: Image Retrieval, Retrieval, Scene Recognition, Stereo Matching, Stereo Matching Hand

Establishing correspondences is a fundamental task in variety of image
processing and computer vision applications. In particular, finding the
correspondences between a non-linearly deformed image pair induced by different
modality conditions is a challenging problem. This paper describes a efficient
but powerful image transform called local area transform (LAT) for
modality-robust correspondence estimation. Specifically, LAT transforms an
image from the intensity domain to the local area domain, which is invariant
under nonlinear intensity deformations, especially radiometric, photometric,
and spectral deformations. In addition, robust feature descriptors are
reformulated with LAT for several practical applications. Furthermore,
LAT-convolution layer and Aception block are proposed and, with these novel
components, deep neural network called LAT-Net is proposed especially for scene
recognition task. Experimental results show that LATransformed images provide a
consistency for nonlinearly deformed images, even under random intensity
deformations. LAT reduces the mean absolute difference as compared to
conventional methods. Furthermore, the reformulation of descriptors with LAT
shows superiority to conventional methods, which is a promising result for the
tasks of cross-spectral and modality correspondence matching. the local area
can be considered as an alternative domain to the intensity domain to achieve
robust correspondence matching, image recognition, and a lot of applications:
such as feature matching, stereo matching, dense correspondence matching, image
recognition, and image retrieval.
Reachability and Differential based Heuristics for Solving Markov Decision Processes
Authors: Shoubhik Debnath, Lantao Liu, Gaurav Sukhatme
Date: 2019-01-03 00:00:00

The solution convergence of Markov Decision Processes (MDPs) can be
accelerated by prioritized sweeping of states ranked by their potential impacts
to other states. In this paper, we present new heuristics to speed up the
solution convergence of MDPs. First, we quantify the level of reachability of
every state using the Mean First Passage Time (MFPT) and show that such
reachability characterization very well assesses the importance of states which
is used for effective state prioritization. Then, we introduce the notion of
backup differentials as an extension to the prioritized sweeping mechanism, in
order to evaluate the impacts of states at an even finer scale. Finally, we
extend the state prioritization to the temporal process, where only partial
sweeping can be performed during certain intermediate value iteration stages.
To validate our design, we have performed numerical evaluations by comparing
the proposed new heuristics with corresponding classic baseline mechanisms. The
evaluation results showed that our reachability based framework and its
differential variants have outperformed the state-of-the-art solutions in terms
of both practical runtime and number of iterations.
A Model for Learned Bloom Filters, and Optimizing by Sandwiching
Authors: Michael Mitzenmacher
Date: 2019-01-03 00:00:00
Tasks: BIG-bench Machine Learning

Recent work has suggested enhancing Bloom filters by using a pre-filter,
based on applying machine learning to determine a function that models the data
set the Bloom filter is meant to represent. Here we model such learned Bloom
filters,, with the following outcomes: (1) we clarify what guarantees can and
cannot be associated with such a structure; (2) we show how to estimate what
size the learning function must obtain in order to obtain improved performance;
(3) we provide a simple method, sandwiching, for optimizing learned Bloom
filters; and (4) we propose a design and analysis approach for a learned
Bloomier filter, based on our modeling approach.
Imminent Collision Mitigation with Reinforcement Learning and Vision
Authors: Horia Porav, Paul Newman
Date: 2019-01-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This work examines the role of reinforcement learning in reducing the
severity of on-road collisions by controlling velocity and steering in
situations in which contact is imminent. We construct a model, given camera
images as input, that is capable of learning and predicting the dynamics of
obstacles, cars and pedestrians, and train our policy using this model. Two
policies that control both braking and steering are compared against a baseline
where the only action taken is (conventional) braking in a straight line. The
two policies are trained using two distinct reward structures, one where any
and all collisions incur a fixed penalty, and a second one where the penalty is
calculated based on already established delta-v models of injury severity. The
results show that both policies exceed the performance of the baseline, with
the policy trained using injury models having the highest performance.
I Can See Clearly Now : Image Restoration via De-Raining
Authors: Horia Porav, Tom Bruls, Paul Newman
Date: 2019-01-03 00:00:00
Tasks: Denoising, Image Reconstruction, Image Restoration, Segmentation, Semantic Segmentation

