pwc-archive/papers-with-abstracts huggingface dataset:
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models
Authors: Hung Vu, Tu Dinh Nguyen, Dinh Phung
Date: 2018-05-03 00:00:00
Tasks: Anomaly Detection, Clustering, Event Detection, Feature Engineering, Representation Learning

Abnormal event detection is one of the important objectives in research and
practical applications of video surveillance. However, there are still three
challenging problems for most anomaly detection systems in practical setting:
limited labeled data, ambiguous definition of "abnormal" and expensive feature
engineering steps. This paper introduces a unified detection framework to
handle these challenges using energy-based models, which are powerful tools for
unsupervised representation learning. Our proposed models are firstly trained
on unlabeled raw pixels of image frames from an input video rather than
hand-crafted visual features; and then identify the locations of abnormal
objects based on the errors between the input video and its reconstruction
produced by the models. To handle video stream, we develop an online version of
our framework, wherein the model parameters are updated incrementally with the
image frames arriving on the fly. Our experiments show that our detectors,
using Restricted Boltzmann Machines (RBMs) and Deep Boltzmann Machines (DBMs)
as core modules, achieve superior anomaly detection performance to unsupervised
baselines and obtain accuracy comparable with the state-of-the-art approaches
when evaluating at the pixel-level. More importantly, we discover that our
system trained with DBMs is able to simultaneously perform scene clustering and
scene reconstruction. This capacity not only distinguishes our method from
other existing detectors but also offers a unique tool to investigate and
understand how the model works.
A Scalable Discrete-Time Survival Model for Neural Networks
Authors: Michael F. Gensheimer, Balasubramanian Narasimhan
Date: 2018-05-02 00:00:00
Tasks: Survival Analysis

There is currently great interest in applying neural networks to prediction
tasks in medicine. It is important for predictive models to be able to use
survival data, where each patient has a known follow-up time and
event/censoring indicator. This avoids information loss when training the model
and enables generation of predicted survival curves. In this paper, we describe
a discrete-time survival model that is designed to be used with neural
networks, which we refer to as Nnet-survival. The model is trained with the
maximum likelihood method using minibatch stochastic gradient descent (SGD).
The use of SGD enables rapid convergence and application to large datasets that
do not fit in memory. The model is flexible, so that the baseline hazard rate
and the effect of the input data on hazard probability can vary with follow-up
time. It has been implemented in the Keras deep learning framework, and source
code for the model and several examples is available online. We demonstrate the
performance of the model on both simulated and real data and compare it to
existing models Cox-nnet and Deepsurv.
Tensor Completion Algorithms in Big Data Analytics
Authors: Qingquan Song, Hancheng Ge, James Caverlee, Xia Hu
Date: 2017-11-28 00:00:00

Tensor completion is a problem of filling the missing or unobserved entries
of partially observed tensors. Due to the multidimensional character of tensors
in describing complex datasets, tensor completion algorithms and their
applications have received wide attention and achievement in areas like data
mining, computer vision, signal processing, and neuroscience. In this survey,
we provide a modern overview of recent advances in tensor completion algorithms
from the perspective of big data analytics characterized by diverse variety,
large volume, and high velocity. We characterize these advances from four
perspectives: general tensor completion algorithms, tensor completion with
auxiliary information (variety), scalable tensor completion algorithms
(volume), and dynamic tensor completion algorithms (velocity). Further, we
identify several tensor completion applications on real-world data-driven
problems and present some common experimental frameworks popularized in the
literature. Our goal is to summarize these popular methods and introduce them
to researchers and practitioners for promoting future research and
applications. We conclude with a discussion of key challenges and promising
research directions in this community for future exploration.
Stack-Pointer Networks for Dependency Parsing
Authors: Xuezhe Ma, Zecong Hu, Jingzhou Liu, Nanyun Peng, Graham Neubig, Eduard Hovy
Date: 2018-05-03 00:00:00
Proceeding: ACL 2018 7
Tasks: Dependency Parsing, Sentence

