pwc-archive/papers-with-abstracts huggingface dataset:
IntelligentCrowd: Mobile Crowdsensing via Multi-Agent Reinforcement Learning
Authors: Yize Chen, Hao Wang
Date: 2018-09-20 00:00:00
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The prosperity of smart mobile devices has made mobile crowdsensing (MCS) a promising paradigm for completing complex sensing and computation tasks. In the past, great efforts have been made on the design of incentive mechanisms and task allocation strategies from MCS platform's perspective to motivate mobile users' participation. However, in practice, MCS participants face many uncertainties coming from their sensing environment as well as other participants' strategies, and how do they interact with each other and make sensing decisions is not well understood. In this paper, we take MCS participants' perspective to derive an online sensing policy to maximize their payoffs via MCS participation. Specifically, we model the interactions of mobile users and sensing environments as a multi-agent Markov decision process. Each participant cannot observe others' decisions, but needs to decide her effort level in sensing tasks only based on local information, e.g., its own record of sensed signals' quality. To cope with the stochastic sensing environment, we develop an intelligent crowdsensing algorithm IntelligentCrowd by leveraging the power of multi-agent reinforcement learning (MARL). Our algorithm leads to the optimal sensing policy for each user to maximize the expected payoff against stochastic sensing environments, and can be implemented at individual participant's level in a distributed fashion. Numerical simulations demonstrate that IntelligentCrowd significantly improves users' payoffs in sequential MCS tasks under various sensing dynamics.
Recurrent Neural Networks based Obesity Status Prediction Using Activity Data
Authors: Qinghan Xue, Xiaoran Wang, Samuel Meehan, Jilong Kuang, Alex Gao, Mooi Choo Chuah
Date: 2018-09-20 00:00:00
Tasks: BIG-bench Machine Learning

Obesity is a serious public health concern world-wide, which increases the
risk of many diseases, including hypertension, stroke, and type 2 diabetes. To
tackle this problem, researchers across the health ecosystem are collecting
diverse types of data, which includes biomedical, behavioral and activity, and
utilizing machine learning techniques to mine hidden patterns for obesity
status improvement prediction. While existing machine learning methods such as
Recurrent Neural Networks (RNNs) can provide exceptional results, it is
challenging to discover hidden patterns of the sequential data due to the
irregular observation time instances. Meanwhile, the lack of understanding of
why those learning models are effective also limits further improvements on
their architectures. Thus, in this work, we develop a RNN based time-aware
architecture to tackle the challenging problem of handling irregular
observation times and relevant feature extractions from longitudinal patient
records for obesity status improvement prediction. To improve the prediction
performance, we train our model using two data sources: (i) electronic medical
records containing information regarding lab tests, diagnoses, and
demographics; (ii) continuous activity data collected from popular wearables.
Evaluations of real-world data demonstrate that our proposed method can capture
the underlying structures in users' time sequences with irregularities, and
achieve an accuracy of 77-86% in predicting the obesity status improvement.
Metric Learning for Phoneme Perception
Authors: Yair Lakretz, Gal Chechik, Evan-Gary Cohen, Alessandro Treves, Naama Friedmann
Date: 2018-09-20 00:00:00
Tasks: Metric Learning