We present a method for improving segmentation tasks on images affected by
adherent rain drops and streaks. We introduce a novel stereo dataset recorded
using a system that allows one lens to be affected by real water droplets while
keeping the other lens clear. We train a denoising generator using this dataset
and show that it is effective at removing the effect of real water droplets, in
the context of image reconstruction and road marking segmentation. To further
test our de-noising approach, we describe a method of adding computer-generated
adherent water droplets and streaks to any images, and use this technique as a
proxy to demonstrate the effectiveness of our model in the context of general
semantic segmentation. We benchmark our results using the CamVid road marking
segmentation dataset, Cityscapes semantic segmentation datasets and our own
real-rain dataset, and show significant improvement on all tasks.
Polarimetric Thermal to Visible Face Verification via Attribute Preserved Synthesis
Authors: Xing Di, He Zhang, Vishal M. Patel
Date: 2019-01-03 00:00:00
Tasks: Attribute, Face Verification, Generative Adversarial Network

Thermal to visible face verification is a challenging problem due to the
large domain discrepancy between the modalities. Existing approaches either
attempt to synthesize visible faces from thermal faces or extract robust
features from these modalities for cross-modal matching. In this paper, we take
a different approach in which we make use of the attributes extracted from the
visible image to synthesize the attribute-preserved visible image from the
input thermal image for cross-modal matching. A pre-trained VGG-Face network is
used to extract the attributes from the visible image. Then, a novel Attribute
Preserved Generative Adversarial Network (AP-GAN) is proposed to synthesize the
visible image from the thermal image guided by the extracted attributes.
Finally, a deep network is used to extract features from the synthesized image
and the input visible image for verification. Extensive experiments on the ARL
Polarimetric face dataset show that the proposed method achieves significant
improvements over the state-of-the-art methods.
Subspace Match Probably Does Not Accurately Assess the Similarity of Learned Representations
Authors: Jeremiah Johnson
Date: 2019-01-03 00:00:00

Learning informative representations of data is one of the primary goals of
deep learning, but there is still little understanding as to what
representations a neural network actually learns. To better understand this,
subspace match was recently proposed as a method for assessing the similarity
of the representations learned by neural networks. It has been shown that two
networks with the same architecture trained from different initializations
learn representations that at hidden layers show low similarity when assessed
with subspace match, even when the output layers show high similarity and the
networks largely exhibit similar performance on classification tasks. In this
note, we present a simple example motivated by standard results in commutative
algebra to illustrate how this can happen, and show that although the subspace
match at a hidden layer may be 0, the representations learned may be isomorphic
as vector spaces. This leads us to conclude that a subspace match comparison of
learned representations may well be uninformative, and it points to the need
for better methods of understanding learned representations.
A Network-based Multimodal Data Fusion Approach for Characterizing Dynamic Multimodal Physiological Patterns
Authors: Miaolin Fan, Chun-An Chou, Sheng-Che Yen, Yingzi Lin
Date: 2019-01-03 00:00:00

Characterizing the dynamic interactive patterns of complex systems helps gain
in-depth understanding of how components interrelate with each other while
performing certain functions as a whole. In this study, we present a novel
multimodal data fusion approach to construct a complex network, which models
the interactions of biological subsystems in the human body under emotional
states through physiological responses. Joint recurrence plot and temporal
network metrics are employed to integrate the multimodal information at the
signal level. A benchmark public dataset of is used for evaluating our model.
Learning Nonlinear State Space Models with Hamiltonian Sequential Monte Carlo Sampler
Authors: Duo Xu
Date: 2019-01-03 00:00:00
Tasks: Gaussian Processes, State Space Models

State space models (SSM) have been widely applied for the analysis and
visualization of large sequential datasets. Sequential Monte Carlo (SMC) is a
very popular particle-based method to sample latent states from intractable
posteriors. However, SSM is significantly influenced by the choice of the
proposal. Recently Hamiltonian Monte Carlo (HMC) sampling has shown success in
many practical problems. In this paper, we propose an SMC augmented by HMC
(HSMC) for inference and model learning of nonlinear SSM, which can exempt us
from learning proposals and reduce the model complexity significantly. Based on
the measure preserving property of HMC, the particles directly generated by
transition function can approximate the posterior of latent states arbitrarily
well. In order to better adapt to the local geometry of latent space, the HMC
is conducted on Riemannian manifold defined by a positive definite metric. In
addition, we show that the proposed HSMC method can improve SSMs realized by
both Gaussian Processes (GP) and Neural Network (NN).
Mapping Informal Settlements in Developing Countries using Machine Learning and Low Resolution Multi-spectral Data
Authors: Bradley Gram-Hansen, Patrick Helber, Indhu Varatharajan, Faiza Azam, Alejandro Coca-Castro, Veronika Kopackova, Piotr Bilinski
Date: 2019-01-03 00:00:00
Tasks: BIG-bench Machine Learning

