pwc-archive/papers-with-abstracts huggingface dataset:
Evorus: A Crowd-powered Conversational Assistant Built to Automate Itself Over Time
Authors: Ting-Hao 'Kenneth' Huang, Joseph Chee Chang, Jeffrey P. Bigham
Date: 2018-01-08 00:00:00
Tasks: Open-Domain Dialog

Crowd-powered conversational assistants have been shown to be more robust
than automated systems, but do so at the cost of higher response latency and
monetary costs. A promising direction is to combine the two approaches for high
quality, low latency, and low cost solutions. In this paper, we introduce
Evorus, a crowd-powered conversational assistant built to automate itself over
time by (i) allowing new chatbots to be easily integrated to automate more
scenarios, (ii) reusing prior crowd answers, and (iii) learning to
automatically approve response candidates. Our 5-month-long deployment with 80
participants and 281 conversations shows that Evorus can automate itself
without compromising conversation quality. Crowd-AI architectures have long
been proposed as a way to reduce cost and latency for crowd-powered systems;
Evorus demonstrates how automation can be introduced successfully in a deployed
system. Its architecture allows future researchers to make further innovation
on the underlying automated components in the context of a deployed open domain
dialog system.
Lung and Pancreatic Tumor Characterization in the Deep Learning Era: Novel Supervised and Unsupervised Learning Approaches
Authors: Sarfaraz Hussein, Pujan Kandel, Candice W. Bolan, Michael B. Wallace, Ulas Bagci
Date: 2018-01-10 00:00:00
Tasks: Multi-Task Learning, Prognosis, Specificity, Transfer Learning

Risk stratification (characterization) of tumors from radiology images can be
more accurate and faster with computer-aided diagnosis (CAD) tools. Tumor
characterization through such tools can also enable non-invasive cancer
staging, prognosis, and foster personalized treatment planning as a part of
precision medicine. In this study, we propose both supervised and unsupervised
machine learning strategies to improve tumor characterization. Our first
approach is based on supervised learning for which we demonstrate significant
gains with deep learning algorithms, particularly by utilizing a 3D
Convolutional Neural Network and Transfer Learning. Motivated by the
radiologists' interpretations of the scans, we then show how to incorporate
task dependent feature representations into a CAD system via a
graph-regularized sparse Multi-Task Learning (MTL) framework. In the second
approach, we explore an unsupervised learning algorithm to address the limited
availability of labeled training data, a common problem in medical imaging
applications. Inspired by learning from label proportion (LLP) approaches in
computer vision, we propose to use proportion-SVM for characterizing tumors. We
also seek the answer to the fundamental question about the goodness of "deep
features" for unsupervised tumor classification. We evaluate our proposed
supervised and unsupervised learning algorithms on two different tumor
diagnosis challenges: lung and pancreas with 1018 CT and 171 MRI scans,
respectively, and obtain the state-of-the-art sensitivity and specificity
results in both problems.
FWLBP: A Scale Invariant Descriptor for Texture Classification
Authors: Swalpa Kumar Roy, Nilavra Bhattacharya, Bhabatosh Chanda, Bidyut. B. Chaudhuri, Dipak Kumar Ghosh
Date: 2018-01-10 00:00:00
Tasks: Classification, General Classification, Texture Classification

In this paper we propose a novel texture descriptor called Fractal Weighted
Local Binary Pattern (FWLBP). The fractal dimension (FD) measure is relatively
invariant to scale-changes, and presents a good correlation with human
viewpoint of surface roughness. We have utilized this property to construct a
scale-invariant descriptor. Here, the input image is sampled using an augmented
form of the local binary pattern (LBP) over three different radii, and then
used an indexing operation to assign FD weights to the collected samples. The
final histogram of the descriptor has its features calculated using LBP, and
its weights computed from the FD image. The proposed descriptor is scale
invariant, and is also robust in rotation or reflection, and partially tolerant
to noise and illumination changes. In addition, the local fractal dimension is
relatively insensitive to the bi-Lipschitz transformations, whereas its
extension is adequate to precisely discriminate the fundamental of texture
primitives. Experiment results carried out on standard texture databases show
that the proposed descriptor achieved better classification rates compared to
the state-of-the-art descriptors.
Hand Segmentation for Hand-Object Interaction from Depth map
Authors: Byeongkeun Kang, Kar-Han Tan, Nan Jiang, Hung-Shuo Tai, Daniel Tretter, Truong Q. Nguyen
Date: 2016-03-08 00:00:00
Tasks: Hand Segmentation, Object, Segmentation

