pwc-archive/papers-with-abstracts huggingface dataset:
Tensor Matched Kronecker-Structured Subspace Detection for Missing Information
Authors: Ishan Jindal, Matthew Nokleby
Date: 2018-10-25 00:00:00

We consider the problem of detecting whether a tensor signal having many
missing entities lies within a given low dimensional Kronecker-Structured (KS)
subspace. This is a matched subspace detection problem. Tensor matched subspace
detection problem is more challenging because of the intertwined signal
dimensions. We solve this problem by projecting the signal onto the Kronecker
structured subspace, which is a Kronecker product of different subspaces
corresponding to each signal dimension. Under this framework, we define the KS
subspaces and the orthogonal projection of the signal onto the KS subspace. We
prove that reliable detection is possible as long as the cardinality of the
missing signal is greater than the dimensions of the KS subspace by bounding
the residual energy of the sampling signal with high probability.
A Preliminary Study on Hyperparameter Configuration for Human Activity Recognition
Authors: Kemilly Dearo Garcia, Tiago Carvalho, João Mendes-Moreira, João M. P. Cardoso, André C. P. L. F. de Carvalho
Date: 2018-10-25 00:00:00
Tasks: Activity Recognition, Classification, General Classification, Human Activity Recognition

Human activity recognition (HAR) is a classification task that aims to
classify human activities or predict human behavior by means of features
extracted from sensors data. Typical HAR systems use wearable sensors and/or
handheld and mobile devices with built-in sensing capabilities. Due to the
widespread use of smartphones and to the inclusion of various sensors in all
contemporary smartphones (e.g., accelerometers and gyroscopes), they are
commonly used for extracting and collecting data from sensors and even for
implementing HAR systems. When using mobile devices, e.g., smartphones, HAR
systems need to deal with several constraints regarding battery, computation
and memory. These constraints enforce the need of a system capable of managing
its resources and maintain acceptable levels of classification accuracy.
Moreover, several factors can influence activity recognition, such as
classification models, sensors availability and size of data window for feature
extraction, making stable accuracy a difficult task. In this paper, we present
a semi-supervised classifier and a study regarding the influence of
hyperparameter configuration in classification accuracy, depending on the user
and the activities performed by each user. This study focuses on sensing data
provided by the PAMAP2 dataset. Experimental results show that it is possible
to maintain classification accuracy by adjusting hyperparameters, like window
size and windows overlap factor, depending on user and activity performed.
These experiments motivate the development of a system able to automatically
adapt hyperparameter settings for the activity performed by each user.
Differential Variable Speed Limits Control for Freeway Recurrent Bottlenecks via Deep Reinforcement learning
Authors: Yuankai Wu, Huachun Tan, Bin Ran
Date: 2018-10-25 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Variable speed limits (VSL) control is a flexible way to improve traffic
condition,increase safety and reduce emission. There is an emerging trend of
using reinforcement learning technique for VSL control and recent studies have
shown promising results. Currently, deep learning is enabling reinforcement
learning to develope autonomous control agents for problems that were
previously intractable. In this paper, we propose a more effective deep
reinforcement learning (DRL) model for differential variable speed limits
(DVSL) control, in which the dynamic and different speed limits among lanes can
be imposed. The proposed DRL models use a novel actor-critic architecture which
can learn a large number of discrete speed limits in a continues action space.
Different reward signals, e.g. total travel time, bottleneck speed, emergency
braking, and vehicular emission are used to train the DVSL controller, and
comparison between these reward signals are conducted. We test proposed DRL
baased DVSL controllers on a simulated freeway recurrent bottleneck. Results
show that the efficiency, safety and emissions can be improved by the proposed
method. We also show some interesting findings through the visulization of the
control policies generated from DRL models.
Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations
Authors: Sven Buechel, João Sedoc, H. Andrew Schwartz, Lyle Ungar
Date: 2018-10-25 00:00:00
Proceeding: COLING (PEOPLES) 2020 12
Tasks: Emotion Recognition, Word Embeddings

