pwc-archive/papers-with-abstracts huggingface dataset:
A Data Mining Approach to Flight Arrival Delay Prediction for American Airlines
Authors: Navoneel Chakrabarty
Date: 2019-03-15 00:00:00

In the present scenario of domestic flights in USA, there have been numerous
instances of flight delays and cancellations. In the United States, the
American Airlines, Inc. have been one of the most entrusted and the world's
largest airline in terms of number of destinations served. But when it comes to
domestic flights, AA has not lived up to the expectations in terms of
punctuality or on-time performance. Flight Delays also result in airline
companies operating commercial flights to incur huge losses. So, they are
trying their best to prevent or avoid Flight Delays and Cancellations by taking
certain measures. This study aims at analyzing flight information of US
domestic flights operated by American Airlines, covering top 5 busiest airports
of US and predicting possible arrival delay of the flight using Data Mining and
Machine Learning Approaches. The Gradient Boosting Classifier Model is deployed
by training and hyper-parameter tuning it, achieving a maximum accuracy of
85.73%. Such an Intelligent System is very essential in foretelling
flights'on-time performance.
Dying ReLU and Initialization: Theory and Numerical Examples
Authors: Lu Lu, Yeonjong Shin, Yanhui Su, George Em. Karniadakis
Date: 2019-03-15 00:00:00

The dying ReLU refers to the problem when ReLU neurons become inactive and only output 0 for any input. There are many empirical and heuristic explanations of why ReLU neurons die. However, little is known about its theoretical analysis. In this paper, we rigorously prove that a deep ReLU network will eventually die in probability as the depth goes to infinite. Several methods have been proposed to alleviate the dying ReLU. Perhaps, one of the simplest treatments is to modify the initialization procedure. One common way of initializing weights and biases uses symmetric probability distributions, which suffers from the dying ReLU. We thus propose a new initialization procedure, namely, a randomized asymmetric initialization. We prove that the new initialization can effectively prevent the dying ReLU. All parameters required for the new initialization are theoretically designed. Numerical examples are provided to demonstrate the effectiveness of the new initialization procedure.
On Sample Complexity of Projection-Free Primal-Dual Methods for Learning Mixture Policies in Markov Decision Processes
Authors: Masoud Badiei Khuzani, Varun Vasudevan, Hongyi Ren, Lei Xing
Date: 2019-03-15 00:00:00

We study the problem of learning policy of an infinite-horizon, discounted cost, Markov decision process (MDP) with a large number of states. We compute the actions of a policy that is nearly as good as a policy chosen by a suitable oracle from a given mixture policy class characterized by the convex hull of a set of known base policies. To learn the coefficients of the mixture model, we recast the problem as an approximate linear programming (ALP) formulation for MDPs, where the feature vectors correspond to the occupation measures of the base policies defined on the state-action space. We then propose a projection-free stochastic primal-dual method with the Bregman divergence to solve the characterized ALP. Furthermore, we analyze the probably approximately correct (PAC) sample complexity of the proposed stochastic algorithm, namely the number of queries required to achieve near optimal objective value. We also propose a modification of our proposed algorithm with the polytope constraint sampling for the smoothed ALP, where the restriction to lower bounding approximations are relaxed. In addition, we apply the proposed algorithms to a queuing problem, and compare their performance with a penalty function algorithm. The numerical results illustrates that the primal-dual achieves better efficiency and low variance across different trials compared to the penalty function method.
Crowd Counting with Decomposed Uncertainty
Authors: Min-hwan Oh, Peder A. Olsen, Karthikeyan Natesan Ramamurthy
Date: 2019-03-15 00:00:00
Tasks: Crowd Counting, Uncertainty Quantification