Hand segmentation for hand-object interaction is a necessary preprocessing
step in many applications such as augmented reality, medical application, and
human-robot interaction. However, typical methods are based on color
information which is not robust to objects with skin color, skin pigment
difference, and light condition variations. Thus, we propose hand segmentation
method for hand-object interaction using only a depth map. It is challenging
because of the small depth difference between a hand and objects during an
interaction. To overcome this challenge, we propose the two-stage random
decision forest (RDF) method consisting of detecting hands and segmenting
hands. To validate the proposed method, we demonstrate results on the publicly
available dataset of hand segmentation for hand-object interaction. The
proposed method achieves high accuracy in short processing time comparing to
the other state-of-the-art methods.
Adaptive Graph Convolutional Neural Networks
Authors: Ruoyu Li, Sheng Wang, Feiyun Zhu, Junzhou Huang
Date: 2018-01-10 00:00:00
Tasks: Metric Learning

Graph Convolutional Neural Networks (Graph CNNs) are generalizations of
classical CNNs to handle graph data such as molecular data, point could and
social networks. Current filters in graph CNNs are built for fixed and shared
graph structure. However, for most real data, the graph structures varies in
both size and connectivity. The paper proposes a generalized and flexible graph
CNN taking data of arbitrary graph structure as input. In that way a
task-driven adaptive graph is learned for each graph data while training. To
efficiently learn the graph, a distance metric learning is proposed. Extensive
experiments on nine graph-structured datasets have demonstrated the superior
performance improvement on both convergence speed and predictive accuracy.
Multivariate Bayesian Structural Time Series Model
Authors: S. Rao Jammalamadaka, Jinwen Qiu, Ning Ning
Date: 2018-01-10 00:00:00
Tasks: feature selection, model, Time Series, Time Series Analysis

This paper deals with inference and prediction for multiple correlated time
series, where one has also the choice of using a candidate pool of
contemporaneous predictors for each target series. Starting with a structural
model for the time-series, Bayesian tools are used for model fitting,
prediction, and feature selection, thus extending some recent work along these
lines for the univariate case. The Bayesian paradigm in this multivariate
setting helps the model avoid overfitting as well as capture correlations among
the multiple time series with the various state components. The model provides
needed flexibility to choose a different set of components and available
predictors for each target series. The cyclical component in the model can
handle large variations in the short term, which may be caused by external
shocks. We run extensive simulations to investigate properties such as
estimation accuracy and performance in forecasting. We then run an empirical
study with one-step-ahead prediction on the max log return of a portfolio of
stocks that involve four leading financial institutions. Both the simulation
studies and the extensive empirical study confirm that this multivariate model
outperforms three other benchmark models, viz. a model that treats each target
series as independent, the autoregressive integrated moving average model with
regression (ARIMAX), and the multivariate ARIMAX (MARIMAX) model.
Fusion of ANN and SVM Classifiers for Network Attack Detection
Authors: Takwa Omrani, Adel Dallali, Bilgacem Chibani Rhaimi, Jaouhar Fattahi
Date: 2018-01-09 00:00:00
Tasks: Intrusion Detection