We introduce a novel architecture for dependency parsing: \emph{stack-pointer
networks} (\textbf{\textsc{StackPtr}}). Combining pointer
networks~\citep{vinyals2015pointer} with an internal stack, the proposed model
first reads and encodes the whole sentence, then builds the dependency tree
top-down (from root-to-leaf) in a depth-first fashion. The stack tracks the
status of the depth-first search and the pointer networks select one child for
the word at the top of the stack at each step. The \textsc{StackPtr} parser
benefits from the information of the whole sentence and all previously derived
subtree structures, and removes the left-to-right restriction in classical
transition-based parsers. Yet, the number of steps for building any (including
non-projective) parse tree is linear in the length of the sentence just as
other transition-based parsers, yielding an efficient decoding algorithm with
$O(n^2)$ time complexity. We evaluate our model on 29 treebanks spanning 20
languages and different dependency annotation schemas, and achieve
state-of-the-art performance on 21 of them.
Transformation Networks for Target-Oriented Sentiment Classification
Authors: Xin Li, Lidong Bing, Wai Lam, Bei Shi
Date: 2018-05-03 00:00:00
Proceeding: ACL 2018 7
Tasks: Aspect-Based Sentiment Analysis (ABSA), Classification, General Classification, Sentence, Sentiment Classification

Target-oriented sentiment classification aims at classifying sentiment
polarities over individual opinion targets in a sentence. RNN with attention
seems a good fit for the characteristics of this task, and indeed it achieves
the state-of-the-art performance. After re-examining the drawbacks of attention
mechanism and the obstacles that block CNN to perform well in this
classification task, we propose a new model to overcome these issues. Instead
of attention, our model employs a CNN layer to extract salient features from
the transformed word representations originated from a bi-directional RNN
layer. Between the two layers, we propose a component to generate
target-specific representations of words in the sentence, meanwhile incorporate
a mechanism for preserving the original contextual information from the RNN
layer. Experiments show that our model achieves a new state-of-the-art
performance on a few benchmarks.
Perceptually Optimized Generative Adversarial Network for Single Image Dehazing
Authors: Yixin Du, Xin Li
Date: 2018-05-03 00:00:00
Tasks: Denoising, Generative Adversarial Network, Image Dehazing, Single Image Dehazing

Existing approaches towards single image dehazing including both model-based
and learning-based heavily rely on the estimation of so-called transmission
maps. Despite its conceptual simplicity, using transmission maps as an
intermediate step often makes it more difficult to optimize the perceptual
quality of reconstructed images. To overcome this weakness, we propose a direct
deep learning approach toward image dehazing bypassing the step of transmission
map estimation and facilitating end-to-end perceptual optimization. Our
technical contributions are mainly three-fold. First, based on the analogy
between dehazing and denoising, we propose to directly learn a nonlinear
mapping from the space of degraded images to that of haze-free ones via
recursive deep residual learning; Second, inspired by the success of generative
adversarial networks (GAN), we propose to optimize the perceptual quality of
dehazed images by introducing a discriminator and a loss function adaptive to
hazy conditions; Third, we propose to remove notorious halo-like artifacts at
large scene depth discontinuities by a novel application of guided filtering.
Extensive experimental results have shown that the subjective qualities of
dehazed images by the proposed perceptually optimized GAN (POGAN) are often
more favorable than those by existing state-of-the-art approaches especially
when hazy condition varies.
Scalable Semantic Querying of Text
Authors: Xiaolan Wang, Aaron Feng, Behzad Golshan, Alon Halevy, George Mihaila, Hidekazu Oiwa, Wang-Chiew Tan
Date: 2018-05-03 00:00:00
Tasks: Articles

We present the KOKO system that takes declarative information extraction to a
new level by incorporating advances in natural language processing techniques
in its extraction language. KOKO is novel in that its extraction language
simultaneously supports conditions on the surface of the text and on the
structure of the dependency parse tree of sentences, thereby allowing for more
refined extractions. KOKO also supports conditions that are forgiving to
linguistic variation of expressing concepts and allows to aggregate evidence
from the entire document in order to filter extractions.
  To scale up, KOKO exploits a multi-indexing scheme and heuristics for
efficient extractions. We extensively evaluate KOKO over publicly available
text corpora. We show that KOKO indices take up the smallest amount of space,
are notably faster and more effective than a number of prior indexing schemes.
Finally, we demonstrate KOKO's scale up on a corpus of 5 million Wikipedia
articles.
Exploration of Numerical Precision in Deep Neural Networks
Authors: Zhaoqi Li, Yu Ma, Catalina Vajiac, Yunkai Zhang
Date: 2018-05-03 00:00:00