Research in neural networks in the field of computer vision has achieved remarkable accuracy for point estimation. However, the uncertainty in the estimation is rarely addressed. Uncertainty quantification accompanied by point estimation can lead to a more informed decision, and even improve the prediction quality. In this work, we focus on uncertainty estimation in the domain of crowd counting. With increasing occurrences of heavily crowded events such as political rallies, protests, concerts, etc., automated crowd analysis is becoming an increasingly crucial task. The stakes can be very high in many of these real-world applications. We propose a scalable neural network framework with quantification of decomposed uncertainty using a bootstrap ensemble. We demonstrate that the proposed uncertainty quantification method provides additional insight to the crowd counting problem and is simple to implement. We also show that our proposed method exhibits the state of the art performances in many benchmark crowd counting datasets.
Live Reconstruction of Large-Scale Dynamic Outdoor Worlds
Authors: Ondrej Miksik, Vibhav Vineet
Date: 2019-03-15 00:00:00
Tasks: 3D Reconstruction, Pose Estimation

Standard 3D reconstruction pipelines assume stationary world, therefore
suffer from `ghost artifacts' whenever dynamic objects are present in the
scene. Recent approaches has started tackling this issue, however, they
typically either only discard dynamic information, represent it using bounding
boxes or per-frame depth or rely on approaches that are inherently slow and not
suitable to online settings.
  We propose an end-to-end system for live reconstruction of large-scale
outdoor dynamic environments. We leverage recent advances in computationally
efficient data-driven approaches for 6-DoF object pose estimation to segment
the scene into objects and stationary `background'. This allows us to represent
the scene using a time-dependent (dynamic) map, in which each object is
explicitly represented as a separate instance and reconstructed in its own
volume. For each time step, our dynamic map maintains a relative pose of each
volume with respect to the stationary background. Our system operates in
incremental manner which is essential for on-line reconstruction, handles
large-scale environments with objects at large distances and runs in (near)
real-time. We demonstrate the efficacy of our approach on the KITTI dataset,
and provide qualitative and quantitative results showing high-quality dense 3D
reconstructions of a number of dynamic scenes.
Sex and Coevolution
Authors: Larry Bull
Date: 2019-03-15 00:00:00

It has been suggested that the fundamental haploid-diploid cycle of
eukaryotic sex exploits a rudimentary form of the Baldwin effect. This paper
uses the well-known NKCS model to explore the effects of coevolution upon the
behaviour of eukaryotes. It is shown how varying fitness landscape size,
ruggedness and connectedness can vary the conditions under which eukaryotic sex
proves beneficial over asexual reproduction in haploids in a coevolutionary
context. Moreover, eukaryotic sex is shown to be more sensitive to the relative
rate of evolution exhibited by its partnering species than asexual haploids.
An Exploration of State-of-the-art Methods for Offensive Language Detection
Authors: Harrison Uglow, Martin Zlocha, Szymon Zmyślony
Date: 2019-03-15 00:00:00
Tasks: Language Identification

We provide a comprehensive investigation of different custom and
off-the-shelf architectures as well as different approaches to generating
feature vectors for offensive language detection. We also show that these
approaches work well on small and noisy datasets such as on the Offensive
Language Identification Dataset (OLID), so it should be possible to use them
for other applications.
A Lightweight Optical Flow CNN -- Revisiting Data Fidelity and Regularization
Authors: Tak-Wai Hui, Xiaoou Tang, Chen Change Loy
Date: 2019-03-15 00:00:00
Tasks: Optical Flow Estimation

Over four decades, the majority addresses the problem of optical flow estimation using variational methods. With the advance of machine learning, some recent works have attempted to address the problem using convolutional neural network (CNN) and have showed promising results. FlowNet2, the state-of-the-art CNN, requires over 160M parameters to achieve accurate flow estimation. Our LiteFlowNet2 outperforms FlowNet2 on Sintel and KITTI benchmarks, while being 25.3 times smaller in the model size and 3.1 times faster in the running speed. LiteFlowNet2 is built on the foundation laid by conventional methods and resembles the corresponding roles as data fidelity and regularization in variational methods. We compute optical flow in a spatial-pyramid formulation as SPyNet but through a novel lightweight cascaded flow inference. It provides high flow estimation accuracy through early correction with seamless incorporation of descriptor matching. Flow regularization is used to ameliorate the issue of outliers and vague flow boundaries through feature-driven local convolutions. Our network also owns an effective structure for pyramidal feature extraction and embraces feature warping rather than image warping as practiced in FlowNet2 and SPyNet. Comparing to LiteFlowNet, LiteFlowNet2 improves the optical flow accuracy on Sintel Clean by 23.3%, Sintel Final by 12.8%, KITTI 2012 by 19.6%, and KITTI 2015 by 18.8%, while being 2.2 times faster. Our network protocol and trained models are made publicly available on https://github.com/twhui/LiteFlowNet2.
Self-Organization and Artificial Life
Authors: Carlos Gershenson, Vito Trianni, Justin Werfel, Hiroki Sayama
Date: 2019-03-14 00:00:00
Tasks: Artificial Life