Metric functions for phoneme perception capture the similarity structure
among phonemes in a given language and therefore play a central role in
phonology and psycho-linguistics. Various phenomena depend on phoneme
similarity, such as spoken word recognition or serial recall from verbal
working memory. This study presents a new framework for learning a metric
function for perceptual distances among pairs of phonemes. Previous studies
have proposed various metric functions, from simple measures counting the
number of phonetic dimensions that two phonemes share (place-,
manner-of-articulation and voicing), to more sophisticated ones such as
deriving perceptual distances based on the number of natural classes that both
phonemes belong to. However, previous studies have manually constructed the
metric function, which may lead to unsatisfactory account of the empirical
data. This study presents a framework to derive the metric function from
behavioral data on phoneme perception using learning algorithms. We first show
that this approach outperforms previous metrics suggested in the literature in
predicting perceptual distances among phoneme pairs. We then study several
metric functions derived by the learning algorithms and show how perceptual
saliencies of phonological features can be derived from them. For English, we
show that the derived perceptual saliencies are in accordance with a previously
described order among phonological features and show how the framework extends
the results to more features. Finally, we explore how the metric function and
perceptual saliencies of phonological features may vary across languages. To
this end, we compare results based on two English datasets and a new dataset
that we have collected for Hebrew.
Logically-Constrained Neural Fitted Q-Iteration
Authors: Mohammadhosein Hasanbeig, Alessandro Abate, Daniel Kroening
Date: 2018-09-20 00:00:00

We propose a method for efficient training of Q-functions for
continuous-state Markov Decision Processes (MDPs) such that the traces of the
resulting policies satisfy a given Linear Temporal Logic (LTL) property. LTL, a
modal logic, can express a wide range of time-dependent logical properties
(including "safety") that are quite similar to patterns in natural language. We
convert the LTL property into a limit deterministic Buchi automaton and
construct an on-the-fly synchronised product MDP. The control policy is then
synthesised by defining an adaptive reward function and by applying a modified
neural fitted Q-iteration algorithm to the synchronised structure, assuming
that no prior knowledge is available from the original MDP. The proposed method
is evaluated in a numerical study to test the quality of the generated control
policy and is compared with conventional methods for policy synthesis such as
MDP abstraction (Voronoi quantizer) and approximate dynamic programming (fitted
value iteration).
Bootstrapping Transliteration with Constrained Discovery for Low-Resource Languages
Authors: Shyam Upadhyay, Jordan Kodner, Dan Roth
Date: 2018-09-20 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Entity Linking, Transliteration

Generating the English transliteration of a name written in a foreign script
is an important and challenging step in multilingual knowledge acquisition and
information extraction. Existing approaches to transliteration generation
require a large (>5000) number of training examples. This difficulty contrasts
with transliteration discovery, a somewhat easier task that involves picking a
plausible transliteration from a given list. In this work, we present a
bootstrapping algorithm that uses constrained discovery to improve generation,
and can be used with as few as 500 training examples, which we show can be
sourced from annotators in a matter of hours. This opens the task to languages
for which large number of training examples are unavailable. We evaluate
transliteration generation performance itself, as well the improvement it
brings to cross-lingual candidate generation for entity linking, a typical
downstream task. We present a comprehensive evaluation of our approach on nine
languages, each written in a unique script.
Understanding Behavior of Clinical Models under Domain Shifts
Authors: Jayaraman J. Thiagarajan, Deepta Rajan, Prasanna Sattigeri
Date: 2018-09-20 00:00:00
Tasks: Domain Adaptation, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Prognosis, Unsupervised Domain Adaptation

