pwc-archive/papers-with-abstracts huggingface dataset:
Automated Assessment of Facial Wrinkling: a case study on the effect of smoking
Authors: Omaima FathElrahman Osman, Remah Mutasim Ibrahim Elbashir, Imad Eldain Abbass, Connah Kendrick, Manu Goyal, Moi Hoon Yap
Date: 2017-08-06 00:00:00
Tasks: Face Model

Facial wrinkle is one of the most prominent biological changes that
accompanying the natural aging process. However, there are some external
factors contributing to premature wrinkles development, such as sun exposure
and smoking. Clinical studies have shown that heavy smoking causes premature
wrinkles development. However, there is no computerised system that can
automatically assess the facial wrinkles on the whole face. This study
investigates the effect of smoking on facial wrinkling using a social habit
face dataset and an automated computerised computer vision algorithm. The
wrinkles pattern represented in the intensity of 0-255 was first extracted
using a modified Hybrid Hessian Filter. The face was divided into ten
predefined regions, where the wrinkles in each region was extracted. Then the
statistical analysis was performed to analyse which region is effected mainly
by smoking. The result showed that the density of wrinkles for smokers in two
regions around the mouth was significantly higher than the non-smokers, at
p-value of 0.05. Other regions are inconclusive due to lack of large scale
dataset. Finally, the wrinkle was visually compared between smoker and
non-smoker faces by generating a generic 3D face model.
SenseNet: 3D Objects Database and Tactile Simulator
Authors: Jason Toy
Date: 2017-12-31 00:00:00
Tasks: BIG-bench Machine Learning

The majority of artificial intelligence research, as it relates from which to
biological senses has been focused on vision. The recent explosion of machine
learning and in particular, dee p learning, can be partially attributed to the
release of high quality data sets for algorithm s from which to model the world
on. Thus, most of these datasets are comprised of images. We believe that
focusing on sensorimotor systems and tactile feedback will create algorithms
that better mimic human intelligence. Here we present SenseNet: a collection of
tactile simulators and a large scale dataset of 3D objects for manipulation.
SenseNet was created for the purpose of researching and training Artificial
Intelligences (AIs) to interact with the environment via sensorimotor neural
systems and tactile feedback. We aim to accelerate that same explosion in image
processing, but for the domain of tactile feedback and sensorimotor research.
We hope that SenseNet can offer researchers in both the machine learning and
computational neuroscience communities brand new opportunities and avenues to
explore.
Evolving a Vector Space with any Generating Set
Authors: Richard Nock, Frank Nielsen
Date: 2017-04-10 00:00:00

In Valiant's model of evolution, a class of representations is evolvable iff
a polynomial-time process of random mutations guided by selection converges
with high probability to a representation as $\epsilon$-close as desired from
the optimal one, for any required $\epsilon>0$. Several previous positive
results exist that can be related to evolving a vector space, but each former
result imposes disproportionate representations or restrictions on
(re)initialisations, distributions, performance functions and/or the mutator.
In this paper, we show that all it takes to evolve a normed vector space is
merely a set that generates the space. Furthermore, it takes only
$\tilde{O}(1/\epsilon^2)$ steps and it is essentially stable, agnostic and
handles target drifts that rival some proven in fairly restricted settings. Our
algorithm can be viewed as a close relative to a popular fifty-years old
gradient-free optimization method for which little is still known from the
convergence standpoint: Nelder-Mead simplex method.
How will the Internet of Things enable Augmented Personalized Health?
Authors: Amit Sheth, Utkarshani Jaimini, Hong Yung Yip
Date: 2017-12-31 00:00:00
Tasks: Clinical Knowledge, Management