With the progressive increase of network application and electronic devices
(computers, mobile phones, android, etc.) attack and intrusion, detection has
become a very challenging task in cybercrime detection area. in this context,
most of the existing approaches of attack detection rely mainly on a finite set
of attacks. These solutions are vulnerable, that is, they fail in detecting
some attacks when sources of informations are ambiguous or imperfect. However,
few approaches started investigating in this direction. This paper investigates
the role of machine learning approach (ANN, SVM) in detecting a TCP connection
traffic as a normal or a suspicious one. But, using ANN and SVM is an expensive
technique individually. In this paper, combining two classifiers are proposed,
where artificial neural network (ANN) classifier and support vector machine
(SVM) are both employed. Additionally, our proposed solution allows to
visualize obtained classification results. Accuracy of the proposed solution
has been compared with other classifier results. Experiments have been
conducted with different network connections selected from NSL-KDD DARPA
dataset. Empirical results show that combining ANN and SVM techniques for
attack detection is a promising direction.
nuts-flow/ml: data pre-processing for deep learning
Authors: S. Maetschke, R. Tennakoon, C. Vecchiola, R. Garnavi
Date: 2017-08-21 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning

Data preprocessing is a fundamental part of any machine learning application
and frequently the most time-consuming aspect when developing a machine
learning solution. Preprocessing for deep learning is characterized by
pipelines that lazily load data and perform data transformation, augmentation,
batching and logging. Many of these functions are common across applications
but require different arrangements for training, testing or inference. Here we
introduce a novel software framework named nuts-flow/ml that encapsulates
common preprocessing operations as components, which can be flexibly arranged
to rapidly construct efficient preprocessing pipelines for deep learning.
Sentiment Perception of Readers and Writers in Emoji use
Authors: Jose Berengueres, Dani Castro
Date: 2017-10-02 00:00:00
Tasks: 4k

Previous research has traditionally analyzed emoji sentiment from the point
of view of the reader of the content not the author. Here, we analyze emoji
sentiment from the point of view of the author and present a emoji sentiment
benchmark that was built from an employee happiness dataset where emoji happen
to be annotated with daily happiness of the author of the comment. The data
spans over 3 years, and 4k employees of 56 companies based in Barcelona. We
compare sentiment of writers to readers. Results indicate that, there is an 82%
agreement in how emoji sentiment is perceived by readers and writers. Finally,
we report that when authors use emoji they report higher levels of happiness.
Emoji use was not found to be correlated with differences in author moodiness.
Automatic Breast Ultrasound Image Segmentation: A Survey
Authors: Min Xian, Yingtao Zhang, H. D. Cheng, Fei Xu, Boyu Zhang, Jianrui Ding
Date: 2017-04-04 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Survey

Breast cancer is one of the leading causes of cancer death among women
worldwide. In clinical routine, automatic breast ultrasound (BUS) image
segmentation is very challenging and essential for cancer diagnosis and
treatment planning. Many BUS segmentation approaches have been studied in the
last two decades, and have been proved to be effective on private datasets.
Currently, the advancement of BUS image segmentation seems to meet its
bottleneck. The improvement of the performance is increasingly challenging, and
only few new approaches were published in the last several years. It is the
time to look at the field by reviewing previous approaches comprehensively and
to investigate the future directions. In this paper, we study the basic ideas,
theories, pros and cons of the approaches, group them into categories, and
extensively review each category in depth by discussing the principles,
application issues, and advantages/disadvantages.
BUSIS: A Benchmark for Breast Ultrasound Image Segmentation
Authors: Min Xian, Yingtao Zhang, H. D. Cheng, Fei Xu, Kuan Huang, Boyu Zhang, Jianrui Ding, Chunping Ning, Ying Wang
Date: 2018-01-09 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Tumor Segmentation

Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Comput-er-Aided Diagnosis (CAD) systems. Many BUS segmentation approaches have been studied in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which results in a discrepancy in performance comparison. Therefore, there is a pressing need for building a benchmark to compare existing methods using a public dataset objectively, to determine the performance of the best breast tumor segmentation algorithm available today, and to investigate what segmentation strategies are valuable in clinical practice and theoretical study. In this work, a benchmark for B-mode breast ultrasound image segmentation is presented. In the benchmark, 1) we collected 562 breast ultrasound images, prepared a software tool, and involved four radiologists in obtaining accurate annotations through standardized procedures; 2) we extensively compared the performance of sixteen state-of-the-art segmentation methods and discussed their advantages and disadvantages; 3) we proposed a set of valuable quantitative metrics to evaluate both semi-automatic and fully automatic segmentation approaches; and 4) the successful segmentation strategies and possible future improvements are discussed in details.
Paranom: A Parallel Anomaly Dataset Generator
Authors: Justin Gottschlich
Date: 2018-01-09 00:00:00
Tasks: Anomaly Detection, General Classification

In this paper, we present Paranom, a parallel anomaly dataset generator. We
discuss its design and provide brief experimental results demonstrating its
usefulness in improving the classification correctness of LSTM-AD, a
state-of-the-art anomaly detection model.
RGB image-based data analysis via discrete Morse theory and persistent homology
Authors: Chuan Du, Christopher Szul, Adarsh Manawa, Nima Rasekh, Rosemary Guzman, Ruth Davidson
Date: 2018-01-09 00:00:00

Understanding and comparing images for the purposes of data analysis is
currently a very computationally demanding task. A group at Australian National
University (ANU) recently developed open-source code that can detect
fundamental topological features of a grayscale image in a computationally
feasible manner. This is made possible by the fact that computers store
grayscale images as cubical cellular complexes. These complexes can be studied
using the techniques of discrete Morse theory. We expand the functionality of
the ANU code by introducing methods and software for analyzing images encoded
in red, green, and blue (RGB), because this image encoding is very popular for
publicly available data. Our methods allow the extraction of key topological
information from RGB images via informative persistence diagrams by introducing
novel methods for transforming RGB-to-grayscale. This paradigm allows us to
perform data analysis directly on RGB images representing water scarcity
variability as well as crime variability. We introduce software enabling a a
user to predict future image properties, towards the eventual aim of more rapid
image-based data behavior prediction.
Moments in Time Dataset: one million videos for event understanding
Authors: Mathew Monfort, Alex Andonian, Bolei Zhou, Kandan Ramakrishnan, Sarah Adel Bargal, Tom Yan, Lisa Brown, Quanfu Fan, Dan Gutfruend, Carl Vondrick, Aude Oliva
Date: 2018-01-09 00:00:00
Tasks: Action Recognition, Diversity, Multimodal Activity Recognition, Temporal Action Localization

We present the Moments in Time Dataset, a large-scale human-annotated
collection of one million short videos corresponding to dynamic events
unfolding within three seconds. Modeling the spatial-audio-temporal dynamics
even for actions occurring in 3 second videos poses many challenges: meaningful
events do not include only people, but also objects, animals, and natural
phenomena; visual and auditory events can be symmetrical in time ("opening" is
"closing" in reverse), and either transient or sustained. We describe the
annotation process of our dataset (each video is tagged with one action or
activity label among 339 different classes), analyze its scale and diversity in
comparison to other large-scale video datasets for action recognition, and
report results of several baseline models addressing separately, and jointly,
three modalities: spatial, temporal and auditory. The Moments in Time dataset,
designed to have a large coverage and diversity of events in both visual and
auditory modalities, can serve as a new challenge to develop models that scale
to the level of complexity and abstract reasoning that a human processes on a
daily basis.
Comparing heterogeneous entities using artificial neural networks of trainable weighted structural components and machine-learned activation functions
Authors: Artit Wangperawong, Kettip Kriangchaivech, Austin Lanari, Supui Lam, Panthong Wangperawong
Date: 2018-01-09 00:00:00
Tasks: Articles

To compare entities of differing types and structural components, the
artificial neural network paradigm was used to cross-compare structural
components between heterogeneous documents. Trainable weighted structural
components were input into machine-learned activation functions of the neurons.
The model was used for matching news articles and videos, where the inputs and
activation functions respectively consisted of term vectors and cosine
similarity measures between the weighted structural components. The model was
tested with different weights, achieving as high as 59.2% accuracy for matching
videos to news articles. A mobile application user interface for recommending
related videos for news articles was developed to demonstrate consumer value,
including its potential usefulness for cross-selling products from unrelated
categories.
Deep In-GPU Experience Replay
Authors: Ben Parr
Date: 2018-01-09 00:00:00
Tasks: CPU, GPU, Reinforcement Learning