Self-organization can be broadly defined as the ability of a system to display ordered spatio-temporal patterns solely as the result of the interactions among the system components. Processes of this kind characterize both living and artificial systems, making self-organization a concept that is at the basis of several disciplines, from physics to biology and engineering. Placed at the frontiers between disciplines, Artificial Life (ALife) has heavily borrowed concepts and tools from the study of self-organization, providing mechanistic interpretations of life-like phenomena as well as useful constructivist approaches to artificial system design. Despite its broad usage within ALife, the concept of self-organization has been often excessively stretched or misinterpreted, calling for a clarification that could help with tracing the borders between what can and cannot be considered self-organization. In this review, we discuss the fundamental aspects of self-organization and list the main usages within three primary ALife domains, namely "soft" (mathematical/computational modeling), "hard" (physical robots), and "wet" (chemical/biological systems) ALife. We also provide a classification to locate this research. Finally, we discuss the usefulness of self-organization and related concepts within ALife studies, point to perspectives and challenges for future research, and list open questions. We hope that this work will motivate discussions related to self-organization in ALife and related fields.
Complexity-entropy analysis at different levels of organization in written language
Authors: E. Estevez-Rams, A. Mesa Rodriguez, D. Estevez-Moya
Date: 2019-03-14 00:00:00

Written language is complex. A written text can be considered an attempt to
convey a meaningful message which ends up being constrained by language rules,
context dependence and highly redundant in its use of resources. Despite all
these constraints, unpredictability is an essential element of natural
language. Here we present the use of entropic measures to assert the balance
between predictability and surprise in written text. In short, it is possible
to measure innovation and context preservation in a document. It is shown that
this can also be done at the different levels of organization of a text. The
type of analysis presented is reasonably general, and can also be used to
analyze the same balance in other complex messages such as DNA, where a
hierarchy of organizational levels are known to exist.
Voice command generation using Progressive Wavegans
Authors: Thomas Wiest, NIcholas Cummins, Alice Baird, Simone Hantke, Judith Dineley, Björn Schuller
Date: 2019-03-13 00:00:00
Tasks: Audio Generation, Image Generation

Generative Adversarial Networks (GANs) have become exceedingly popular in a
wide range of data-driven research fields, due in part to their success in
image generation. Their ability to generate new samples, often from only a
small amount of input data, makes them an exciting research tool in areas with
limited data resources. One less-explored application of GANs is the synthesis
of speech and audio samples. Herein, we propose a set of extensions to the
WaveGAN paradigm, a recently proposed approach for sound generation using GANs.
The aim of these extensions - preprocessing, Audio-to-Audio generation, skip
connections and progressive structures - is to improve the human likeness of
synthetic speech samples. Scores from listening tests with 30 volunteers
demonstrated a moderate improvement (Cohen's d coefficient of 0.65) in human
likeness using the proposed extensions compared to the original WaveGAN
approach.
A Heuristic Algorithm for the Fabric Spreading and Cutting Problem in Apparel Factories
Authors: Xiuqin Shang, Dayong Shen, Fei-Yue Wang, Timo R. Nyberg
Date: 2019-03-13 00:00:00