Informal settlements are home to the most socially and economically vulnerable people on the planet. In order to deliver effective economic and social aid, non-government organizations (NGOs), such as the United Nations Children's Fund (UNICEF), require detailed maps of the locations of informal settlements. However, data regarding informal and formal settlements is primarily unavailable and if available is often incomplete. This is due, in part, to the cost and complexity of gathering data on a large scale. To address these challenges, we, in this work, provide three contributions. 1) A brand new machine learning data-set, purposely developed for informal settlement detection. 2) We show that it is possible to detect informal settlements using freely available low-resolution (LR) data, in contrast to previous studies that use very-high resolution (VHR) satellite and aerial imagery, something that is cost-prohibitive for NGOs. 3) We demonstrate two effective classification schemes on our curated data set, one that is cost-efficient for NGOs and another that is cost-prohibitive for NGOs, but has additional utility. We integrate these schemes into a semi-automated pipeline that converts either a LR or VHR satellite image into a binary map that encodes the locations of informal settlements.
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision
Authors: Vishal Kaushal, Rishabh Iyer, Suraj Kothawade, Rohan Mahadev, Khoshrav Doctor, Ganesh Ramakrishnan
Date: 2019-01-03 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning, Diversity, Face Recognition, object-detection, Object Detection, Object Recognition, Scene Recognition

Supervised machine learning based state-of-the-art computer vision techniques
are in general data hungry. Their data curation poses the challenges of
expensive human labeling, inadequate computing resources and larger experiment
turn around times. Training data subset selection and active learning
techniques have been proposed as possible solutions to these challenges. A
special class of subset selection functions naturally model notions of
diversity, coverage and representation and can be used to eliminate redundancy
thus lending themselves well for training data subset selection. They can also
help improve the efficiency of active learning in further reducing human
labeling efforts by selecting a subset of the examples obtained using the
conventional uncertainty sampling based techniques. In this work, we
empirically demonstrate the effectiveness of two diversity models, namely the
Facility-Location and Dispersion models for training-data subset selection and
reducing labeling effort. We demonstrate this across the board for a variety of
computer vision tasks including Gender Recognition, Face Recognition, Scene
Recognition, Object Detection and Object Recognition. Our results show that
diversity based subset selection done in the right way can increase the
accuracy by upto 5 - 10% over existing baselines, particularly in settings in
which less training data is available. This allows the training of complex
machine learning models like Convolutional Neural Networks with much less
training data and labeling costs while incurring minimal performance loss.
Demystifying Multi-Faceted Video Summarization: Tradeoff Between Diversity,Representation, Coverage and Importance
Authors: Vishal Kaushal, Rishabh Iyer, Khoshrav Doctor, Anurag Sahoo, Pratik Dubal, Suraj Kothawade, Rohan Mahadev, Kunal Dargan, Ganesh Ramakrishnan
Date: 2019-01-03 00:00:00
Tasks: Diversity, Video Summarization

This paper addresses automatic summarization of videos in a unified manner.
In particular, we propose a framework for multi-faceted summarization for
extractive, query base and entity summarization (summarization at the level of
entities like objects, scenes, humans and faces in the video). We investigate
several summarization models which capture notions of diversity, coverage,
representation and importance, and argue the utility of these different models
depending on the application. While most of the prior work on submodular
summarization approaches has focused oncombining several models and learning
weighted mixtures, we focus on the explainability of different models and
featurizations, and how they apply to different domains. We also provide
implementation details on summarization systems and the different modalities
involved. We hope that the study from this paper will give insights into
practitioners to appropriately choose the right summarization models for the
problems at hand.
HG-Caffe: Mobile and Embedded Neural Network GPU (OpenCL) Inference Engine with FP16 Supporting
Authors: Zhuoran Ji
Date: 2019-01-03 00:00:00
Tasks: GPU

Breakthroughs in the fields of deep learning and mobile system-on-chips are
radically changing the way we use our smartphones. However, deep neural
networks inference is still a challenging task for edge AI devices due to the
computational overhead on mobile CPUs and a severe drain on the batteries. In
this paper, we present a deep neural network inference engine named HG-Caffe,
which supports GPUs with half precision. HG-Caffe provides up to 20 times
speedup with GPUs compared to the original implementations. In addition to the
speedup, the peak memory usage is also reduced to about 80%. With HG-Caffe,
more innovative and fascinating mobile applications will be turned into
reality.
A unified framework of epidemic spreading prediction by empirical mode decomposition based ensemble learning techniques
Authors: Yun Feng, Bing-Chuan Wang
Date: 2019-01-03 00:00:00
Tasks: Ensemble Learning, Prediction, Time Series, Time Series Analysis