Internet-of-Things (IoT) is profoundly redefining the way we create, consume,
and share information. Health aficionados and citizens are increasingly using
IoT technologies to track their sleep, food intake, activity, vital body
signals, and other physiological observations. This is complemented by IoT
systems that continuously collect health-related data from the environment and
inside the living quarters. Together, these have created an opportunity for a
new generation of healthcare solutions. However, interpreting data to
understand an individual's health is challenging. It is usually necessary to
look at that individual's clinical record and behavioral information, as well
as social and environmental information affecting that individual. Interpreting
how well a patient is doing also requires looking at his adherence to
respective health objectives, application of relevant clinical knowledge and
the desired outcomes.
  We resort to the vision of Augmented Personalized Healthcare (APH) to exploit
the extensive variety of relevant data and medical knowledge using Artificial
Intelligence (AI) techniques to extend and enhance human health to presents
various stages of augmented health management strategies: self-monitoring,
self-appraisal, self-management, intervention, and disease progress tracking
and prediction. kHealth technology, a specific incarnation of APH, and its
application to Asthma and other diseases are used to provide illustrations and
discuss alternatives for technology-assisted health management. Several
prominent efforts involving IoT and patient-generated health data (PGHD) with
respect converting multimodal data into actionable information (big data to
smart data) are also identified. Roles of three components in an evidence-based
semantic perception approach- Contextualization, Abstraction, and
Personalization are discussed.
Mined Semantic Analysis: A New Concept Space Model for Semantic Representation of Textual Data
Authors: Walid Shalaby, Wlodek Zadrozny
Date: 2015-12-10 00:00:00
Tasks: Implicit Relations

Mined Semantic Analysis (MSA) is a novel concept space model which employs
unsupervised learning to generate semantic representations of text. MSA
represents textual structures (terms, phrases, documents) as a Bag of Concepts
(BoC) where concepts are derived from concept rich encyclopedic corpora.
Traditional concept space models exploit only target corpus content to
construct the concept space. MSA, alternatively, uncovers implicit relations
between concepts by mining for their associations (e.g., mining Wikipedia's
"See also" link graph). We evaluate MSA's performance on benchmark datasets for
measuring semantic relatedness of words and sentences. Empirical results show
competitive performance of MSA compared to prior state-of-the-art methods.
Additionally, we introduce the first analytical study to examine statistical
significance of results reported by different semantic relatedness methods. Our
study shows that, the nuances of results across top performing methods could be
statistically insignificant. The study positions MSA as one of state-of-the-art
methods for measuring semantic relatedness, besides the inherent
interpretability and simplicity of the generated semantic representation.
A General Framework for Adversarial Examples with Objectives
Authors: Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer, Michael K. Reiter
Date: 2017-12-31 00:00:00
Tasks: Face Recognition

Images perturbed subtly to be misclassified by neural networks, called
adversarial examples, have emerged as a technically deep challenge and an
important concern for several application domains. Most research on adversarial
examples takes as its only constraint that the perturbed images are similar to
the originals. However, real-world application of these ideas often requires
the examples to satisfy additional objectives, which are typically enforced
through custom modifications of the perturbation process. In this paper, we
propose adversarial generative nets (AGNs), a general methodology to train a
generator neural network to emit adversarial examples satisfying desired
objectives. We demonstrate the ability of AGNs to accommodate a wide range of
objectives, including imprecise ones difficult to model, in two application
domains. In particular, we demonstrate physical adversarial examples---eyeglass
frames designed to fool face recognition---with better robustness,
inconspicuousness, and scalability than previous approaches, as well as a new
attack to fool a handwritten-digit classifier.
Mammalian Value Systems
Authors: Gopal P. Sarma, Nick J. Hay
Date: 2016-07-28 00:00:00
Tasks: Autonomous Vehicles, Ethics