We study the fabric spreading and cutting problem in apparel factories. For
the sake of saving the material costs, the cutting requirement should be met
exactly without producing additional garment components. For reducing the
production costs, the number of lays that corresponds to the frequency of using
the cutting beds should be minimized. We propose an iterated greedy algorithm
for solving the fabric spreading and cutting problem. This algorithm contains a
constructive procedure and an improving loop. Firstly the constructive
procedure creates a set of lays in sequence, and then the improving loop tries
to pick each lay from the lay set and rearrange the remaining lays into a
smaller lay set. The improving loop will run until it cannot obtain any small
lay set or the time limit is due. The experiment results on 500 cases shows
that the proposed algorithm is effective and efficient.
Low Power Inference for On-Device Visual Recognition with a Quantization-Friendly Solution
Authors: Chen Feng, Tao Sheng, Zhiyu Liang, Shaojie Zhuo, Xiaopeng Zhang, Liang Shen, Matthew Ardi, Alexander C. Berg, Yiran Chen, Bo Chen, Kent Gauen, Yung-Hsiang Lu
Date: 2019-03-12 00:00:00
Tasks: CPU, Quantization

The IEEE Low-Power Image Recognition Challenge (LPIRC) is an annual
competition started in 2015 that encourages joint hardware and software
solutions for computer vision systems with low latency and power. Track 1 of
the competition in 2018 focused on the innovation of software solutions with
fixed inference engine and hardware. This decision allows participants to
submit models online and not worry about building and bringing custom hardware
on-site, which attracted a historically large number of submissions. Among the
diverse solutions, the winning solution proposed a quantization-friendly
framework for MobileNets that achieves an accuracy of 72.67% on the holdout
dataset with an average latency of 27ms on a single CPU core of Google Pixel2
phone, which is superior to the best real-time MobileNet models at the time.
Data-driven Neural Architecture Learning For Financial Time-series Forecasting
Authors: Dat Thanh Tran, Juho Kanniainen, Moncef Gabbouj, Alexandros Iosifidis
Date: 2019-03-05 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Forecasting, Time Series Prediction

Forecasting based on financial time-series is a challenging task since most
real-world data exhibits nonstationary property and nonlinear dependencies. In
addition, different data modalities often embed different nonlinear
relationships which are difficult to capture by human-designed models. To
tackle the supervised learning task in financial time-series prediction, we
propose the application of a recently formulated algorithm that adaptively
learns a mapping function, realized by a heterogeneous neural architecture
composing of Generalized Operational Perceptron, given a set of labeled data.
With a modified objective function, the proposed algorithm can accommodate the
frequently observed imbalanced data distribution problem. Experiments on a
large-scale Limit Order Book dataset demonstrate that the proposed algorithm
outperforms related algorithms, including tensor-based methods which have
access to a broader set of input information.
Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis
Authors: Cheng Cheng, Beitong Zhou, Guijun Ma, Dongrui Wu, Ye Yuan
Date: 2019-03-02 00:00:00
Tasks: Fault Diagnosis, Transfer Learning

The demand of artificial intelligent adoption for condition-based maintenance
strategy is astonishingly increased over the past few years. Intelligent fault
diagnosis is one critical topic of maintenance solution for mechanical systems.
Deep learning models, such as convolutional neural networks (CNNs), have been
successfully applied to fault diagnosis tasks for mechanical systems and
achieved promising results. However, for diverse working conditions in the
industry, deep learning suffers two difficulties: one is that the well-defined
(source domain) and new (target domain) datasets are with different feature
distributions; another one is the fact that insufficient or no labelled data in
target domain significantly reduce the accuracy of fault diagnosis. As a novel
idea, deep transfer learning (DTL) is created to perform learning in the target
domain by leveraging information from the relevant source domain. Inspired by
Wasserstein distance of optimal transport, in this paper, we propose a novel
DTL approach to intelligent fault diagnosis, namely Wasserstein Distance based
Deep Transfer Learning (WD-DTL), to learn domain feature representations
(generated by a CNN based feature extractor) and to minimize the distributions
between the source and target domains through adversarial training. The
effectiveness of the proposed WD-DTL is verified through 3 transfer scenarios
and 16 transfer fault diagnosis experiments of both unsupervised and supervised
(with insufficient labelled data) learning. We also provide a comprehensive
analysis of the network visualization of those transfer tasks.
Predicting customer's gender and age depending on mobile phone data
Authors: Ibrahim Mousa AlZuabi, Assef Jafar, Kadan Aljoumaa
Date: 2019-02-20 00:00:00
Tasks: Gender Prediction, Management, Marketing, Prediction

