pwc-archive/papers-with-abstracts huggingface dataset:
Semiparametric correction for endogenous truncation bias with Vox Populi based participation decision
Authors: Nir Billfeld, Moshe Kim
Date: 2019-02-17 00:00:00

We synthesize the knowledge present in various scientific disciplines for the
development of semiparametric endogenous truncation-proof algorithm, correcting
for truncation bias due to endogenous self-selection. This synthesis enriches
the algorithm's accuracy, efficiency and applicability. Improving upon the
covariate shift assumption, data are intrinsically affected and largely
generated by their own behavior (cognition). Refining the concept of Vox Populi
(Wisdom of Crowd) allows data points to sort themselves out depending on their
estimated latent reference group opinion space. Monte Carlo simulations, based
on 2,000,000 different distribution functions, practically generating 100
million realizations, attest to a very high accuracy of our model.
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank
Authors: Xialei Liu, Joost Van de Weijer, Andrew D. Bagdanov
Date: 2019-02-17 00:00:00
Tasks: Active Learning, Crowd Counting, Image Quality Assessment, Informativeness, Learning-To-Rank, regression, Self-Supervised Learning

For many applications the collection of labeled data is expensive laborious.
Exploitation of unlabeled data during training is thus a long pursued objective
of machine learning. Self-supervised learning addresses this by positing an
auxiliary task (different, but related to the supervised task) for which data
is abundantly available. In this paper, we show how ranking can be used as a
proxy task for some regression problems. As another contribution, we propose an
efficient backpropagation technique for Siamese networks which prevents the
redundant computation introduced by the multi-branch network architecture. We
apply our framework to two regression problems: Image Quality Assessment (IQA)
and Crowd Counting. For both we show how to automatically generate ranked image
sets from unlabeled data. Our results show that networks trained to regress to
the ground truth targets for labeled data and to simultaneously learn to rank
unlabeled data obtain significantly better, state-of-the-art results for both
IQA and crowd counting. In addition, we show that measuring network uncertainty
on the self-supervised proxy task is a good measure of informativeness of
unlabeled data. This can be used to drive an algorithm for active learning and
we show that this reduces labeling effort by up to 50%.
A semi-supervised deep residual network for mode detection in Wi-Fi signals
Authors: Arash Kalatian, Bilal Farooq
Date: 2019-02-17 00:00:00
Tasks: Transportation Mode Detection

Due to their ubiquitous and pervasive nature, Wi-Fi networks have the
potential to collect large-scale, low-cost, and disaggregate data on multimodal
transportation. In this study, we develop a semi-supervised deep residual
network (ResNet) framework to utilize Wi-Fi communications obtained from
smartphones for the purpose of transportation mode detection. This framework is
evaluated on data collected by Wi-Fi sensors located in a congested urban area
in downtown Toronto. To tackle the intrinsic difficulties and costs associated
with labelled data collection, we utilize ample amount of easily collected
low-cost unlabelled data by implementing the semi-supervised part of the
framework. By incorporating a ResNet architecture as the core of the framework,
we take advantage of the high-level features not considered in the traditional
machine learning frameworks. The proposed framework shows a promising
performance on the collected data, with a prediction accuracy of 81.8% for
walking, 82.5% for biking and 86.0% for the driving mode.
ODIN: ODE-Informed Regression for Parameter and State Inference in Time-Continuous Dynamical Systems
Authors: Philippe Wenk, Gabriele Abbati, Michael A. Osborne, Bernhard Schölkopf, Andreas Krause, Stefan Bauer
Date: 2019-02-17 00:00:00
Tasks: Gaussian Processes, Model Selection, regression

Parameter inference in ordinary differential equations is an important problem in many applied sciences and in engineering, especially in a data-scarce setting. In this work, we introduce a novel generative modeling approach based on constrained Gaussian processes and leverage it to build a computationally and data efficient algorithm for state and parameter inference. In an extensive set of experiments, our approach outperforms the current state of the art for parameter inference both in terms of accuracy and computational cost. It also shows promising results for the much more challenging problem of model selection.
Deep-learning inversion: a next generation seismic velocity-model building method
Authors: Fangshu Yang, Jianwei Ma
Date: 2019-02-17 00:00:00
Tasks: Seismic Imaging

