pwc-archive/papers-with-abstracts huggingface dataset:
Image Segmentation and Processing for Efficient Parking Space Analysis
Authors: Chetan Sai Tutika, Charan Vallapaneni, Karthik R, Bharath KP, N Ruban Rajesh Kumar Muthu
Date: 2018-03-13 00:00:00
Tasks: Contour Detection, Image Segmentation, Semantic Segmentation

In this paper, we develop a method to detect vacant parking spaces in an
environment with unclear segments and contours with the help of MATLAB image
processing capabilities. Due to the anomalies present in the parking spaces,
such as uneven illumination, distorted slot lines and overlapping of cars. The
present-day conventional algorithms have difficulties processing the image for
accurate results. The algorithm proposed uses a combination of image
pre-processing and false contour detection techniques to improve the detection
efficiency. The proposed method also eliminates the need to employ individual
sensors to detect a car, instead uses real-time static images to consider a
group of slots together, instead of the usual single slot method. This greatly
decreases the expenses required to design an efficient parking system. We
compare the performance of our algorithm to that of other techniques. These
comparisons show that the proposed algorithm can detect the vacancies in the
parking spots while ignoring the false data and other distortions.
ThUnderVolt: Enabling Aggressive Voltage Underscaling and Timing Error Resilience for Energy Efficient Deep Neural Network Accelerators
Authors: Jeff Zhang, Kartheek Rangineni, Zahra Ghodsi, Siddharth Garg
Date: 2018-02-11 00:00:00
Tasks: General Classification

Hardware accelerators are being increasingly deployed to boost the
performance and energy efficiency of deep neural network (DNN) inference. In
this paper we propose Thundervolt, a new framework that enables aggressive
voltage underscaling of high-performance DNN accelerators without compromising
classification accuracy even in the presence of high timing error rates. Using
post-synthesis timing simulations of a DNN accelerator modeled on the Google
TPU, we show that Thundervolt enables between 34%-57% energy savings on
state-of-the-art speech and image recognition benchmarks with less than 1% loss
in classification accuracy and no performance loss. Further, we show that
Thundervolt is synergistic with and can further increase the energy efficiency
of commonly used run-time DNN pruning techniques like Zero-Skip.
Target Driven Instance Detection
Authors: Phil Ammirato, Cheng-Yang Fu, Mykhailo Shvets, Jana Kosecka, Alexander C. Berg
Date: 2018-03-13 00:00:00
Tasks: Object

While state-of-the-art general object detectors are getting better and better, there are not many systems specifically designed to take advantage of the instance detection problem. For many applications, such as household robotics, a system may need to recognize a few very specific instances at a time. Speed can be critical in these applications, as can the need to recognize previously unseen instances. We introduce a Target Driven Instance Detector(TDID), which modifies existing general object detectors for the instance recognition setting. TDID not only improves performance on instances seen during training, with a fast runtime, but is also able to generalize to detect novel instances.
CapsuleGAN: Generative Adversarial Capsule Network
Authors: Ayush Jaiswal, Wael Abd-Almageed, Yue Wu, Premkumar Natarajan
Date: 2018-02-17 00:00:00
Tasks: General Classification, Generative Adversarial Network, image-classification, Image Classification, Semi-Supervised Image Classification

We present Generative Adversarial Capsule Network (CapsuleGAN), a framework
that uses capsule networks (CapsNets) instead of the standard convolutional
neural networks (CNNs) as discriminators within the generative adversarial
network (GAN) setting, while modeling image data. We provide guidelines for
designing CapsNet discriminators and the updated GAN objective function, which
incorporates the CapsNet margin loss, for training CapsuleGAN models. We show
that CapsuleGAN outperforms convolutional-GAN at modeling image data
distribution on MNIST and CIFAR-10 datasets, evaluated on the generative
adversarial metric and at semi-supervised image classification.
Fusing Hierarchical Convolutional Features for Human Body Segmentation and Clothing Fashion Classification
Authors: Zheng Zhang, Chengfang Song, Qin Zou
Date: 2018-03-09 00:00:00
Tasks: Classification, General Classification, Segmentation

