pwc-archive/papers-with-abstracts huggingface dataset:
RNNSecureNet: Recurrent neural networks for Cyber security use-cases
Authors: Mohammed Harun Babu R, Vinayakumar R, Soman Kp
Date: 2019-01-05 00:00:00
Tasks: Fraud Detection, General Classification, Malware Classification

Recurrent neural network (RNN) is an effective neural network in solving very
complex supervised and unsupervised tasks. There has been a significant
improvement in RNN field such as natural language processing, speech
processing, computer vision and other multiple domains. This paper deals with
RNN application on different use cases like Incident Detection, Fraud
Detection, and Android Malware Classification. The best performing neural
network architecture is chosen by conducting different chain of experiments for
different network parameters and structures. The network is run up to 1000
epochs with learning rate set in the range of 0.01 to 0.5.Obviously, RNN
performed very well when compared to classical machine learning algorithms.
This is mainly possible because RNNs implicitly extracts the underlying
features and also identifies the characteristics of the data. This helps to
achieve better accuracy.
Risk-Aware Resource Allocation for URLLC: Challenges and Strategies with Machine Learning
Authors: Amin Azari, Mustafa Ozger, Cicek Cavdar
Date: 2018-12-22 00:00:00
Tasks: BIG-bench Machine Learning, Management

Supporting ultra-reliable low-latency communications (URLLC) is a major
challenge of 5G wireless networks. Stringent delay and reliability requirements
need to be satisfied for both scheduled and non-scheduled URLLC traffic to
enable a diverse set of 5G applications. Although physical and media access
control layer solutions have been investigated to satisfy only scheduled URLLC
traffic, there is a lack of study on enabling transmission of non-scheduled
URLLC traffic, especially in coexistence with the scheduled URLLC traffic.
Machine learning (ML) is an important enabler for such a co-existence scenario
due to its ability to exploit spatial/temporal correlation in user behaviors
and use of radio resources. Hence, in this paper, we first study the
coexistence design challenges, especially the radio resource management (RRM)
problem and propose a distributed risk-aware ML solution for RRM. The proposed
solution benefits from hybrid orthogonal/non-orthogonal radio resource slicing,
and proactively regulates the spectrum needed for satisfying delay/reliability
requirement of each URLLC traffic type. A case study is introduced to
investigate the potential of the proposed RRM in serving coexisting URLLC
traffic types. The results further provide insights on the benefits of
leveraging intelligent RRM, e.g. a 75% increase in data rate with respect to
the conservative design approach for the scheduled traffic is achieved, while
the 99.99% reliability of both scheduled and nonscheduled traffic types is
satisfied.
On-Demand Video Dispatch Networks: A Scalable End-to-End Learning Approach
Authors: Damao Yang, Sihan Peng, He Huang, Hongliang Xue
Date: 2018-12-25 00:00:00
Tasks: Clustering

We design a dispatch system to improve the peak service quality of video on
demand (VOD). Our system predicts the hot videos during the peak hours of the
next day based on the historical requests, and dispatches to the content
delivery networks (CDNs) at the previous off-peak time. In order to scale to
billions of videos, we build the system with two neural networks, one for video
clustering and the other for dispatch policy developing. The clustering network
employs autoencoder layers and reduces the video number to a fixed value. The
policy network employs fully connected layers and ranks the clustered videos
with dispatch probabilities. The two networks are coupled with weight-sharing
temporal layers, which analyze the video request sequences with convolutional
and recurrent modules. Therefore, the clustering and dispatch tasks are trained
in an end-to-end mechanism. The real-world results show that our approach
achieves an average prediction accuracy of 17%, compared with 3% from the
present baseline method, for the same amount of dispatches.
Quantum-theoretic Modeling in Computer Science A complex Hilbert space model for entangled concepts in corpuses of documents
Authors: Diederik Aerts, Lester Beltran, Suzette Geriente, Sandro Sozzo
Date: 2019-01-05 00:00:00
Tasks: Retrieval