In this paper, a unified susceptible-exposed-infected-susceptible-aware
(SEIS-A) framework is proposed to combine epidemic spreading with individuals'
on-line self-consultation behaviors. An epidemic spreading prediction model is
established based on the SEIS-A framework. The prediction process contains two
phases. In phase I, the time series data of disease density are decomposed
through the empirical mode decomposition (EMD) method to obtain the intrinsic
mode functions (IMFs). In phase II, the ensemble learning techniques which use
the on-line query data as an additional input are applied to these IMFs.
Finally, experiments for prediction of weekly consultation rates of
Hand-foot-and-mouth disease (HFMD) in Hong Kong are conducted to validate the
effectiveness of the proposed method. The main advantage of this method is that
it outperforms other methods on fluctuating complex data.
Draining the Water Hole: Mitigating Social Engineering Attacks with CyberTWEAK
Authors: Zheyuan Ryan Shi, Aaron Schlenker, Brian Hay, Daniel Bittleston, Siyu Gao, Emily Peterson, John Trezza, Fei Fang
Date: 2019-01-03 00:00:00

Cyber adversaries have increasingly leveraged social engineering attacks to breach large organizations and threaten the well-being of today's online users. One clever technique, the "watering hole" attack, compromises a legitimate website to execute drive-by download attacks by redirecting users to another malicious domain. We introduce a game-theoretic model that captures the salient aspects for an organization protecting itself from a watering hole attack by altering the environment information in web traffic so as to deceive the attackers. Our main contributions are (1) a novel Social Engineering Deception (SED) game model that features a continuous action set for the attacker, (2) an in-depth analysis of the SED model to identify computationally feasible real-world cases, and (3) the CyberTWEAK algorithm which solves for the optimal protection policy. To illustrate the potential use of our framework, we built a browser extension based on our algorithms which is now publicly available online. The CyberTWEAK extension will be vital to the continued development and deployment of countermeasures for social engineering.
Transfer learning from language models to image caption generators: Better models may not transfer better
Authors: Marc Tanti, Albert Gatt, Kenneth P. Camilleri
Date: 2019-01-01 00:00:00
Tasks: Language Modeling, Language Modelling, Sentence, Transfer Learning

When designing a neural caption generator, a convolutional neural network can
be used to extract image features. Is it possible to also use a neural language
model to extract sentence prefix features? We answer this question by trying
different ways to transfer the recurrent neural network and embedding layer
from a neural language model to an image caption generator. We find that image
caption generators with transferred parameters perform better than those
trained from scratch, even when simply pre-training them on the text of the
same captions dataset it will later be trained on. We also find that the best
language models (in terms of perplexity) do not result in the best caption
generators after transfer learning.
Theory and Algorithms for Pulse Signal Processing
Authors: Gabriel Nallathambi, Jose C. Principe
Date: 2018-12-31 00:00:00

The integrate and fire converter transforms an analog signal into train of
biphasic pulses. The pulse train has information encoded in the timing and
polarity of pulses. While it has been shown that any finite bandwidth analog
signal can be reconstructed from these pulse trains with an error as small as
desired, there is a need for fundamental signal processing techniques to
operate directly on pulse trains without signal reconstruction. In this paper,
the feasibility of performing online the signal processing operations of
addition, multiplication, and convolution of analog signals using their pulses
train representations is explored. Theoretical framework to perform signal
processing with pulse trains imposing minimal restrictions is derived, and
algorithms for online implementation of the operators are developed.
Performance of the algorithms in processing simulated data is studied. An
application of noise subtraction and representation of relevant features of
interest in electrocardiogram signal is demonstrated with mean pulse rate less
than 20 pulses per second.
Space Expansion of Feature Selection for Designing more Accurate Error Predictors
Authors: Shayan Tabatabaei Nikkhah, Mehdi Kamal, Ali Afzali-Kusha, Massoud Pedram
Date: 2018-12-30 00:00:00
Tasks: feature selection, Prediction, Scheduling

Approximate computing is being considered as a promising design paradigm to
overcome the energy and performance challenges in computationally demanding
applications. If the case where the accuracy can be configured, the quality
level versus energy efficiency or delay also may be traded-off. For this
technique to be used, one needs to make sure a satisfactory user experience.
This requires employing error predictors to detect unacceptable approximation
errors. In this work, we propose a scheduling-aware feature selection method
which leverages the intermediate results of the hardware accelerator to improve
the prediction accuracy. Additionally, it configures the error predictors
according to the energy consumption and latency of the system. The approach
enjoys the flexibility of the prediction time for a higher accuracy. The
results on various benchmarks demonstrate significant improvements in the
prediction accuracy compared to the prior works which used only the accelerator
inputs for the prediction.