The clothing fashion reflects the common aesthetics that people share with
each other in dressing. To recognize the fashion time of a clothing is
meaningful for both an individual and the industry. In this paper, under the
assumption that the clothing fashion changes year by year, the fashion-time
recognition problem is mapped into a clothing-fashion classification problem.
Specifically, a novel deep neural network is proposed which achieves accurate
human body segmentation by fusing multi-scale convolutional features in a fully
convolutional network, and then feature learning and fashion classification are
performed on the segmented parts avoiding the influence of image background. In
the experiments, 9,339 fashion images from 8 continuous years are collected for
performance evaluation. The results demonstrate the effectiveness of the
proposed body segmentation and fashion classification methods.
Automatic Detection of Online Jihadist Hate Speech
Authors: Tom De Smedt, Guy De Pauw, Pieter Van Ostaeyen
Date: 2018-03-13 00:00:00
Tasks: BIG-bench Machine Learning

We have developed a system that automatically detects online jihadist hate
speech with over 80% accuracy, by using techniques from Natural Language
Processing and Machine Learning. The system is trained on a corpus of 45,000
subversive Twitter messages collected from October 2014 to December 2016. We
present a qualitative and quantitative analysis of the jihadist rhetoric in the
corpus, examine the network of Twitter users, outline the technical procedure
used to train the system, and discuss examples of use.
Learning Scene-specific Object Detectors Based on a Generative-Discriminative Model with Minimal Supervision
Authors: Dapeng Luo, Zhipeng Zeng, Nong Sang, Xiang Wu, Longsheng Wei, Quanzheng Mou, Jun Cheng, Chen Luo
Date: 2016-11-12 00:00:00
Tasks: Object, object-detection, Object Detection, Self-Learning

One object class may show large variations due to diverse illuminations,
backgrounds and camera viewpoints. Traditional object detection methods often
perform worse under unconstrained video environments. To address this problem,
many modern approaches model deep hierarchical appearance representations for
object detection. Most of these methods require a timeconsuming training
process on large manual labelling sample set. In this paper, the proposed
framework takes a remarkably different direction to resolve the multi-scene
detection problem in a bottom-up fashion. First, a scene-specific objector is
obtained from a fully autonomous learning process triggered by marking several
bounding boxes around the object in the first video frame via a mouse. Here the
human labeled training data or a generic detector are not needed. Second, this
learning process is conveniently replicated many times in different
surveillance scenes and results in particular detectors under various camera
viewpoints. Thus, the proposed framework can be employed in multi-scene object
detection applications with minimal supervision. Obviously, the initial
scene-specific detector, initialized by several bounding boxes, exhibits poor
detection performance and is difficult to improve with traditional online
learning algorithm. Consequently, we propose Generative-Discriminative model to
partition detection response space and assign each partition an individual
descriptor that progressively achieves high classification accuracy. A novel
online gradual optimized process is proposed to optimize the
Generative-Discriminative model and focus on the hard samples.Experimental
results on six video datasets show our approach achieves comparable performance
to robust supervised methods, and outperforms the state of the art
self-learning methods under varying imaging conditions.
Interpretability via Model Extraction
Authors: Osbert Bastani, Carolyn Kim, Hamsa Bastani
Date: 2017-06-29 00:00:00
Tasks: BIG-bench Machine Learning, model, Model extraction, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The ability to interpret machine learning models has become increasingly
important now that machine learning is used to inform consequential decisions.
We propose an approach called model extraction for interpreting complex,
blackbox models. Our approach approximates the complex model using a much more
interpretable model; as long as the approximation quality is good, then
statistical properties of the complex model are reflected in the interpretable
model. We show how model extraction can be used to understand and debug random
forests and neural nets trained on several datasets from the UCI Machine
Learning Repository, as well as control policies learned for several classical
reinforcement learning problems.
Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation
Authors: Alistair Shilton, Sunil Gupta, Santu Rana, Pratibha Vellanki, Cheng Li, Laurence Park, Svetha Venkatesh, Alessandra Sutti, David Rubin, Thomas Dorin, Alireza Vahid, Murray Height
Date: 2018-02-15 00:00:00
Tasks: Bayesian Optimisation, Experimental Design