The hypothesis that computational models can be reliable enough to be adopted in prognosis and patient care is revolutionizing healthcare. Deep learning, in particular, has been a game changer in building predictive models, thus leading to community-wide data curation efforts. However, due to inherent variabilities in population characteristics and biological systems, these models are often biased to the training datasets. This can be limiting when models are deployed in new environments, when there are systematic domain shifts not known a priori. In this paper, we propose to emulate a large class of domain shifts, that can occur in clinical settings, with a given dataset, and argue that evaluating the behavior of predictive models in light of those shifts is an effective way to quantify their reliability. More specifically, we develop an approach for building realistic scenarios, based on analysis of \textit{disease landscapes} in multi-label classification. Using the openly available MIMIC-III EHR dataset for phenotyping, for the first time, our work sheds light into data regimes where deep clinical models can fail to generalize. This work emphasizes the need for novel validation mechanisms driven by real-world domain shifts in AI for healthcare.
Dynamic Weights in Multi-Objective Deep Reinforcement Learning
Authors: Axel Abels, Diederik M. Roijers, Tom Lenaerts, Ann Nowé, Denis Steckelmacher
Date: 2018-09-20 00:00:00
Tasks: Deep Reinforcement Learning, Multi-Objective Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many real-world decision problems are characterized by multiple conflicting objectives which must be balanced based on their relative importance. In the dynamic weights setting the relative importance changes over time and specialized algorithms that deal with such change, such as a tabular Reinforcement Learning (RL) algorithm by Natarajan and Tadepalli (2005), are required. However, this earlier work is not feasible for RL settings that necessitate the use of function approximators. We generalize across weight changes and high-dimensional inputs by proposing a multi-objective Q-network whose outputs are conditioned on the relative importance of objectives and we introduce Diverse Experience Replay (DER) to counter the inherent non-stationarity of the Dynamic Weights setting. We perform an extensive experimental evaluation and compare our methods to adapted algorithms from Deep Multi-Task/Multi-Objective Reinforcement Learning and show that our proposed network in combination with DER dominates these adapted algorithms across weight change scenarios and problem domains.
Playing the Game of Universal Adversarial Perturbations
Authors: Julien Perolat, Mateusz Malinowski, Bilal Piot, Olivier Pietquin
Date: 2018-09-20 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

We study the problem of learning classifiers robust to universal adversarial
perturbations. While prior work approaches this problem via robust
optimization, adversarial training, or input transformation, we instead phrase
it as a two-player zero-sum game. In this new formulation, both players
simultaneously play the same game, where one player chooses a classifier that
minimizes a classification loss whilst the other player creates an adversarial
perturbation that increases the same loss when applied to every sample in the
training set. By observing that performing a classification (respectively
creating adversarial samples) is the best response to the other player, we
propose a novel extension of a game-theoretic algorithm, namely fictitious
play, to the domain of training robust classifiers. Finally, we empirically
show the robustness and versatility of our approach in two defence scenarios
where universal attacks are performed on several image classification datasets
-- CIFAR10, CIFAR100 and ImageNet.
Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images
Authors: Zahra Sobhaninia, Safiyeh Rezaei, Alireza Noroozi, Mehdi Ahmadi, Hamidreza Zarrabi, Nader Karimi, Ali Emami, Shadrokh Samavi
Date: 2018-09-20 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

Recently deep learning has been playing a major role in the field of computer
vision. One of its applications is the reduction of human judgment in the
diagnosis of diseases. Especially, brain tumor diagnosis requires high
accuracy, where minute errors in judgment may lead to disaster. For this
reason, brain tumor segmentation is an important challenge for medical
purposes. Currently several methods exist for tumor segmentation but they all
lack high accuracy. Here we present a solution for brain tumor segmenting by
using deep learning. In this work, we studied different angles of brain MR
images and applied different networks for segmentation. The effect of using
separate networks for segmentation of MR images is evaluated by comparing the
results with a single network. Experimental evaluations of the networks show
that Dice score of 0.73 is achieved for a single network and 0.79 in obtained
for multiple networks.
Rapid Customization for Event Extraction
Authors: Yee Seng Chan, Joshua Fasching, Haoling Qiu, Bonan Min
Date: 2018-09-20 00:00:00
Proceeding: ACL 2019 7
Tasks: Event Extraction

We present a system for rapidly customizing event extraction capability to
find new event types and their arguments. The system allows a user to find,
expand and filter event triggers for a new event type by exploring an
unannotated corpus. The system will then automatically generate mention-level
event annotation automatically, and train a Neural Network model for finding
the corresponding event. Additionally, the system uses the ACE corpus to train
an argument model for extracting Actor, Place, and Time arguments for any event
types, including ones not seen in its training data. Experiments show that with
less than 10 minutes of human effort per event type, the system achieves good
performance for 67 novel event types. The code, documentation, and a
demonstration video will be released as open source on github.com.
Deep Domain Adaptation under Deep Label Scarcity
Authors: Amar Prakash Azad, Dinesh Garg, Priyanka Agrawal, Arun Kumar
Date: 2018-09-20 00:00:00
Tasks: Domain Adaptation, Transductive Learning