We work out a quantum-theoretic model in complex Hilbert space of a recently
performed test on co-occurrencies of two concepts and their combination in
retrieval processes on specific corpuses of documents. The test violated the
Clauser-Horne-Shimony-Holt version of the Bell inequalities ('CHSH
inequality'), thus indicating the presence of entanglement between the combined
concepts. We make use of a recently elaborated 'entanglement scheme' and
represent the collected data in the tensor product of Hilbert spaces of the
individual concepts, showing that the identified violation is due to the
occurrence of a strong form of entanglement, involving both states and
measurements and reflecting the meaning connection between the component
concepts. These results provide a significant confirmation of the presence of
quantum structures in corpuses of documents, like it is the case for the
entanglement identified in human cognition.
Self-Refining Deep Symmetry Enhanced Network for Rain Removal
Authors: Hanrong Ye, Xia Li, Hong Liu, Wei Shi, Mengyuan Liu, Qianru Sun
Date: 2018-11-06 00:00:00
Tasks: Image Restoration, Rain Removal

Rain removal aims to extract and remove rain streaks from images. Although
convolutional neural network (CNN) based methods have achieved impressive
results in this field, they are not equivariant to object rotation, which
decreases their generalization capacity for tilted rain streaks. In order to
solve this problem, we propose Deep Symmetry Enhanced Network (DSEN). DSEN
extracts rotationally equivariant features of rain streaks, and then generates
rain layer for image restoration. Furthermore, an efficient self-refining
mechanism is designed to remove tilted rain streaks more thoroughly.
Experimental study verifies the validity of our method, with self-refining DSEN
yielding the state-of-the-art performance on both synthetic and real-world rain
image datasets.
DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion
Authors: Chen Wang, Danfei Xu, Yuke Zhu, Roberto Martín-Martín, Cewu Lu, Li Fei-Fei, Silvio Savarese
Date: 2019-01-15 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Object Detection, 6D Pose Estimation, 6D Pose Estimation using RGBD, Object, Pose Estimation

A key technical challenge in performing 6D object pose estimation from RGB-D
image is to fully leverage the two complementary data sources. Prior works
either extract information from the RGB image and depth separately or use
costly post-processing steps, limiting their performances in highly cluttered
scenes and real-time applications. In this work, we present DenseFusion, a
generic framework for estimating 6D pose of a set of known objects from RGB-D
images. DenseFusion is a heterogeneous architecture that processes the two data
sources individually and uses a novel dense fusion network to extract
pixel-wise dense feature embedding, from which the pose is estimated.
Furthermore, we integrate an end-to-end iterative pose refinement procedure
that further improves the pose estimation while achieving near real-time
inference. Our experiments show that our method outperforms state-of-the-art
approaches in two datasets, YCB-Video and LineMOD. We also deploy our proposed
method to a real robot to grasp and manipulate objects based on the estimated
pose.
Identifying and Correcting Label Bias in Machine Learning
Authors: Heinrich Jiang, Ofir Nachum
Date: 2019-01-15 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

Datasets often contain biases which unfairly disadvantage certain groups, and
classifiers trained on such datasets can inherit these biases. In this paper,
we provide a mathematical formulation of how this bias can arise. We do so by
assuming the existence of underlying, unknown, and unbiased labels which are
overwritten by an agent who intends to provide accurate labels but may have
biases against certain groups. Despite the fact that we only observe the biased
labels, we are able to show that the bias may nevertheless be corrected by
re-weighting the data points without changing the labels. We show, with
theoretical guarantees, that training on the re-weighted dataset corresponds to
training on the unobserved but unbiased labels, thus leading to an unbiased
machine learning classifier. Our procedure is fast and robust and can be used
with virtually any learning algorithm. We evaluate on a number of standard
machine learning fairness datasets and a variety of fairness notions, finding
that our method outperforms standard approaches in achieving fair
classification.
Cascade Decoder: A Universal Decoding Method for Biomedical Image Segmentation
Authors: Peixian Liang, Jianxu Chen, Hao Zheng, Lin Yang, Yizhe Zhang, Danny Z. Chen
Date: 2019-01-15 00:00:00
Tasks: Decoder, Image Segmentation, Segmentation, Semantic Segmentation