In the age of data driven solution, the customer demographic attributes, such
as gender and age, play a core role that may enable companies to enhance the
offers of their services and target the right customer in the right time and
place. In the marketing campaign, the companies want to target the real user of
the GSM (global system for mobile communications), not the line owner. Where
sometimes they may not be the same. This work proposes a method that predicts
users' gender and age based on their behavior, services and contract
information. We used call detail records (CDRs), customer relationship
management (CRM) and billing information as a data source to analyze telecom
customer behavior, and applied different types of machine learning algorithms
to provide marketing campaigns with more accurate information about customer
demographic attributes. This model is built using reliable data set of 18,000
users provided by SyriaTel Telecom Company, for training and testing. The model
applied by using big data technology and achieved 85.6% accuracy in terms of
user gender prediction and 65.5% of user age prediction. The main contribution
of this work is the improvement in the accuracy in terms of user gender
prediction and user age prediction based on mobile phone data and end-to-end
solution that approaches customer data from multiple aspects in the telecom
domain.
Day-Ahead Hourly Forecasting of Power Generation from Photovoltaic Plants
Authors: Lorenzo Gigoni, Alessandro Betti, Emanuele Crisostomi, Alessandro Franco, Mauro Tucci, Fabrizio Bizzarri, Debora Mucci
Date: 2019-02-26 00:00:00

The ability to accurately forecast power generation from renewable sources is
nowadays recognised as a fundamental skill to improve the operation of power
systems. Despite the general interest of the power community in this topic, it
is not always simple to compare different forecasting methodologies, and infer
the impact of single components in providing accurate predictions. In this
paper we extensively compare simple forecasting methodologies with more
sophisticated ones over 32 photovoltaic plants of different size and technology
over a whole year. Also, we try to evaluate the impact of weather conditions
and weather forecasts on the prediction of PV power generation.
Complexity Analysis Approach for Prefabricated Construction Products Using Uncertain Data Clustering
Authors: Wenying Ji, Simaan M. AbouRizk, Osmar R. Zaiane, Yitong Li
Date: 2017-10-29 00:00:00
Tasks: Clustering

This paper proposes an uncertain data clustering approach to quantitatively
analyze the complexity of prefabricated construction components through the
integration of quality performance-based measures with associated engineering
design information. The proposed model is constructed in three steps, which (1)
measure prefabricated construction product complexity (hereafter referred to as
product complexity) by introducing a Bayesian-based nonconforming quality
performance indicator; (2) score each type of product complexity by developing
a Hellinger distance-based distribution similarity measurement; and (3) cluster
products into homogeneous complexity groups by using the agglomerative
hierarchical clustering technique. An illustrative example is provided to
demonstrate the proposed approach, and a case study of an industrial company in
Edmonton, Canada, is conducted to validate the feasibility and applicability of
the proposed model. This research inventively defines and investigates product
complexity from the perspective of product quality performance with design
information associated. The research outcomes provide simplified,
interpretable, and informative insights for practitioners to better analyze and
manage product complexity. In addition to this practical contribution, a novel
hierarchical clustering technique is devised. This technique is capable of
clustering uncertain data (i.e., beta distributions) with lower computational
complexity and has the potential to be generalized to cluster all types of
uncertain data.
A Machine Learning Approach to Comment Toxicity Classification
Authors: Navoneel Chakrabarty
Date: 2019-02-27 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification

Now-a-days, derogatory comments are often made by one another, not only in
offline environment but also immensely in online environments like social
networking websites and online communities. So, an Identification combined with
Prevention System in all social networking websites and applications, including
all the communities, existing in the digital world is a necessity. In such a
system, the Identification Block should identify any negative online behaviour
and should signal the Prevention Block to take action accordingly. This study
aims to analyse any piece of text and detecting different types of toxicity
like obscenity, threats, insults and identity-based hatred. The labelled
Wikipedia Comment Dataset prepared by Jigsaw is used for the purpose. A
6-headed Machine Learning tf-idf Model has been made and trained separately,
yielding a Mean Validation Accuracy of 98.08% and Absolute Validation Accuracy
of 91.61%. Such an Automated System should be deployed for enhancing healthy
online conversation