Seismic velocity is one of the most important parameters used in seismic
exploration. Accurate velocity models are key prerequisites for reverse-time
migration and other high-resolution seismic imaging techniques. Such velocity
information has traditionally been derived by tomography or full-waveform
inversion (FWI), which are time consuming and computationally expensive, and
they rely heavily on human interaction and quality control. We investigate a
novel method based on the supervised deep fully convolutional neural network
(FCN) for velocity-model building (VMB) directly from raw seismograms. Unlike
the conventional inversion method based on physical models, the supervised
deep-learning methods are based on big-data training rather than
prior-knowledge assumptions. During the training stage, the network establishes
a nonlinear projection from the multi-shot seismic data to the corresponding
velocity models. During the prediction stage, the trained network can be used
to estimate the velocity models from the new input seismic data. One key
characteristic of the deep-learning method is that it can automatically extract
multi-layer useful features without the need for human-curated activities and
initial velocity setup. The data-driven method usually requires more time
during the training stage, and actual predictions take less time, with only
seconds needed. Therefore, the computational time of geophysical inversions,
including real-time inversions, can be dramatically reduced once a good
generalized network is built. By using numerical experiments on synthetic
models, the promising performances of our proposed method are shown in
comparison with conventional FWI even when the input data are in more realistic
scenarios. Discussions on the deep-learning methods, training dataset, lack of
low frequencies, and advantages and disadvantages of the new method are also
provided.
Exploring Stereovision-Based 3-D Scene Reconstruction for Augmented Reality
Authors: Guang-Yu Nie, Yun Liu, Cong Wang, Yue Liu, Yongtian Wang
Date: 2019-02-17 00:00:00
Tasks: Decoder, Stereo Matching, Stereo Matching Hand

Three-dimensional (3-D) scene reconstruction is one of the key techniques in
Augmented Reality (AR), which is related to the integration of image processing
and display systems of complex information. Stereo matching is a computer
vision based approach for 3-D scene reconstruction. In this paper, we explore
an improved stereo matching network, SLED-Net, in which a Single Long
Encoder-Decoder is proposed to replace the stacked hourglass network in PSM-Net
for better contextual information learning. We compare SLED-Net to
state-of-the-art methods recently published, and demonstrate its superior
performance on Scene Flow and KITTI2015 test sets.
A Comparative Study of Feature Selection Methods for Dialectal Arabic Sentiment Classification Using Support Vector Machine
Authors: Omar Al-Harbi
Date: 2019-02-17 00:00:00
Tasks: Arabic Sentiment Analysis, feature selection, General Classification, Sentiment Analysis, Sentiment Classification

Unlike other languages, the Arabic language has a morphological complexity
which makes the Arabic sentiment analysis is a challenging task. Moreover, the
presence of the dialects in the Arabic texts have made the sentiment analysis
task is more challenging, due to the absence of specific rules that govern the
writing or speaking system. Generally, one of the problems of sentiment
analysis is the high dimensionality of the feature vector. To resolve this
problem, many feature selection methods have been proposed. In contrast to the
dialectal Arabic language, these selection methods have been investigated
widely for the English language. This work investigated the effect of feature
selection methods and their combinations on dialectal Arabic sentiment
classification. The feature selection methods are Information Gain (IG),
Correlation, Support Vector Machine (SVM), Gini Index (GI), and Chi-Square. A
number of experiments were carried out on dialectical Jordanian reviews with
using an SVM classifier. Furthermore, the effect of different term weighting
schemes, stemmers, stop words removal, and feature models on the performance
were investigated. The experimental results showed that the best performance of
the SVM classifier was obtained after the SVM and correlation feature selection
methods had been combined with the uni-gram model.
Separating common (global and local) and distinct variation in multiple mixed types data sets
Authors: Yipeng Song, Johan A. Westerhuis, Age K. Smilde
Date: 2019-02-17 00:00:00
Tasks: Model Selection