One of the major downsides of Deep Learning is its supposed need for vast amounts of training data. As such, these techniques appear ill-suited for NLP areas where annotated data is limited, such as less-resourced languages or emotion analysis, with its many nuanced and hard-to-acquire annotation formats. We conduct a questionnaire study indicating that indeed the vast majority of researchers in emotion analysis deems neural models inferior to traditional machine learning when training data is limited. In stark contrast to those survey results, we provide empirical evidence for English, Polish, and Portuguese that commonly used neural architectures can be trained on surprisingly few observations, outperforming $n$-gram based ridge regression on only 100 data points. Our analysis suggests that high-quality, pre-trained word embeddings are a main factor for achieving those results.
Training Generative Adversarial Networks Via Turing Test
Authors: Jianlin Su
Date: 2018-10-25 00:00:00

In this article, we introduce a new mode for training Generative Adversarial
Networks (GANs). Rather than minimizing the distance of evidence distribution
$\tilde{p}(x)$ and the generative distribution $q(x)$, we minimize the distance
of $\tilde{p}(x_r)q(x_f)$ and $\tilde{p}(x_f)q(x_r)$. This adversarial pattern
can be interpreted as a Turing test in GANs. It allows us to use information of
real samples during training generator and accelerates the whole training
procedure. We even find that just proportionally increasing the size of
discriminator and generator, it succeeds on 256x256 resolution without
adjusting hyperparameters carefully.
Understanding the Role of Two-Sided Argumentation in Online Consumer Reviews: A Language-Based Perspective
Authors: Bernhard Lutz, Nicolas Pröllochs, Dirk Neumann
Date: 2018-10-25 00:00:00

This paper examines the effect of two-sided argumentation on the perceived
helpfulness of online consumer reviews. In contrast to previous works, our
analysis thereby sheds light on the reception of reviews from a language-based
perspective. For this purpose, we propose an intriguing text analysis approach
based on distributed text representations and multi-instance learning to
operationalize the two-sidedness of argumentation in review texts. A subsequent
empirical analysis using a large corpus of Amazon reviews suggests that
two-sided argumentation in reviews significantly increases their helpfulness.
We find this effect to be stronger for positive reviews than for negative
reviews, whereas a higher degree of emotional language weakens the effect. Our
findings have immediate implications for retailer platforms, which can utilize
our results to optimize their customer feedback system and to present more
useful product reviews.
Alzheimer's Disease Diagnosis Based on Cognitive Methods in Virtual Environments and Emotions Analysis
Authors: Juan Manuel Fernández Montenegro
Date: 2018-10-25 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Emotion Recognition, Facial Expression Recognition, Facial Expression Recognition (FER), Motion Magnification

Dementia is a syndrome characterised by the decline of different cognitive
abilities. Alzheimer's Disease (AD) is the most common dementia affecting
cognitive domains such as memory and learning, perceptual-motion or executive
function. High rate of deaths and high cost for detection, treatments and
patient's care count amongst its consequences. Early detection of AD is
considered of high importance for improving the quality of life of patients and
their families. The aim of this thesis is to introduce novel non-invasive early
diagnosis methods in order to speed the diagnosis, reduce the associated costs
and make them widely accessible. Novel AD's screening tests based on virtual
environments using new immersive technologies combined with advanced Human
Computer Interaction (HCI) systems are introduced. Four tests demonstrate the
wide range of screening mechanisms based on cognitive domain impairments that
can be designed using virtual environments. The use of emotion recognition to
analyse AD symptoms has been also proposed. A novel multimodal dataset was
specifically created to remark the autobiographical memory deficits of AD
patients. Data from this dataset is used to introduce novel descriptors for
Electroencephalogram (EEG) and facial images data. EEG features are based on
quaternions in order to keep the correlation information between sensors,
whereas, for facial expression recognition, a preprocessing method for motion
magnification and descriptors based on origami crease pattern algorithm are
proposed to enhance facial micro-expressions. These features have been proved
on classifiers such as SVM and Adaboost for the classification of reactions to
autobiographical stimuli such as long and short term memories.
Evading classifiers in discrete domains with provable optimality guarantees
Authors: Bogdan Kulynych, Jamie Hayes, Nikita Samarin, Carmela Troncoso
Date: 2018-10-25 00:00:00
Tasks: Adversarial Robustness, Spam detection, Twitter Bot Detection, valid