Characterizing human values is a topic deeply interwoven with the sciences,
humanities, art, and many other human endeavors. In recent years, a number of
thinkers have argued that accelerating trends in computer science, cognitive
science, and related disciplines foreshadow the creation of intelligent
machines which meet and ultimately surpass the cognitive abilities of human
beings, thereby entangling an understanding of human values with future
technological development. Contemporary research accomplishments suggest
sophisticated AI systems becoming widespread and responsible for managing many
aspects of the modern world, from preemptively planning users' travel schedules
and logistics, to fully autonomous vehicles, to domestic robots assisting in
daily living. The extrapolation of these trends has been most forcefully
described in the context of a hypothetical "intelligence explosion," in which
the capabilities of an intelligent software agent would rapidly increase due to
the presence of feedback loops unavailable to biological organisms. The
possibility of superintelligent agents, or simply the widespread deployment of
sophisticated, autonomous AI systems, highlights an important theoretical
problem: the need to separate the cognitive and rational capacities of an agent
from the fundamental goal structure, or value system, which constrains and
guides the agent's actions. The "value alignment problem" is to specify a goal
structure for autonomous agents compatible with human values. In this brief
article, we suggest that recent ideas from affective neuroscience and related
disciplines aimed at characterizing neurological and behavioral universals in
the mammalian class provide important conceptual foundations relevant to
describing human values. We argue that the notion of "mammalian value systems"
points to a potential avenue for fundamental research in AI safety and AI
ethics.
Robust Computer Algebra, Theorem Proving, and Oracle AI
Authors: Gopal P. Sarma, Nick J. Hay
Date: 2017-08-08 00:00:00
Tasks: Automated Theorem Proving, Question Answering

In the context of superintelligent AI systems, the term "oracle" has two
meanings. One refers to modular systems queried for domain-specific tasks.
Another usage, referring to a class of systems which may be useful for
addressing the value alignment and AI control problems, is a superintelligent
AI system that only answers questions. The aim of this manuscript is to survey
contemporary research problems related to oracles which align with long-term
research goals of AI safety. We examine existing question answering systems and
argue that their high degree of architectural heterogeneity makes them poor
candidates for rigorous analysis as oracles. On the other hand, we identify
computer algebra systems (CASs) as being primitive examples of domain-specific
oracles for mathematics and argue that efforts to integrate computer algebra
systems with theorem provers, systems which have largely been developed
independent of one another, provide a concrete set of problems related to the
notion of provable safety that has emerged in the AI safety community. We
review approaches to interfacing CASs with theorem provers, describe
well-defined architectural deficiencies that have been identified with CASs,
and suggest possible lines of research and practical software projects for
scientists interested in AI safety.
Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification
Authors: Abien Fred Agarap
Date: 2017-12-31 00:00:00
Tasks: General Classification, Malware Classification

Effective and efficient mitigation of malware is a long-time endeavor in the
information security community. The development of an anti-malware system that
can counteract an unknown malware is a prolific activity that may benefit
several sectors. We envision an intelligent anti-malware system that utilizes
the power of deep learning (DL) models. Using such models would enable the
detection of newly-released malware through mathematical generalization. That
is, finding the relationship between a given malware $x$ and its corresponding
malware family $y$, $f: x \mapsto y$. To accomplish this feat, we used the
Malimg dataset (Nataraj et al., 2011) which consists of malware images that
were processed from malware binaries, and then we trained the following DL
models 1 to classify each malware family: CNN-SVM (Tang, 2013), GRU-SVM
(Agarap, 2017), and MLP-SVM. Empirical evidence has shown that the GRU-SVM
stands out among the DL models with a predictive accuracy of ~84.92%. This
stands to reason for the mentioned model had the relatively most sophisticated
architecture design among the presented models. The exploration of an even more
optimal DL-SVM model is the next stage towards the engineering of an
intelligent anti-malware system.
Finding Efficient Swimming Strategies in a Three Dimensional Chaotic Flow by Reinforcement Learning
Authors: K. Gustavsson, L. Biferale, A. Celani, S. Colabrese
Date: 2017-11-15 00:00:00
Tasks: Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We apply a reinforcement learning algorithm to show how smart particles can
learn approximately optimal strategies to navigate in complex flows. In this
paper we consider microswimmers in a paradigmatic three-dimensional case given
by a stationary superposition of two Arnold-Beltrami-Childress flows with
chaotic advection along streamlines. In such a flow, we study the evolution of
point-like particles which can decide in which direction to swim, while keeping
the velocity amplitude constant. We show that it is sufficient to endow the
swimmers with a very restricted set of actions (six fixed swimming directions
in our case) to have enough freedom to find efficient strategies to move upward
and escape local fluid traps. The key ingredient is the
learning-from-experience structure of the algorithm, which assigns positive or
negative rewards depending on whether the taken action is, or is not,
profitable for the predetermined goal in the long term horizon. This is another
example supporting the efficiency of the reinforcement learning approach to
learn how to accomplish difficult tasks in complex fluid environments.
Learning Relevant Features of Data with Multi-scale Tensor Networks
Authors: E. M. Stoudenmire
Date: 2017-12-31 00:00:00
Tasks: General Classification, Tensor Networks