Multiple sets of measurements on the same objects obtained from different platforms may reflect partially complementary information of the studied system. The integrative analysis of such data sets not only provides us with the opportunity of a deeper understanding of the studied system, but also introduces some new statistical challenges. First, the separation of information that is common across all or some of the data sets, and the information that is specific to each data set is problematic. Furthermore, these data sets are often a mix of quantitative and discrete (binary or categorical) data types, while commonly used data fusion methods require all data sets to be quantitative. In this paper, we propose an exponential family simultaneous component analysis (ESCA) model to tackle the potential mixed data types problem of multiple data sets. In addition, a structured sparse pattern of the loading matrix is induced through a nearly unbiased group concave penalty to disentangle the global, local common and distinct information of the multiple data sets. A Majorization-Minimization based algorithm is derived to fit the proposed model. Analytic solutions are derived for updating all the parameters of the model in each iteration, and the algorithm will decrease the objective function in each iteration monotonically. For model selection, a missing value based cross validation procedure is implemented. The advantages of the proposed method in comparison with other approaches are assessed using comprehensive simulations as well as the analysis of real data from a chronic lymphocytic leukaemia (CLL) study. Availability: the codes to reproduce the results in this article are available at https://gitlab.com/uvabda.
A new Potential-Based Reward Shaping for Reinforcement Learning Agent
Authors: Babak Badnava, Mona Esmaeili, Nasser Mozayani, Payman Zarkesh-Ha
Date: 2019-02-17 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Potential-based reward shaping (PBRS) is a particular category of machine learning methods which aims to improve the learning speed of a reinforcement learning agent by extracting and utilizing extra knowledge while performing a task. There are two steps in the process of transfer learning: extracting knowledge from previously learned tasks and transferring that knowledge to use it in a target task. The latter step is well discussed in the literature with various methods being proposed for it, while the former has been explored less. With this in mind, the type of knowledge that is transmitted is very important and can lead to considerable improvement. Among the literature of both the transfer learning and the potential-based reward shaping, a subject that has never been addressed is the knowledge gathered during the learning process itself. In this paper, we presented a novel potential-based reward shaping method that attempted to extract knowledge from the learning process. The proposed method extracts knowledge from episodes' cumulative rewards. The proposed method has been evaluated in the Arcade learning environment and the results indicate an improvement in the learning process in both the single-task and the multi-task reinforcement learner agents.
Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences
Authors: Yixin Cao, Xiang Wang, Xiangnan He, Zikun Hu, Tat-Seng Chua
Date: 2019-02-17 00:00:00
Tasks: Graph Learning, Knowledge Graph Completion, Recommendation Systems, Relation

Incorporating knowledge graph (KG) into recommender system is promising in
improving the recommendation accuracy and explainability. However, existing
methods largely assume that a KG is complete and simply transfer the
"knowledge" in KG at the shallow level of entity raw data or embeddings. This
may lead to suboptimal performance, since a practical KG can hardly be
complete, and it is common that a KG has missing facts, relations, and
entities. Thus, we argue that it is crucial to consider the incomplete nature
of KG when incorporating it into recommender system.
  In this paper, we jointly learn the model of recommendation and knowledge
graph completion. Distinct from previous KG-based recommendation methods, we
transfer the relation information in KG, so as to understand the reasons that a
user likes an item. As an example, if a user has watched several movies
directed by (relation) the same person (entity), we can infer that the director
relation plays a critical role when the user makes the decision, thus help to
understand the user's preference at a finer granularity.
  Technically, we contribute a new translation-based recommendation model,
which specially accounts for various preferences in translating a user to an
item, and then jointly train it with a KG completion model by combining several
transfer schemes. Extensive experiments on two benchmark datasets show that our
method outperforms state-of-the-art KG-based recommendation methods. Further
analysis verifies the positive effect of joint training on both tasks of
recommendation and KG completion, and the advantage of our model in
understanding user preference. We publish our project at
https://github.com/TaoMiner/joint-kg-recommender.
Multiple Document Representations from News Alerts for Automated Bio-surveillance Event Detection
Authors: Aaron Tuor, Fnu Anubhav, Lauren Charles
Date: 2019-02-17 00:00:00
Tasks: Classification, Event Detection, General Classification, Information Retrieval, Retrieval