Machine-learning models for security-critical applications such as bot, malware, or spam detection, operate in constrained discrete domains. These applications would benefit from having provable guarantees against adversarial examples. The existing literature on provable adversarial robustness of models, however, exclusively focuses on robustness to gradient-based attacks in domains such as images. These attacks model the adversarial cost, e.g., amount of distortion applied to an image, as a $p$-norm. We argue that this approach is not well-suited to model adversarial costs in constrained domains where not all examples are feasible. We introduce a graphical framework that (1) generalizes existing attacks in discrete domains, (2) can accommodate complex cost functions beyond $p$-norms, including financial cost incurred when attacking a classifier, and (3) efficiently produces valid adversarial examples with guarantees of minimal adversarial cost. These guarantees directly translate into a notion of adversarial robustness that takes into account domain constraints and the adversary's capabilities. We show how our framework can be used to evaluate security by crafting adversarial examples that evade a Twitter-bot detection classifier with provably minimal number of changes; and to build privacy defenses by crafting adversarial examples that evade a privacy-invasive website-fingerprinting classifier.
Sample Efficient Algorithms for Learning Quantum Channels in PAC Model and the Approximate State Discrimination Problem
Authors: Kai-Min Chung, Han-Hsuan Lin
Date: 2018-10-25 00:00:00
Tasks: PAC learning

We generalize the PAC (probably approximately correct) learning model to the quantum world by generalizing the concepts from classical functions to quantum processes, defining the problem of \emph{PAC learning quantum process}, and study its sample complexity. In the problem of PAC learning quantum process, we want to learn an $\epsilon$-approximate of an unknown quantum process $c^*$ from a known finite concept class $C$ with probability $1-\delta$ using samples $\{(x_1,c^*(x_1)),(x_2,c^*(x_2)),\dots\}$, where $\{x_1,x_2, \dots\}$ are computational basis states sampled from an unknown distribution $D$ and $\{c^*(x_1),c^*(x_2),\dots\}$ are the (possibly mixed) quantum states outputted by $c^*$. The special case of PAC-learning quantum process under constant input reduces to a natural problem which we named as approximate state discrimination, where we are given copies of an unknown quantum state $c^*$ from an known finite set $C$, and we want to learn with probability $1-\delta$ an $\epsilon$-approximate of $c^*$ with as few copies of $c^*$ as possible. We show that the problem of PAC learning quantum process can be solved with $$O\left(\frac{\log|C| + \log(1/ \delta)} { \epsilon^2}\right)$$ samples when the outputs are pure states and $$O\left(\frac{\log^3 |C|(\log |C|+\log(1/ \delta))} { \epsilon^2}\right)$$ samples if the outputs can be mixed. Some implications of our results are that we can PAC-learn a polynomial sized quantum circuit in polynomial samples and approximate state discrimination can be solved in polynomial samples even when concept class size $|C|$ is exponential in the number of qubits, an exponentially improvement over a full state tomography.
Practical Shape Analysis and Segmentation Methods for Point Cloud Models
Authors: Reed M. Williams, Horea T. Ilieş
Date: 2018-10-25 00:00:00
Tasks: Clustering, Surface Reconstruction