Reduced numerical precision is a common technique to reduce computational
cost in many Deep Neural Networks (DNNs). While it has been observed that DNNs
are resilient to small errors and noise, no general result exists that is
capable of predicting a given DNN system architecture's sensitivity to reduced
precision. In this project, we emulate arbitrary bit-width using a specified
floating-point representation with a truncation method, which is applied to the
neural network after each batch. We explore the impact of several model
parameters on the network's training accuracy and show results on the MNIST
dataset. We then present a preliminary theoretical investigation of the error
scaling in both forward and backward propagations. We end with a discussion of
the implications of these results as well as the potential for generalization
to other network architectures.
Real-time Air Pollution prediction model based on Spatiotemporal Big data
Authors: V. Duc Le, Sang Kyun Cha
Date: 2018-04-05 00:00:00
Tasks: Air Pollution Prediction, Prediction, Time Series, Time Series Analysis

Air pollution is one of the most concerns for urban areas. Many countries
have constructed monitoring stations to hourly collect pollution values.
Recently, there is a research in Daegu city, Korea for real-time air quality
monitoring via sensors installed on taxis running across the whole city. The
collected data is huge (1-second interval) and in both Spatial and Temporal
format. In this paper, based on this spatiotemporal Big data, we propose a
real-time air pollution prediction model based on Convolutional Neural Network
(CNN) algorithm for image-like Spatial distribution of air pollution. Regarding
to Temporal information in the data, we introduce a combination of a Long
Short-Term Memory (LSTM) unit for time series data and a Neural Network model
for other air pollution impact factors such as weather conditions to build a
hybrid prediction model. This model is simple in architecture but still brings
good prediction ability.
What you can cram into a single vector: Probing sentence embeddings for linguistic properties
Authors: Alexis Conneau, German Kruszewski, Guillaume Lample, Loïc Barrault, Marco Baroni
Date: 2018-05-03 00:00:00
Tasks: General Classification, Sentence, Sentence Classification, Sentence Embeddings

Although much effort has recently been devoted to training high-quality
sentence embeddings, we still have a poor understanding of what they are
capturing. "Downstream" tasks, often based on sentence classification, are
commonly used to evaluate the quality of sentence representations. The
complexity of the tasks makes it however difficult to infer what kind of
information is present in the representations. We introduce here 10 probing
tasks designed to capture simple linguistic features of sentences, and we use
them to study embeddings generated by three different encoders trained in eight
distinct ways, uncovering intriguing properties of both encoders and training
methods.
Select, Attend, and Transfer: Light, Learnable Skip Connections
Authors: Saeid Asgari Taghanaki, Aicha Bentaieb, Anmol Sharma, S. Kevin Zhou, Yefeng Zheng, Bogdan Georgescu, Puneet Sharma, Sasa Grbic, Zhoubing Xu, Dorin Comaniciu, Ghassan Hamarneh
Date: 2018-04-14 00:00:00
Tasks: Decoder, Segmentation

Skip connections in deep networks have improved both segmentation and
classification performance by facilitating the training of deeper network
architectures, and reducing the risks for vanishing gradients. They equip
encoder-decoder-like networks with richer feature representations, but at the
cost of higher memory usage, computation, and possibly resulting in
transferring non-discriminative feature maps. In this paper, we focus on
improving skip connections used in segmentation networks (e.g., U-Net, V-Net,
and The One Hundred Layers Tiramisu (DensNet) architectures). We propose light,
learnable skip connections which learn to first select the most discriminative
channels and then attend to the most discriminative regions of the selected
feature maps. The output of the proposed skip connections is a unique feature
map which not only reduces the memory usage and network parameters to a high
extent, but also improves segmentation accuracy. We evaluate the proposed
method on three different 2D and volumetric datasets and demonstrate that the
proposed light, learnable skip connections can outperform the traditional heavy
skip connections in terms of segmentation accuracy, memory usage, and number of
network parameters.
Multimodal Emotion Recognition for One-Minute-Gradual Emotion Challenge
Authors: Ziqi Zheng, Chenjie Cao, Xingwei Chen, Guoqiang Xu
Date: 2018-05-03 00:00:00
Tasks: Emotion Recognition, Multimodal Emotion Recognition