The paper presents a novel approach to direct covariance function learning
for Bayesian optimisation, with particular emphasis on experimental design
problems where an existing corpus of condensed knowledge is present. The method
presented borrows techniques from reproducing kernel Banach space theory
(specifically m-kernels) and leverages them to convert (or re-weight) existing
covariance functions into new, problem-specific covariance functions. The key
advantage of this approach is that rather than relying on the user to manually
select (with some hyperparameter tuning and experimentation) an appropriate
covariance function it constructs the covariance function to specifically match
the problem at hand. The technique is demonstrated on two real-world problems -
specifically alloy design and short-polymer fibre manufacturing - as well as a
selected test function.
Coregionalised Locomotion Envelopes - A Qualitative Approach
Authors: Neil Dhir, Houman Dallali, Mo Rastgaar
Date: 2018-03-13 00:00:00
Tasks: regression, Transfer Learning

'Sharing of statistical strength' is a phrase often employed in machine
learning and signal processing. In sensor networks, for example, missing
signals from certain sensors may be predicted by exploiting their correlation
with observed signals acquired from other sensors. For humans, our hands move
synchronously with our legs, and we can exploit these implicit correlations for
predicting new poses and for generating new natural-looking walking sequences.
We can also go much further and exploit this form of transfer learning, to
develop new control schemas for robust control of rehabilitation robots. In
this short paper we introduce coregionalised locomotion envelopes - a method
for multi-dimensional manifold regression, on human locomotion variates. Herein
we render a qualitative description of this method.
It was the training data pruning too!
Authors: Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan, Kedar Dhamdhere
Date: 2018-03-12 00:00:00
Tasks: Question Answering

We study the current best model (KDG) for question answering on tabular data
evaluated over the WikiTableQuestions dataset. Previous ablation studies
performed against this model attributed the model's performance to certain
aspects of its architecture. In this paper, we find that the model's
performance also crucially depends on a certain pruning of the data used to
train the model. Disabling the pruning step drops the accuracy of the model
from 43.3% to 36.3%. The large impact on the performance of the KDG model
suggests that the pruning may be a useful pre-processing step in training other
semantic parsers as well.
Improving Object Localization with Fitness NMS and Bounded IoU Loss
Authors: Lachlan Tychsen-Smith, Lars Petersson
Date: 2017-11-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Clustering, Object Localization

We demonstrate that many detection methods are designed to identify only a
sufficently accurate bounding box, rather than the best available one. To
address this issue we propose a simple and fast modification to the existing
methods called Fitness NMS. This method is tested with the DeNet model and
obtains a significantly improved MAP at greater localization accuracies without
a loss in evaluation rate, and can be used in conjunction with Soft NMS for
additional improvements. Next we derive a novel bounding box regression loss
based on a set of IoU upper bounds that better matches the goal of IoU
maximization while still providing good convergence properties. Following these
novelties we investigate RoI clustering schemes for improving evaluation rates
for the DeNet wide model variants and provide an analysis of localization
performance at various input image dimensions. We obtain a MAP of 33.6%@79Hz
and 41.8%@5Hz for MSCOCO and a Titan X (Maxwell). Source code available from:
https://github.com/lachlants/denet
COPA: Constrained PARAFAC2 for Sparse & Large Datasets
Authors: Ardavan Afshar, Ioakeim Perros, Evangelos E. Papalexakis, Elizabeth Searles, Joyce Ho, Jimeng Sun
Date: 2018-03-12 00:00:00