Inspired by coarse-graining approaches used in physics, we show how similar
algorithms can be adapted for data. The resulting algorithms are based on
layered tree tensor networks and scale linearly with both the dimension of the
input and the training set size. Computing most of the layers with an
unsupervised algorithm, then optimizing just the top layer for supervised
classification of the MNIST and fashion-MNIST data sets gives very good
results. We also discuss mixing a prior guess for supervised weights together
with an unsupervised representation of the data, yielding a smaller number of
features nevertheless able to give good performance.
Routing Networks: Adaptive Selection of Non-linear Functions for Multi-Task Learning
Authors: Clemens Rosenbaum, Tim Klinger, Matthew Riemer
Date: 2017-11-03 00:00:00
Proceeding: ICLR 2018 1
Tasks: Multi-agent Reinforcement Learning, Multi-Task Learning, Reinforcement Learning

Multi-task learning (MTL) with neural networks leverages commonalities in
tasks to improve performance, but often suffers from task interference which
reduces the benefits of transfer. To address this issue we introduce the
routing network paradigm, a novel neural network and training algorithm. A
routing network is a kind of self-organizing neural network consisting of two
components: a router and a set of one or more function blocks. A function block
may be any neural network - for example a fully-connected or a convolutional
layer. Given an input the router makes a routing decision, choosing a function
block to apply and passing the output back to the router recursively,
terminating when a fixed recursion depth is reached. In this way the routing
network dynamically composes different function blocks for each input. We
employ a collaborative multi-agent reinforcement learning (MARL) approach to
jointly train the router and function blocks. We evaluate our model against
cross-stitch networks and shared-layer baselines on multi-task settings of the
MNIST, mini-imagenet, and CIFAR-100 datasets. Our experiments demonstrate a
significant improvement in accuracy, with sharper convergence. In addition,
routing networks have nearly constant per-task training cost while cross-stitch
networks scale linearly with the number of tasks. On CIFAR-100 (20 tasks) we
obtain cross-stitch performance levels with an 85% reduction in training time.
Deep Stacked Networks with Residual Polishing for Image Inpainting
Authors: Ugur Demir, Gozde Unal
Date: 2017-12-31 00:00:00
Tasks: Denoising, Image Inpainting

Deep neural networks have shown promising results in image inpainting even if
the missing area is relatively large. However, most of the existing inpainting
networks introduce undesired artifacts and noise to the repaired regions. To
solve this problem, we present a novel framework which consists of two stacked
convolutional neural networks that inpaint the image and remove the artifacts,
respectively. The first network considers the global structure of the damaged
image and coarsely fills the blank area. Then the second network modifies the
repaired image to cancel the noise introduced by the first network. The
proposed framework splits the problem into two distinct partitions that can be
optimized separately, therefore it can be applied to any inpainting algorithm
by changing the first network. Second stage in our framework which aims at
polishing the inpainted images can be treated as a denoising problem where a
wide range of algorithms can be employed. Our results demonstrate that the
proposed framework achieves significant improvement on both visual and
quantitative evaluations.
Restricted Boltzmann Machines for Robust and Fast Latent Truth Discovery
Authors: Klaus Broelemann, Thomas Gottron, Gjergji Kasneci
Date: 2017-12-31 00:00:00