The continuous dimensional emotion modelled by arousal and valence can depict
complex changes of emotions. In this paper, we present our works on arousal and
valence predictions for One-Minute-Gradual (OMG) Emotion Challenge. Multimodal
representations are first extracted from videos using a variety of acoustic,
video and textual models and support vector machine (SVM) is then used for
fusion of multimodal signals to make final predictions. Our solution achieves
Concordant Correlation Coefficient (CCC) scores of 0.397 and 0.520 on arousal
and valence respectively for the validation dataset, which outperforms the
baseline systems with the best CCC scores of 0.15 and 0.23 on arousal and
valence by a large margin.
Toward Multimodal Image-to-Image Translation
Authors: Jun-Yan Zhu, Richard Zhang, Deepak Pathak, Trevor Darrell, Alexei A. Efros, Oliver Wang, Eli Shechtman
Date: 2017-11-30 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Diversity, Image-to-Image Translation, Translation

Many image-to-image translation problems are ambiguous, as a single input
image may correspond to multiple possible outputs. In this work, we aim to
model a \emph{distribution} of possible outputs in a conditional generative
modeling setting. The ambiguity of the mapping is distilled in a
low-dimensional latent vector, which can be randomly sampled at test time. A
generator learns to map the given input, combined with this latent code, to the
output. We explicitly encourage the connection between output and the latent
code to be invertible. This helps prevent a many-to-one mapping from the latent
code to the output during training, also known as the problem of mode collapse,
and produces more diverse results. We explore several variants of this approach
by employing different training objectives, network architectures, and methods
of injecting the latent code. Our proposed method encourages bijective
consistency between the latent encoding and output modes. We present a
systematic comparison of our method and other variants on both perceptual
realism and diversity.
Vision-based Structural Inspection using Multiscale Deep Convolutional Neural Networks
Authors: Vedhus Hoskere, Yasutaka Narazaki, Tu Hoang, BillieF Spencer Jr
Date: 2018-05-02 00:00:00
Tasks: General Classification

Current methods of practice for inspection of civil infrastructure typically
involve visual assessments conducted manually by trained inspectors. For
post-earthquake structural inspections, the number of structures to be
inspected often far exceeds the capability of the available inspectors. The
labor intensive and time consuming natures of manual inspection have engendered
research into development of algorithms for automated damage identification
using computer vision techniques. In this paper, a novel damage localization
and classification technique based on a state of the art computer vision
algorithm is presented to address several key limitations of current computer
vision techniques. The proposed algorithm carries out a pixel-wise
classification of each image at multiple scales using a deep convolutional
neural network and can recognize 6 different types of damage. The resulting
output is a segmented image where the portion of the image representing damage
is outlined and classified as one of the trained damage categories. The
proposed method is evaluated in terms of pixel accuracy and the application of
the method to real world images is shown.
Automatic Coding for Neonatal Jaundice From Free Text Data Using Ensemble Methods
Authors: Scott Werwath
Date: 2018-05-02 00:00:00
Tasks: BIG-bench Machine Learning, Classification, feature selection, General Classification

This study explores the creation of a machine learning model to automatically
identify whether a Neonatal Intensive Care Unit (NICU) patient was diagnosed
with neonatal jaundice during a particular hospitalization based on their
associated clinical notes. We develop a number of techniques for text
preprocessing and feature selection and compare the effectiveness of different
classification models. We show that using ensemble decision tree
classification, both with AdaBoost and with bagging, outperforms support vector
machines (SVM), the current state-of-the-art technique for neonatal jaundice
coding.
Constituency Parsing with a Self-Attentive Encoder
Authors: Nikita Kitaev, Dan Klein
Date: 2018-05-02 00:00:00
Proceeding: ACL 2018 7
Tasks: Constituency Parsing, Sentence