PARAFAC2 has demonstrated success in modeling irregular tensors, where the
tensor dimensions vary across one of the modes. An example scenario is modeling
treatments across a set of patients with the varying number of medical
encounters over time. Despite recent improvements on unconstrained PARAFAC2,
its model factors are usually dense and sensitive to noise which limits their
interpretability. As a result, the following open challenges remain: a) various
modeling constraints, such as temporal smoothness, sparsity and non-negativity,
are needed to be imposed for interpretable temporal modeling and b) a scalable
approach is required to support those constraints efficiently for large
datasets. To tackle these challenges, we propose a {\it CO}nstrained {\it
PA}RAFAC2 (COPA) method, which carefully incorporates optimization constraints
such as temporal smoothness, sparsity, and non-negativity in the resulting
factors. To efficiently support all those constraints, COPA adopts a hybrid
optimization framework using alternating optimization and alternating direction
method of multiplier (AO-ADMM). As evaluated on large electronic health record
(EHR) datasets with hundreds of thousands of patients, COPA achieves
significant speedups (up to 36 times faster) over prior PARAFAC2 approaches
that only attempt to handle a subset of the constraints that COPA enables.
Overall, our method outperforms all the baselines attempting to handle a subset
of the constraints in terms of speed, while achieving the same level of
accuracy. Through a case study on temporal phenotyping of medically complex
children, we demonstrate how the constraints imposed by COPA reveal concise
phenotypes and meaningful temporal profiles of patients. The clinical
interpretation of both the phenotypes and the temporal profiles was confirmed
by a medical expert.
Compact Convolutional Neural Networks for Classification of Asynchronous Steady-state Visual Evoked Potentials
Authors: Nicholas R. Waytowich, Vernon Lawhern, Javier O. Garcia, Jennifer Cummings, Josef Faller, Paul Sajda, Jean M. Vettel
Date: 2018-03-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification, SSVEP

Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from
the parietal and occipital regions of the brain that are evoked from flickering
visual stimuli. SSVEPs are robust signals measurable in the
electroencephalogram (EEG) and are commonly used in brain-computer interfaces
(BCIs). However, methods for high-accuracy decoding of SSVEPs usually require
hand-crafted approaches that leverage domain-specific knowledge of the stimulus
signals, such as specific temporal frequencies in the visual stimuli and their
relative spatial arrangement. When this knowledge is unavailable, such as when
SSVEP signals are acquired asynchronously, such approaches tend to fail. In
this paper, we show how a compact convolutional neural network (Compact-CNN),
which only requires raw EEG signals for automatic feature extraction, can be
used to decode signals from a 12-class SSVEP dataset without the need for any
domain-specific knowledge or calibration data. We report across subject mean
accuracy of approximately 80% (chance being 8.3%) and show this is
substantially better than current state-of-the-art hand-crafted approaches
using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, we
analyze our Compact-CNN to examine the underlying feature representation,
discovering that the deep learner extracts additional phase and amplitude
related features associated with the structure of the dataset. We discuss how
our Compact-CNN shows promise for BCI applications that allow users to freely
gaze/attend to any stimulus at any time (e.g., asynchronous BCI) as well as
provides a method for analyzing SSVEP signals in a way that might augment our
understanding about the basic processing in the visual cortex.
Learning to recognize Abnormalities in Chest X-Rays with Location-Aware Dense Networks
Authors: Sebastian Guendel, Sasa Grbic, Bogdan Georgescu, Kevin Zhou, Ludwig Ritschl, Andreas Meier, Dorin Comaniciu
Date: 2018-03-12 00:00:00
Tasks: Benchmarking