Current point cloud processing algorithms do not have the capability to
automatically extract semantic information from the observed scenes, except in
very specialized cases. Furthermore, existing mesh analysis paradigms cannot be
directly employed to automatically perform typical shape analysis tasks
directly on point cloud models.
  We present a potent framework for shape analysis, similarity, and
segmentation of noisy point cloud models for real objects of engineering
interest, models that may be incomplete. The proposed framework relies on
spectral methods and the heat diffusion kernel to construct compact shape
signatures, and we show that the framework supports a variety of clustering
techniques that have traditionally been applied only on mesh models. We
developed and implemented one practical and convergent estimate of the
Laplace-Beltrami operator for point clouds as well as a number of clustering
techniques adapted to work directly on point clouds to produce geometric
features of engineering interest. The key advantage of this framework is that
it supports practical shape analysis capabilities that operate directly on
point cloud models of objects without requiring surface reconstruction or
global meshing. We show that the proposed technique is robust against typical
noise present in possibly incomplete point clouds, and segment point clouds
scanned by depth cameras (e.g. Kinect) into semantically-meaningful sub-shapes.
HAR-Net:Fusing Deep Representation and Hand-crafted Features for Human Activity Recognition
Authors: Mingtao Dong, Jindong Han
Date: 2018-10-25 00:00:00
Tasks: Activity Recognition, Deep Learning, Feature Engineering, Human Activity Recognition

Wearable computing and context awareness are the focuses of study in the
field of artificial intelligence recently. One of the most appealing as well as
challenging applications is the Human Activity Recognition (HAR) utilizing
smart phones. Conventional HAR based on Support Vector Machine relies on
subjective manually extracted features. This approach is time and energy
consuming as well as immature in prediction due to the partial view toward
which features to be extracted by human. With the rise of deep learning,
artificial intelligence has been making progress toward being a mature
technology. This paper proposes a new approach based on deep learning and
traditional feature engineering called HAR-Net to address the issue related to
HAR. The study used the data collected by gyroscopes and acceleration sensors
in android smart phones. The raw sensor data was put into the HAR-Net proposed.
The HAR-Net fusing the hand-crafted features and high-level features extracted
from convolutional network to make prediction. The performance of the proposed
method was proved to be 0.9% higher than the original MC-SVM approach. The
experimental results on the UCI dataset demonstrate that fusing the two kinds
of features can make up for the shortage of traditional feature engineering and
deep learning techniques.
Bayesian Compression for Natural Language Processing
Authors: Nadezhda Chirkova, Ekaterina Lobacheva, Dmitry Vetrov
Date: 2018-10-25 00:00:00
Proceeding: EMNLP 2018 10

In natural language processing, a lot of the tasks are successfully solved
with recurrent neural networks, but such models have a huge number of
parameters. The majority of these parameters are often concentrated in the
embedding layer, which size grows proportionally to the vocabulary length. We
propose a Bayesian sparsification technique for RNNs which allows compressing
the RNN dozens or hundreds of times without time-consuming hyperparameters
tuning. We also generalize the model for vocabulary sparsification to filter
out unnecessary words and compress the RNN even further. We show that the
choice of the kept words is interpretable. Code is available on github:
https://github.com/tipt0p/SparseBayesianRNN
Adversarial Semantic Scene Completion from a Single Depth Image
Authors: Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari
Date: 2018-10-25 00:00:00

We propose a method to reconstruct, complete and semantically label a 3D
scene from a single input depth image. We improve the accuracy of the regressed
semantic 3D maps by a novel architecture based on adversarial learning. In
particular, we suggest using multiple adversarial loss terms that not only
enforce realistic outputs with respect to the ground truth, but also an
effective embedding of the internal features. This is done by correlating the
latent features of the encoder working on partial 2.5D data with the latent
features extracted from a variational 3D auto-encoder trained to reconstruct
the complete semantic scene. In addition, differently from other approaches
that operate entirely through 3D convolutions, at test time we retain the
original 2.5D structure of the input during downsampling to improve the
effectiveness of the internal representation of our model. We test our approach
on the main benchmark datasets for semantic scene completion to qualitatively
and quantitatively assess the effectiveness of our proposal.
Almost Optimal Algorithms for Linear Stochastic Bandits with Heavy-Tailed Payoffs
Authors: Han Shao, Xiaotian Yu, Irwin King, Michael R. Lyu
Date: 2018-10-25 00:00:00
Proceeding: NeurIPS 2018 12