The goal behind Domain Adaptation (DA) is to leverage the labeled examples
from a source domain so as to infer an accurate model in a target domain where
labels are not available or in scarce at the best. A state-of-the-art approach
for the DA is due to (Ganin et al. 2016), known as DANN, where they attempt to
induce a common representation of source and target domains via adversarial
training. This approach requires a large number of labeled examples from the
source domain to be able to infer a good model for the target domain. However,
in many situations obtaining labels in the source domain is expensive which
results in deteriorated performance of DANN and limits its applicability in
such scenarios. In this paper, we propose a novel approach to overcome this
limitation. In our work, we first establish that DANN reduces the original DA
problem into a semi-supervised learning problem over the space of common
representation. Next, we propose a learning approach, namely TransDANN, that
amalgamates adversarial learning and transductive learning to mitigate the
detrimental impact of limited source labels and yields improved performance.
Experimental results (both on text and images) show a significant boost in the
performance of TransDANN over DANN under such scenarios. We also provide
theoretical justification for the performance boost.
Human activity recognition based on time series analysis using U-Net
Authors: Yong Zhang, Yu Zhang, Zhao Zhang, Jie Bao, Yunpeng Song
Date: 2018-09-20 00:00:00
Tasks: Activity Recognition, Gesture Recognition, Human Activity Recognition, Time Series, Time Series Analysis

Traditional human activity recognition (HAR) based on time series adopts
sliding window analysis method. This method faces the multi-class window
problem which mistakenly labels different classes of sampling points within a
window as a class. In this paper, a HAR algorithm based on U-Net is proposed to
perform activity labeling and prediction at each sampling point. The activity
data of the triaxial accelerometer is mapped into an image with the single
pixel column and multi-channel which is input into the U-Net network for
training and recognition. Our proposal can complete the pixel-level gesture
recognition function. The method does not need manual feature extraction and
can effectively identify short-term behaviors in long-term activity sequences.
We collected the Sanitation dataset and tested the proposed scheme with four
open data sets. The experimental results show that compared with Support Vector
Machine (SVM), k-Nearest Neighbor (kNN), Decision Tree(DT), Quadratic
Discriminant Analysis (QDA), Convolutional Neural Network (CNN) and Fully
Convolutional Networks (FCN) methods, our proposal has the highest accuracy and
F1-socre in each dataset, and has stable performance and high robustness. At
the same time, after the U-Net has finished training, our proposal can achieve
fast enough recognition speed.
Global and Local Consistent Wavelet-domain Age Synthesis
Authors: Pei-Pei Li, Yibo Hu, Ran He, Zhenan Sun
Date: 2018-09-20 00:00:00
Tasks: Age Estimation, Face Verification, Generative Adversarial Network, Human Aging, MORPH

Age synthesis is a challenging task due to the complicated and non-linear
transformation in human aging process. Aging information is usually reflected
in local facial parts, such as wrinkles at the eye corners. However, these
local facial parts contribute less in previous GAN based methods for age
synthesis. To address this issue, we propose a Wavelet-domain Global and Local
Consistent Age Generative Adversarial Network (WaveletGLCA-GAN), in which one
global specific network and three local specific networks are integrated
together to capture both global topology information and local texture details
of human faces. Different from the most existing methods that modeling age
synthesis in image-domain, we adopt wavelet transform to depict the textual
information in frequency-domain. %Moreover, to achieve accurate age generation
under the premise of preserving the identity information, age estimation
network and face verification network are employed. Moreover, five types of
losses are adopted: 1) adversarial loss aims to generate realistic wavelets; 2)
identity preserving loss aims to better preserve identity information; 3) age
preserving loss aims to enhance the accuracy of age synthesis; 4) pixel-wise
loss aims to preserve the background information of the input face; 5) the
total variation regularization aims to remove ghosting artifacts. Our method is
evaluated on three face aging datasets, including CACD2000, Morph and FG-NET.
Qualitative and quantitative experiments show the superiority of the proposed
method over other state-of-the-arts.
Distribution Networks for Open Set Learning
Authors: Chengsheng Mao, Liang Yao, Yuan Luo
Date: 2018-09-20 00:00:00
Tasks: General Classification, Open Set Learning