Experience replay allows a reinforcement learning agent to train on samples
from a large amount of the most recent experiences. A simple in-RAM experience
replay stores these most recent experiences in a list in RAM, and then copies
sampled batches to the GPU for training. I moved this list to the GPU, thus
creating an in-GPU experience replay, and a training step that no longer has
inputs copied from the CPU. I trained an agent to play Super Smash Bros. Melee,
using internal game memory values as inputs and outputting controller button
presses. A single state in Melee contains 27 floats, so the full experience
replay fits on a single GPU. For a batch size of 128, the in-GPU experience
replay trained twice as fast as the in-RAM experience replay. As far as I know,
this is the first in-GPU implementation of experience replay. Finally, I note a
few ideas for fitting the experience replay inside the GPU when the environment
state requires more memory.
Convergence Analysis of Gradient Descent Algorithms with Proportional Updates
Authors: Igor Gitman, Deepak Dilipkumar, Ben Parr
Date: 2018-01-09 00:00:00

The rise of deep learning in recent years has brought with it increasingly
clever optimization methods to deal with complex, non-linear loss functions.
These methods are often designed with convex optimization in mind, but have
been shown to work well in practice even for the highly non-convex optimization
associated with neural networks. However, one significant drawback of these
methods when they are applied to deep learning is that the magnitude of the
update step is sometimes disproportionate to the magnitude of the weights (much
smaller or larger), leading to training instabilities such as vanishing and
exploding gradients. An idea to combat this issue is gradient descent with
proportional updates. Gradient descent with proportional updates was introduced
in 2017. It was independently developed by You et al (Layer-wise Adaptive Rate
Scaling (LARS) algorithm) and by Abu-El-Haija (PercentDelta algorithm). The
basic idea of both of these algorithms is to make each step of the gradient
descent proportional to the current weight norm and independent of the gradient
magnitude. It is common in the context of new optimization methods to prove
convergence or derive regret bounds under the assumption of Lipschitz
continuity and convexity. However, even though LARS and PercentDelta were shown
to work well in practice, there is no theoretical analysis of the convergence
properties of these algorithms. Thus it is not clear if the idea of gradient
descent with proportional updates is used in the optimal way, or if it could be
improved by using a different norm or specific learning rate schedule, for
example. Moreover, it is not clear if these algorithms can be extended to other
problems, besides neural networks. We attempt to answer these questions by
establishing the theoretical analysis of gradient descent with proportional
updates, and verifying this analysis with empirical examples.
Robust Propensity Score Computation Method based on Machine Learning with Label-corrupted Data
Authors: Chen Wang, Suzhen Wang, Fuyan Shi, Zaixiang Wang
Date: 2018-01-09 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

In biostatistics, propensity score is a common approach to analyze the
imbalance of covariate and process confounding covariates to eliminate
differences between groups. While there are an abundant amount of methods to
compute propensity score, a common issue of them is the corrupted labels in the
dataset. For example, the data collected from the patients could contain
samples that are treated mistakenly, and the computing methods could
incorporate them as a misleading information. In this paper, we propose a
Machine Learning-based method to handle the problem. Specifically, we utilize
the fact that the majority of sample should be labeled with the correct
instance and design an approach to first cluster the data with spectral
clustering and then sample a new dataset with a distribution processed from the
clustering results. The propensity score is computed by Xgboost, and a
mathematical justification of our method is provided in this paper. The
experimental results illustrate that xgboost propensity scores computing with
the data processed by our method could outperform the same method with original
data, and the advantages of our method increases as we add some artificial
corruptions to the dataset. Meanwhile, the implementation of xgboost to compute
propensity score for multiple treatments is also a pioneering work in the area.