In linear stochastic bandits, it is commonly assumed that payoffs are with
sub-Gaussian noises. In this paper, under a weaker assumption on noises, we
study the problem of \underline{lin}ear stochastic {\underline b}andits with
h{\underline e}avy-{\underline t}ailed payoffs (LinBET), where the
distributions have finite moments of order $1+\epsilon$, for some $\epsilon\in
(0,1]$. We rigorously analyze the regret lower bound of LinBET as
$\Omega(T^{\frac{1}{1+\epsilon}})$, implying that finite moments of order 2
(i.e., finite variances) yield the bound of $\Omega(\sqrt{T})$, with $T$ being
the total number of rounds to play bandits. The provided lower bound also
indicates that the state-of-the-art algorithms for LinBET are far from optimal.
By adopting median of means with a well-designed allocation of decisions and
truncation based on historical information, we develop two novel bandit
algorithms, where the regret upper bounds match the lower bound up to
polylogarithmic factors. To the best of our knowledge, we are the first to
solve LinBET optimally in the sense of the polynomial order on $T$. Our
proposed algorithms are evaluated based on synthetic datasets, and outperform
the state-of-the-art results.
Investigating the Automatic Classification of Algae Using Fusion of Spectral and Morphological Characteristics of Algae via Deep Residual Learning
Authors: Jason L. Deglint, Chao Jin, Alexander Wong
Date: 2018-10-25 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Management

Under the impact of global climate changes and human activities, harmful
algae blooms in surface waters have become a growing concern due to negative
impacts on water related industries. Therefore, reliable and cost effective
methods of quantifying the type and concentration of threshold levels of algae
cells has become critical for ensuring successful water management. In this
work, we present SAMSON, an innovative system to automatically classify
multiple types of algae from different phyla groups by combining standard
morphological features with their multi-wavelength signals. Two phyla with
focused investigation in this study are the Cyanophyta phylum (blue-green
algae), and the Chlorophyta phylum (green algae). We use a custom-designed
microscopy imaging system which is configured to image water samples at two
fluorescent wavelengths and seven absorption wavelengths using
discrete-wavelength high-powered light emitting diodes (LEDs). Powered by
computer vision and machine learning, we investigate the possibility and
effectiveness of automatic classification using a deep residual convolutional
neural network. More specifically, a classification accuracy of 96% was
achieved in an experiment conducted with six different algae types. This high
level of accuracy was achieved using a deep residual convolutional neural
network that learns the optimal combination of spectral and morphological
features. These findings elude to the possibility of leveraging a unique
fingerprint of algae cell (i.e. spectral wavelengths and morphological
features) to automatically distinguish different algae types. Our work herein
demonstrates that, when coupled with multi-band fluorescence microscopy,
machine learning algorithms can potentially be used as a robust and
cost-effective tool for identifying and enumerating algae cells.
Short utterance compensation in speaker verification via cosine-based teacher-student learning of speaker embeddings
Authors: Jee-weon Jung, Hee-Soo Heo, Hye-jin Shim, Ha-Jin Yu
Date: 2018-10-25 00:00:00
Tasks: Speaker Verification, Text-Independent Speaker Verification

The short duration of an input utterance is one of the most critical threats
that degrade the performance of speaker verification systems. This study aimed
to develop an integrated text-independent speaker verification system that
inputs utterances with short duration of 2 seconds or less. We propose an
approach using a teacher-student learning framework for this goal, applied to
short utterance compensation for the first time in our knowledge. The core
concept of the proposed system is to conduct the compensation throughout the
network that extracts the speaker embedding, mainly in phonetic-level, rather
than compensating via a separate system after extracting the speaker embedding.
In the proposed architecture, phonetic-level features where each feature
represents a segment of 130 ms are extracted using convolutional layers. A
layer of gated recurrent units extracts an utterance-level feature using
phonetic-level features. The proposed approach also adopts a new objective
function for teacher-student learning that considers both Kullback-Leibler
divergence of output layers and cosine distance of speaker embeddings layers.
Experiments were conducted using deep neural networks that take raw waveforms
as input, and output speaker embeddings on VoxCeleb1 dataset. The proposed
model could compensate approximately 65 \% of the performance degradation due
to the shortened duration.