In open set learning, a model must be able to generalize to novel classes
when it encounters a sample that does not belong to any of the classes it has
seen before. Open set learning poses a realistic learning scenario that is
receiving growing attention. Existing studies on open set learning mainly
focused on detecting novel classes, but few studies tried to model them for
differentiating novel classes. In this paper, we recognize that novel classes
should be different from each other, and propose distribution networks for open
set learning that can model different novel classes based on probability
distributions. We hypothesize that, through a certain mapping, samples from
different classes with the same classification criterion should follow
different probability distributions from the same distribution family. A deep
neural network is learned to map the samples in the original feature space to a
latent space where the distributions of known classes can be jointly learned
with the network. We additionally propose a distribution parameter transfer and
updating strategy for novel class modeling when a novel class is detected in
the latent space. By novel class modeling, the detected novel classes can serve
as known classes to the subsequent classification. Our experimental results on
image datasets MNIST and CIFAR10 show that the distribution networks can detect
novel classes accurately, and model them well for the subsequent classification
tasks.
Towards the Development of a Rule-based Drought Early Warning Expert Systems using Indigenous Knowledge
Authors: A. K. Akanbi, M. Masinde
Date: 2018-09-19 00:00:00

Drought forecasting and prediction is a complicated process due to the
complexity and scalability of the environmental parameters involved. Hence, it
required a high level of expertise to predict. In this paper, we describe the
research and development of a rule-based drought early warning expert systems
(RB-DEWES) for forecasting drought using local indigenous knowledge obtained
from domain experts. The system generates inference by using rule set and
provides drought advisory information with attributed certainty factor (CF)
based on the user's input. The system is believed to be the first expert system
for drought forecasting to use local indigenous knowledge on drought. The
architecture and components such as knowledge base, JESS inference engine and
model base of the system and their functions are presented.
Efficient Formal Safety Analysis of Neural Networks
Authors: Shiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang, Suman Jana
Date: 2018-09-19 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Adversarial Attack, Adversarial Defense, Autonomous Driving, Collision Avoidance, Malware Detection

Neural networks are increasingly deployed in real-world safety-critical
domains such as autonomous driving, aircraft collision avoidance, and malware
detection. However, these networks have been shown to often mispredict on
inputs with minor adversarial or even accidental perturbations. Consequences of
such errors can be disastrous and even potentially fatal as shown by the recent
Tesla autopilot crash. Thus, there is an urgent need for formal analysis
systems that can rigorously check neural networks for violations of different
safety properties such as robustness against adversarial perturbations within a
certain $L$-norm of a given image. An effective safety analysis system for a
neural network must be able to either ensure that a safety property is
satisfied by the network or find a counterexample, i.e., an input for which the
network will violate the property. Unfortunately, most existing techniques for
performing such analysis struggle to scale beyond very small networks and the
ones that can scale to larger networks suffer from high false positives and
cannot produce concrete counterexamples in case of a property violation. In
this paper, we present a new efficient approach for rigorously checking
different safety properties of neural networks that significantly outperforms
existing approaches by multiple orders of magnitude. Our approach can check
different safety properties and find concrete counterexamples for networks that
are 10$\times$ larger than the ones supported by existing analysis techniques.
We believe that our approach to estimating tight output bounds of a network for
a given input range can also help improve the explainability of neural networks
and guide the training process of more robust neural networks.