Due to globalization, geographic boundaries no longer serve as effective
shields for the spread of infectious diseases. In order to aid bio-surveillance
analysts in disease tracking, recent research has been devoted to developing
information retrieval and analysis methods utilizing the vast corpora of
publicly available documents on the internet. In this work, we present methods
for the automated retrieval and classification of documents related to active
public health events. We demonstrate classification performance on an
auto-generated corpus, using recurrent neural network, TF-IDF, and Naive Bayes
log count ratio document representations. By jointly modeling the title and
description of a document, we achieve 97% recall and 93.3% accuracy with our
best performing bio-surveillance event classification model: logistic
regression on the combined output from a pair of bidirectional recurrent neural
networks.
Learning Linear-Quadratic Regulators Efficiently with only $\sqrt{T}$ Regret
Authors: Alon Cohen, Tomer Koren, Yishay Mansour
Date: 2019-02-17 00:00:00
Tasks: Open-Ended Question Answering

We present the first computationally-efficient algorithm with $\widetilde
O(\sqrt{T})$ regret for learning in Linear Quadratic Control systems with
unknown dynamics. By that, we resolve an open question of Abbasi-Yadkori and
Szepesv\'ari (2011) and Dean, Mania, Matni, Recht, and Tu (2018).
Detecting Colorized Images via Convolutional Neural Networks: Toward High Accuracy and Good Generalization
Authors: Weize Quan, Dong-Ming Yan, Kai Wang, Xiaopeng Zhang, Denis Pellerin
Date: 2019-02-17 00:00:00
Tasks: Colorization, General Classification, Image Colorization

Image colorization achieves more and more realistic results with the
increasing computation power of recent deep learning techniques. It becomes
more difficult to identify the fake colorized images by human eyes. In this
work, we propose a novel forensic method to distinguish between natural images
(NIs) and colorized images (CIs) based on convolutional neural network (CNN).
Our method is able to achieve high classification accuracy and cope with the
challenging scenario of blind detection, i.e., no training sample is available
from "unknown" colorization algorithm that we may encounter during the testing
phase. This blind detection performance can be regarded as a generalization
performance. First, we design and implement a base network, which can attain
better performance in terms of classification accuracy and generalization (in
most cases) compared with state-of-the-art methods. Furthermore, we design a
new branch, which analyzes smaller regions of extracted features, and insert it
into the above base network. Consequently, our network can not only improve the
classification accuracy, but also enhance the generalization in the vast
majority of cases. To further improve the performance of blind detection, we
propose to automatically construct negative samples through linear
interpolation of paired natural and colorized images. Then, we progressively
insert these negative samples into the original training dataset and continue
to train the network. Experimental results demonstrate that our method can
achieve stable and high generalization performance when tested against
different state-of-the-art colorization algorithms.
LapEPI-Net: A Laplacian Pyramid EPI structure for Learning-based Dense Light Field Reconstruction
Authors: Gaochang Wu, Yebin Liu, Lu Fang, Tianyou Chai
Date: 2019-02-17 00:00:00
Tasks: Transfer Learning

For dense sampled light field (LF) reconstruction problem, existing
approaches focus on a depth-free framework to achieve non-Lambertian
performance. However, they trap in the trade-off "either aliasing or blurring"
problem, i.e., pre-filtering the aliasing components (caused by the angular
sparsity of the input LF) always leads to a blurry result. In this paper, we
intend to solve this challenge by introducing an elaborately designed epipolar
plane image (EPI) structure within a learning-based framework. Specifically, we
start by analytically showing that decreasing the spatial scale of an EPI shows
higher efficiency in addressing the aliasing problem than simply adopting
pre-filtering. Accordingly, we design a Laplacian Pyramid EPI (LapEPI)
structure that contains both low spatial scale EPI (for aliasing) and
high-frequency residuals (for blurring) to solve the trade-off problem. We then
propose a novel network architecture for the LapEPI structure, termed as
LapEPI-net. To ensure the non-Lambertian performance, we adopt a
transfer-learning strategy by first pre-training the network with natural
images then fine-tuning it with unstructured LFs. Extensive experiments
demonstrate the high performance and robustness of the proposed approach for
tackling the aliasing-or-blurring problem as well as the non-Lambertian
reconstruction.
Fast Pedestrian Detection based on T-CENTRIST in infrared image
Authors: Hongyin Ni, Fengping Li
Date: 2019-02-17 00:00:00
Tasks: Human Detection, Pedestrian Detection