The Encoder-Decoder architecture is a main stream deep learning model for
biomedical image segmentation. The encoder fully compresses the input and
generates encoded features, and the decoder then produces dense predictions
using encoded features. However, decoders are still under-explored in such
architectures. In this paper, we comprehensively study the state-of-the-art
Encoder-Decoder architectures, and propose a new universal decoder, called
cascade decoder, to improve semantic segmentation accuracy. Our cascade decoder
can be embedded into existing networks and trained altogether in an end-to-end
fashion. The cascade decoder structure aims to conduct more effective decoding
of hierarchically encoded features and is more compatible with common encoders
than the known decoders. We replace the decoders of state-of-the-art models
with our cascade decoder for several challenging biomedical image segmentation
tasks, and the considerable improvements achieved demonstrate the efficacy of
our new decoding method.
Automatic Surface Area and Volume Prediction on Ellipsoidal Ham using Deep Learning
Authors: Y. S. Gan, Sze-Teng Liong, Yen-Chang Huang
Date: 2019-01-15 00:00:00
Tasks: Semantic Segmentation

This paper presents novel methods to predict the surface and volume of the
ham through a camera. This implies that the conventional weight measurement to
obtain in the object's volume can be neglected and hence it is economically
effective. Both of the measurements are obtained in the following two ways:
manually and automatically. The former is assume as the true or exact
measurement and the latter is through a computer vision technique with some
geometrical analysis that includes mathematical derived functions. For the
automatic implementation, most of the existing approaches extract the features
of the food material based on handcrafted features and to the best of our
knowledge this is the first attempt to estimate the surface area and volume on
ham with deep learning features. We address the estimation task with a Mask
Region-based CNN (Mask R-CNN) approach, which well performs the ham detection
and semantic segmentation from a video. The experimental results demonstrate
that the algorithm proposed is robust as promising surface area and volume
estimation are obtained for two angles of the ellipsoidal ham (i.e., horizontal
and vertical positions). Specifically, in the vertical ham point of view, it
achieves an overall accuracy up to 95% whereas the horizontal ham reaches 80%
of accuracy.
Incremental Reading for Question Answering
Authors: Samira Abnar, Tania Bedrax-Weiss, Tom Kwiatkowski, William W. Cohen
Date: 2019-01-15 00:00:00
Tasks: Continual Learning, Question Answering

Any system which performs goal-directed continual learning must not only
learn incrementally but process and absorb information incrementally. Such a
system also has to understand when its goals have been achieved. In this paper,
we consider these issues in the context of question answering. Current
state-of-the-art question answering models reason over an entire passage, not
incrementally. As we will show, naive approaches to incremental reading, such
as restriction to unidirectional language models in the model, perform poorly.
We present extensions to the DocQA [2] model to allow incremental reading
without loss of accuracy. The model also jointly learns to provide the best
answer given the text that is seen so far and predict whether this best-so-far
answer is sufficient.
Unconstrained Church-Turing thesis cannot possibly be true
Authors: Yuri Gurevich
Date: 2019-01-15 00:00:00

The Church-Turing thesis asserts that if a partial strings-to-strings
function is effectively computable then it is computable by a Turing machine.
  In the 1930s, when Church and Turing worked on their versions of the thesis,
there was a robust notion of algorithm. These traditional algorithms are known
also as classical or sequential. In the original thesis, effectively computable
meant computable by an effective classical algorithm. Based on an earlier
axiomatization of classical algorithms, the original thesis was proven in 2008.
  Since the 1930s, the notion of algorithm has changed dramatically. New
species of algorithms have been and are being introduced. We argue that the
generalization of the original thesis, where effectively computable means
computable by an effective algorithm of any species, cannot possibly be true.
Rapid Visual Categorization is not Guided by Early Salience-Based Selection
Authors: John K. Tsotsos, Iuliia Kotseruba, Calden Wloka
Date: 2019-01-15 00:00:00