We address the problem of latent truth discovery, LTD for short, where the
goal is to discover the underlying true values of entity attributes in the
presence of noisy, conflicting or incomplete information. Despite a multitude
of algorithms to address the LTD problem that can be found in literature, only
little is known about their overall performance with respect to effectiveness
(in terms of truth discovery capabilities), efficiency and robustness. A
practical LTD approach should satisfy all these characteristics so that it can
be applied to heterogeneous datasets of varying quality and degrees of
cleanliness.
  We propose a novel algorithm for LTD that satisfies the above requirements.
The proposed model is based on Restricted Boltzmann Machines, thus coined
LTD-RBM. In extensive experiments on various heterogeneous and publicly
available datasets, LTD-RBM is superior to state-of-the-art LTD techniques in
terms of an overall consideration of effectiveness, efficiency and robustness.
Kronecker Recurrent Units
Authors: Cijo Jose, Moustpaha Cisse, Francois Fleuret
Date: 2017-05-29 00:00:00
Proceeding: ICML 2018 7

Our work addresses two important issues with recurrent neural networks: (1)
they are over-parameterized, and (2) the recurrence matrix is ill-conditioned.
The former increases the sample complexity of learning and the training time.
The latter causes the vanishing and exploding gradient problem. We present a
flexible recurrent neural network model called Kronecker Recurrent Units (KRU).
KRU achieves parameter efficiency in RNNs through a Kronecker factored
recurrent matrix. It overcomes the ill-conditioning of the recurrent matrix by
enforcing soft unitary constraints on the factors. Thanks to the small
dimensionality of the factors, maintaining these constraints is computationally
efficient. Our experimental results on seven standard data-sets reveal that KRU
can reduce the number of parameters by three orders of magnitude in the
recurrent weight matrix compared to the existing recurrent models, without
trading the statistical performance. These results in particular show that
while there are advantages in having a high dimensional recurrent space, the
capacity of the recurrent part of the model can be dramatically reduced.
Interactive Video Object Segmentation in the Wild
Authors: Arnaud Benard, Michael Gygli
Date: 2017-12-31 00:00:00
Tasks: Image Segmentation, Interactive Video Object Segmentation, Object, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Segmentation, Video Semantic Segmentation

In this paper we present our system for human-in-the-loop video object
segmentation. The backbone of our system is a method for one-shot video object
segmentation. While fast, this method requires an accurate pixel-level
segmentation of one (or several) frames as input. As manually annotating such a
segmentation is impractical, we propose a deep interactive image segmentation
method, that can accurately segment objects with only a handful of clicks. On
the GrabCut dataset, our method obtains 90% IOU with just 3.8 clicks on
average, setting the new state of the art. Furthermore, as our method
iteratively refines an initial segmentation, it can effectively correct frames
where the video object segmentation fails, thus allowing users to quickly
obtain high quality results even on challenging sequences. Finally, we
investigate usage patterns and give insights in how many steps users take to
annotate frames, what kind of corrections they provide, etc., thus giving
important insights for further improving interactive video segmentation.
A New Approach for Measuring Sentiment Orientation based on Multi-Dimensional Vector Space
Authors: Youngsam Kim, Hyopil Shin
Date: 2017-12-31 00:00:00

This study implements a vector space model approach to measure the sentiment
orientations of words. Two representative vectors for positive/negative
polarity are constructed using high-dimensional vec-tor space in both an
unsupervised and a semi-supervised manner. A sentiment ori-entation value per
word is determined by taking the difference between the cosine distances
against the two reference vec-tors. These two conditions (unsupervised and
semi-supervised) are compared against an existing unsupervised method (Turney,
2002). As a result of our experi-ment, we demonstrate that this novel ap-proach
significantly outperforms the pre-vious unsupervised approach and is more
practical and data efficient as well.