Chest X-ray is the most common medical imaging exam used to assess multiple
pathologies. Automated algorithms and tools have the potential to support the
reading workflow, improve efficiency, and reduce reading errors. With the
availability of large scale data sets, several methods have been proposed to
classify pathologies on chest X-ray images. However, most methods report
performance based on random image based splitting, ignoring the high
probability of the same patient appearing in both training and test set. In
addition, most methods fail to explicitly incorporate the spatial information
of abnormalities or utilize the high resolution images. We propose a novel
approach based on location aware Dense Networks (DNetLoc), whereby we
incorporate both high-resolution image data and spatial information for
abnormality classification. We evaluate our method on the largest data set
reported in the community, containing a total of 86,876 patients and 297,541
chest X-ray images. We achieve (i) the best average AUC score for published
training and test splits on the single benchmarking data set (ChestX-Ray14),
and (ii) improved AUC scores when the pathology location information is
explicitly used. To foster future research we demonstrate the limitations of
the current benchmarking setup and provide new reference patient-wise splits
for the used data sets. This could support consistent and meaningful
benchmarking of future methods on the largest publicly available data sets.
Onion-Peeling Outlier Detection in 2-D data Sets
Authors: Archit Harsh, John E. Ball, Pan Wei
Date: 2018-03-12 00:00:00
Tasks: Clustering, Fraud Detection, Intrusion Detection, Network Intrusion Detection, Outlier Detection

Outlier Detection is a critical and cardinal research task due its array of
applications in variety of domains ranging from data mining, clustering,
statistical analysis, fraud detection, network intrusion detection and
diagnosis of diseases etc. Over the last few decades, distance-based outlier
detection algorithms have gained significant reputation as a viable alternative
to the more traditional statistical approaches due to their scalable,
non-parametric and simple implementation. In this paper, we present a modified
onion peeling (Convex hull) genetic algorithm to detect outliers in a Gaussian
2-D point data set. We present three different scenarios of outlier detection
using a) Euclidean Distance Metric b) Standardized Euclidean Distance Metric
and c) Mahalanobis Distance Metric. Finally, we analyze the performance and
evaluate the results.
Measuring Conflict in a Multi-Source Environment as a Normal Measure
Authors: Pan Wei, John E. Ball, Derek T. Anderson, Archit Harsh, Christopher Archibald
Date: 2018-03-12 00:00:00

In a multi-source environment, each source has its own credibility. If there
is no external knowledge about credibility then we can use the information
provided by the sources to assess their credibility. In this paper, we propose
a way to measure conflict in a multi-source environment as a normal measure. We
examine our algorithm using three simulated examples of increasing conflict and
one experimental example. The results demonstrate that the proposed measure can
represent conflict in a meaningful way similar to what a human might expect and
from it we can identify conflict within our sources.
Multi-Sensor Conflict Measurement and Information Fusion
Authors: Pan Wei, John E. Ball, Derek T. Anderson
Date: 2018-03-12 00:00:00
Tasks: Sensor Fusion

In sensing applications where multiple sensors observe the same scene, fusing
sensor outputs can provide improved results. However, if some of the sensors
are providing lower quality outputs, the fused results can be degraded. In this
work, a multi-sensor conflict measure is proposed which estimates multi-sensor
conflict by representing each sensor output as interval-valued information and
examines the sensor output overlaps on all possible n-tuple sensor
combinations. The conflict is based on the sizes of the intervals and how many
sensors output values lie in these intervals. In this work, conflict is defined
in terms of how little the output from multiple sensors overlap. That is, high
degrees of overlap mean low sensor conflict, while low degrees of overlap mean
high conflict. This work is a preliminary step towards a robust conflict and
sensor fusion framework. In addition, a sensor fusion algorithm is proposed
based on a weighted sum of sensor outputs, where the weights for each sensor
diminish as the conflict measure increases. The proposed methods can be
utilized to (1) assess a measure of multi-sensor conflict, and (2) improve
sensor output fusion by lessening weighting for sensors with high conflict.
Using this measure, a simulated example is given to explain the mechanics of
calculating the conflict measure, and stereo camera 3D outputs are analyzed and
fused. In the stereo camera case, the sensor output is corrupted by additive
impulse noise, DC offset, and Gaussian noise. Impulse noise is common in
sensors due to intermittent interference, a DC offset a sensor bias or
registration error, and Gaussian noise represents a sensor output with low SNR.
The results show that sensor output fusion based on the conflict measure shows
improved accuracy over a simple averaging fusion strategy.