The current dominant visual processing paradigm in both human and machine research is the feedforward, layered hierarchy of neural-like processing elements. Within this paradigm, visual saliency is seen by many to have a specific role, namely that of early selection. Early selection is thought to enable very fast visual performance by limiting processing to only the most salient candidate portions of an image. This strategy has led to a plethora of saliency algorithms that have indeed improved processing time efficiency in machine algorithms, which in turn have strengthened the suggestion that human vision also employs a similar early selection strategy. However, at least one set of critical tests of this idea has never been performed with respect to the role of early selection in human vision. How would the best of the current saliency models perform on the stimuli used by experimentalists who first provided evidence for this visual processing paradigm? Would the algorithms really provide correct candidate sub-images to enable fast categorization on those same images? Do humans really need this early selection for their impressive performance? Here, we report on a new series of tests of these questions whose results suggest that it is quite unlikely that such an early selection process has any role in human rapid visual categorization.
Combinatorial Sleeping Bandits with Fairness Constraints
Authors: Fengjiao Li, Jia Liu, Bo Ji
Date: 2019-01-15 00:00:00
Tasks: Fairness

The multi-armed bandit (MAB) model has been widely adopted for studying many practical optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with unknown parameters. The goal of the player here is to maximize the cumulative reward in the face of uncertainty. However, the basic MAB model neglects several important factors of the system in many real-world applications, where multiple arms can be simultaneously played and an arm could sometimes be "sleeping". Besides, ensuring fairness is also a key design concern in practice. To that end, we propose a new Combinatorial Sleeping MAB model with Fairness constraints, called CSMAB-F, aiming to address the aforementioned crucial modeling issues. The objective is now to maximize the reward while satisfying the fairness requirement of a minimum selection fraction for each individual arm. To tackle this new problem, we extend an online learning algorithm, UCB, to deal with a critical tradeoff between exploitation and exploration and employ the virtual queue technique to properly handle the fairness constraints. By carefully integrating these two techniques, we develop a new algorithm, called Learning with Fairness Guarantee (LFG), for the CSMAB-F problem. Further, we rigorously prove that not only LFG is feasibility-optimal, but it also has a time-average regret upper bounded by $\frac{N}{2\eta}+\frac{\beta_1\sqrt{mNT\log{T}}+\beta_2 N}{T}$, where N is the total number of arms, m is the maximum number of arms that can be simultaneously played, T is the time horizon, $\beta_1$ and $\beta_2$ are constants, and $\eta$ is a design parameter that we can tune. Finally, we perform extensive simulations to corroborate the effectiveness of the proposed algorithm. Interestingly, the simulation results reveal an important tradeoff between the regret and the speed of convergence to a point satisfying the fairness constraints.
Deep Fusion: An Attention Guided Factorized Bilinear Pooling for Audio-video Emotion Recognition
Authors: Yuanyuan Zhang, Zi-Rui Wang, Jun Du
Date: 2019-01-15 00:00:00
Tasks: Emotion Recognition, Video Emotion Recognition

Automatic emotion recognition (AER) is a challenging task due to the abstract
concept and multiple expressions of emotion. Although there is no consensus on
a definition, human emotional states usually can be apperceived by auditory and
visual systems. Inspired by this cognitive process in human beings, it's
natural to simultaneously utilize audio and visual information in AER. However,
most traditional fusion approaches only build a linear paradigm, such as
feature concatenation and multi-system fusion, which hardly captures complex
association between audio and video. In this paper, we introduce factorized
bilinear pooling (FBP) to deeply integrate the features of audio and video.
Specifically, the features are selected through the embedded attention
mechanism from respective modalities to obtain the emotion-related regions. The
whole pipeline can be completed in a neural network. Validated on the AFEW
database of the audio-video sub-challenge in EmotiW2018, the proposed approach
achieves an accuracy of 62.48%, outperforming the state-of-the-art result.
Optimistic optimization of a Brownian
Authors: Jean-bastien Grill, Michal Valko, Rémi Munos
Date: 2019-01-15 00:00:00
Proceeding: NeurIPS 2018 12