Pedestrian detection is a research hotspot and a difficult issue in the computer vision such as the Intelligent Surveillance System, the Intelligent Transport System, robotics, and automotive safety. However, the human body's position, angle, and dress in a video scene are complicated and changeable, which have a great influence on the detection accuracy. In this paper, through the analysis on the pros and cons of Census Transform Histogram (CENTRIST), a novel feature is presented for human detection Ternary CENTRIST (T-CENTRIST). The T-CENTRIST feature takes the relationship between each pixel and its neighborhood pixels into account. Meanwhile, it also considers the relevancy among these neighborhood pixels. Therefore, the proposed feature description method can reflect the silhouette of pedestrian more adequately and accurately than that of CENTRIST. Second, we propose a fast pedestrian detection framework based on T-CENTRIST in infrared image, which introduces the idea of extended blocks and the integral image. Finally, experimental results verify the effectiveness of the proposed pedestrian detection method.
Twitch Plays Pokemon, Machine Learns Twitch: Unsupervised Context-Aware Anomaly Detection for Identifying Trolls in Streaming Data
Authors: Albert Haque
Date: 2019-02-17 00:00:00
Tasks: Anomaly Detection, Clustering, Contextual Anomaly Detection, FAD

With the increasing importance of online communities, discussion forums, and
customer reviews, Internet "trolls" have proliferated thereby making it
difficult for information seekers to find relevant and correct information. In
this paper, we consider the problem of detecting and identifying Internet
trolls, almost all of which are human agents. Identifying a human agent among a
human population presents significant challenges compared to detecting
automated spam or computerized robots. To learn a troll's behavior, we use
contextual anomaly detection to profile each chat user. Using clustering and
distance-based methods, we use contextual data such as the group's current
goal, the current time, and the username to classify each point as an anomaly.
A user whose features significantly differ from the norm will be classified as
a troll. We collected 38 million data points from the viral Internet fad,
Twitch Plays Pokemon. Using clustering and distance-based methods, we develop
heuristics for identifying trolls. Using MapReduce techniques for preprocessing
and user profiling, we are able to classify trolls based on 10 features
extracted from a user's lifetime history.
Using Persistent Homology to Quantify a Diurnal Cycle in Hurricane Felix
Authors: Sarah Tymochko, Elizabeth Munch, Jason Dunion, Kristen Corbosiero, Ryan Torn
Date: 2019-02-17 00:00:00
Tasks: Topological Data Analysis

The diurnal cycle of tropical cyclones (TCs) is a daily cycle in clouds that appears in satellite images and may have implications for TC structure and intensity. The diurnal pattern can be seen in infrared (IR) satellite imagery as cyclical pulses in the cloud field that propagate radially outward from the center of nearly all Atlantic-basin TCs. These diurnal pulses, a distinguishing characteristic of the TC diurnal cycle, begin forming in the storm's inner core near sunset each day and appear as a region of cooling cloud-top temperatures. The area of cooling takes on a ring-like appearance as cloud-top warming occurs on its inside edge and the cooling moves away from the storm overnight, reaching several hundred kilometers from the circulation center by the following afternoon. The state-of-the-art TC diurnal cycle measurement has a limited ability to analyze the behavior beyond qualitative observations. We present a method for quantifying the TC diurnal cycle using one-dimensional persistent homology, a tool from Topological Data Analysis, by tracking maximum persistence and quantifying the cycle using the discrete Fourier transform. Using Geostationary Operational Environmental Satellite IR imagery data from Hurricane Felix (2007), our method is able to detect an approximate daily cycle.