We demonstrate that replacing an LSTM encoder with a self-attentive
architecture can lead to improvements to a state-of-the-art discriminative
constituency parser. The use of attention makes explicit the manner in which
information is propagated between different locations in the sentence, which we
use to both analyze our model and propose potential improvements. For example,
we find that separating positional and content information in the encoder can
lead to improved parsing accuracy. Additionally, we evaluate different
approaches for lexical representation. Our parser achieves new state-of-the-art
results for single models trained on the Penn Treebank: 93.55 F1 without the
use of any external data, and 95.13 F1 when using pre-trained word
representations. Our parser also outperforms the previous best-published
accuracy figures on 8 of the 9 languages in the SPMRL dataset.
Robustness of sentence length measures in written texts
Authors: Denner S. Vieira, Sergio Picoli, Renio S. Mendes
Date: 2018-05-02 00:00:00
Tasks: Lemmatization, Sentence, Time Series, Time Series Analysis

Hidden structural patterns in written texts have been subject of considerable
research in the last decades. In particular, mapping a text into a time series
of sentence lengths is a natural way to investigate text structure. Typically,
sentence length has been quantified by using measures based on the number of
words and the number of characters, but other variations are possible. To
quantify the robustness of different sentence length measures, we analyzed a
database containing about five hundred books in English. For each book, we
extracted six distinct measures of sentence length, including number of words
and number of characters (taking into account lemmatization and stop words
removal). We compared these six measures for each book by using i) Pearson's
coefficient to investigate linear correlations; ii) Kolmogorov--Smirnov test to
compare distributions; and iii) detrended fluctuation analysis (DFA) to
quantify auto-correlations. We have found that all six measures exhibit very
similar behavior, suggesting that sentence length is a robust measure related
to text structure.
Large-Scale Unsupervised Deep Representation Learning for Brain Structure
Authors: Ayush Jaiswal, Dong Guo, Cauligi S. Raghavendra, Paul Thompson
Date: 2018-05-02 00:00:00
Tasks: Representation Learning

Machine Learning (ML) is increasingly being used for computer aided diagnosis
of brain related disorders based on structural magnetic resonance imaging (MRI)
data. Most of such work employs biologically and medically meaningful
hand-crafted features calculated from different regions of the brain. The
construction of such highly specialized features requires a considerable amount
of time, manual oversight and careful quality control to ensure the absence of
errors in the computational process. Recent advances in Deep Representation
Learning have shown great promise in extracting highly non-linear and
information-rich features from data. In this paper, we present a novel
large-scale deep unsupervised approach to learn generic feature representations
of structural brain MRI scans, which requires no specialized domain knowledge
or manual intervention. Our method produces low-dimensional representations of
brain structure, which can be used to reconstruct brain images with very low
error and exhibit performance comparable to FreeSurfer features on various
classification tasks.
RF-PUF: IoT Security Enhancement through Authentication of Wireless Nodes using In-situ Machine Learning
Authors: Baibhab Chatterjee, Debayan Das, Shreyas Sen
Date: 2018-05-02 00:00:00
Tasks: BIG-bench Machine Learning

Physical unclonable functions (PUF) in silicon exploit die-to-die
manufacturing variations during fabrication for uniquely identifying each die.
Since it is practically a hard problem to recreate exact silicon features
across dies, a PUFbased authentication system is robust, secure and
cost-effective, as long as bias removal and error correction are taken into
account. In this work, we utilize the effects of inherent process variation on
analog and radio-frequency (RF) properties of multiple wireless transmitters
(Tx) in a sensor network, and detect the features at the receiver (Rx) using a
deep neural network based framework. The proposed mechanism/framework, called
RF-PUF, harnesses already existing RF communication hardware and does not
require any additional PUF-generation circuitry in the Tx for practical
implementation. Simulation results indicate that the RF-PUF framework can
distinguish up to 10000 transmitters (with standard foundry defined variations
for a 65 nm process, leading to non-idealities such as LO offset and I-Q
imbalance) under varying channel conditions, with a probability of false
detection < 10e-3