We address the problem of optimizing a Brownian motion. We consider a
(random) realization $W$ of a Brownian motion with input space in $[0,1]$.
Given $W$, our goal is to return an $\epsilon$-approximation of its maximum
using the smallest possible number of function evaluations, the sample
complexity of the algorithm. We provide an algorithm with sample complexity of
order $\log^2(1/\epsilon)$. This improves over previous results of Al-Mharmah
and Calvin (1996) and Calvin et al. (2017) which provided only polynomial
rates. Our algorithm is adaptive---each query depends on previous values---and
is an instance of the optimism-in-the-face-of-uncertainty principle.
A deep learning approach to solar-irradiance forecasting in sky-videos
Authors: Talha A. Siddiqui, Samarth Bharadwaj, Shivkumar Kalyanaraman
Date: 2019-01-15 00:00:00
Tasks: Solar Irradiance Forecasting

Ahead-of-time forecasting of incident solar-irradiance on a panel is
indicative of expected energy yield and is essential for efficient grid
distribution and planning. Traditionally, these forecasts are based on
meteorological physics models whose parameters are tuned by coarse-grained
radiometric tiles sensed from geo-satellites. This research presents a novel
application of deep neural network approach to observe and estimate short-term
weather effects from videos. Specifically, we use time-lapsed videos
(sky-videos) obtained from upward facing wide-lensed cameras (sky-cameras) to
directly estimate and forecast solar irradiance. We introduce and present
results on two large publicly available datasets obtained from weather stations
in two regions of North America using relatively inexpensive optical hardware.
These datasets contain over a million images that span for 1 and 12 years
respectively, the largest such collection to our knowledge. Compared to
satellite based approaches, the proposed deep learning approach significantly
reduces the normalized mean-absolute-percentage error for both nowcasting, i.e.
prediction of the solar irradiance at the instance the frame is captured, as
well as forecasting, ahead-of-time irradiance prediction for a duration for
upto 4 hours.
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
Authors: Yibo Yang, Paris Perdikaris
Date: 2019-01-15 00:00:00
Tasks: Probabilistic Deep Learning, Variational Inference

We present a probabilistic deep learning methodology that enables the
construction of predictive data-driven surrogates for stochastic systems.
Leveraging recent advances in variational inference with implicit
distributions, we put forth a statistical inference framework that enables the
end-to-end training of surrogate models on paired input-output observations
that may be stochastic in nature, originate from different information sources
of variable fidelity, or be corrupted by complex noise processes. The resulting
surrogates can accommodate high-dimensional inputs and outputs and are able to
return predictions with quantified uncertainty. The effectiveness our approach
is demonstrated through a series of canonical studies, including the regression
of noisy data, multi-fidelity modeling of stochastic processes, and uncertainty
propagation in high-dimensional dynamical systems.
Feature Boosting Network For 3D Pose Estimation
Authors: Jun Liu, Henghui Ding, Amir Shahroudy, Ling-Yu Duan, Xudong Jiang, Gang Wang, Alex C. Kot
Date: 2019-01-15 00:00:00
Tasks: 3D Hand Pose Estimation, 3D Pose Estimation, Hand Pose Estimation, Pose Estimation

In this paper, a feature boosting network is proposed for estimating 3D hand pose and 3D body pose from a single RGB image. In this method, the features learned by the convolutional layers are boosted with a new long short-term dependence-aware (LSTD) module, which enables the intermediate convolutional feature maps to perceive the graphical long short-term dependency among different hand (or body) parts using the designed Graphical ConvLSTM. Learning a set of features that are reliable and discriminatively representative of the pose of a hand (or body) part is difficult due to the ambiguities, texture and illumination variation, and self-occlusion in the real application of 3D pose estimation. To improve the reliability of the features for representing each body part and enhance the LSTD module, we further introduce a context consistency gate (CCG) in this paper, with which the convolutional feature maps are modulated according to their consistency with the context representations. We evaluate the proposed method on challenging benchmark datasets for 3D hand pose estimation and 3D full body pose estimation. Experimental results show the effectiveness of our method that achieves state-of-the-art performance on both of the tasks.