pwc-archive/papers-with-abstracts huggingface dataset:
Audiovisual Analytics Vocabulary and Ontology (AAVO): initial core and example expansion
Authors: Renato Fabbri, Maria Cristina Ferreira de Oliveira
Date: 2017-10-27 00:00:00

Visual Analytics might be defined as data mining assisted by interactive
visual interfaces. The field has been receiving prominent consideration by
researchers, developers and the industry. The literature, however, is complex
because it involves multiple fields of knowledge and is considerably recent. In
this article we describe an initial tentative organization of the knowledge in
the field as an OWL ontology and a SKOS vocabulary. This effort might be useful
in many ways that include conceptual considerations and software
implementations. Within the results and discussions, we expose a core and an
example expansion of the conceptualization, and incorporate design issues that
enhance the expressive power of the abstraction.
Enhancements of linked data expressiveness for ontologies
Authors: Renato Fabbri
Date: 2017-10-27 00:00:00

The semantic web has received many contributions of researchers as ontologies
which, in this context, i.e. within RDF linked data, are formalized
conceptualizations that might use different protocols, such as RDFS, OWL DL and
OWL FULL. In this article, we describe new expressive techniques which were
found necessary after elaborating dozens of OWL ontologies for the scientific
academy, the State and the civil society. They consist in: 1) stating possible
uses a property might have without incurring into axioms or restrictions; 2)
assigning a level of priority for an element (class, property, triple); 3)
correct depiction in diagrams of relations between classes, between individuals
which are imperative, and between individuals which are optional; 4) a
convenient association between OWL classes and SKOS concepts. We propose
specific rules to accomplish these enhancements and exemplify both its use and
the difficulties that arise because these techniques are currently not
established as standards to the ontology designer.
CANDiS: Coupled & Attention-Driven Neural Distant Supervision
Authors: Tushar Nagarajan, Sharmistha, Partha Talukdar
Date: 2017-10-26 00:00:00
Tasks: Relation, Relation Extraction

Distant Supervision for Relation Extraction uses heuristically aligned text
data with an existing knowledge base as training data. The unsupervised nature
of this technique allows it to scale to web-scale relation extraction tasks, at
the expense of noise in the training data. Previous work has explored
relationships among instances of the same entity-pair to reduce this noise, but
relationships among instances across entity-pairs have not been fully
exploited. We explore the use of inter-instance couplings based on verb-phrase
and entity type similarities. We propose a novel technique, CANDiS, which casts
distant supervision using inter-instance coupling into an end-to-end neural
network model. CANDiS incorporates an attention module at the instance-level to
model the multi-instance nature of this problem. CANDiS outperforms existing
state-of-the-art techniques on a standard benchmark dataset.
A Unified approach for Conventional Zero-shot, Generalized Zero-shot and Few-shot Learning
Authors: Shafin Rahman, Salman H. Khan, Fatih Porikli
Date: 2017-06-27 00:00:00
Tasks: Few-Shot Learning, Generalized Zero-Shot Learning, One-Shot Learning, Zero-Shot Learning

Prevalent techniques in zero-shot learning do not generalize well to other
related problem scenarios. Here, we present a unified approach for conventional
zero-shot, generalized zero-shot and few-shot learning problems. Our approach
is based on a novel Class Adapting Principal Directions (CAPD) concept that
allows multiple embeddings of image features into a semantic space. Given an
image, our method produces one principal direction for each seen class. Then,
it learns how to combine these directions to obtain the principal direction for
each unseen class such that the CAPD of the test image is aligned with the
semantic embedding of the true class, and opposite to the other classes. This
allows efficient and class-adaptive information transfer from seen to unseen
classes. In addition, we propose an automatic process for selection of the most
useful seen classes for each unseen class to achieve robustness in zero-shot
learning. Our method can update the unseen CAPD taking the advantages of few
unseen images to work in a few-shot learning scenario. Furthermore, our method
can generalize the seen CAPDs by estimating seen-unseen diversity that
significantly improves the performance of generalized zero-shot learning. Our
extensive evaluations demonstrate that the proposed approach consistently
achieves superior performance in zero-shot, generalized zero-shot and
few/one-shot learning problems.
Data-driven Feature Sampling for Deep Hyperspectral Classification and Segmentation
Authors: William M. Severa, Jerilyn A. Timlin, Suraj Kholwadwala, Conrad D. James, James B. Aimone
Date: 2017-10-26 00:00:00
Proceeding: ICLR 2018 1
Tasks: Classification, feature selection, General Classification

The high dimensionality of hyperspectral imaging forces unique challenges in
scope, size and processing requirements. Motivated by the potential for an
in-the-field cell sorting detector, we examine a $\textit{Synechocystis sp.}$
PCC 6803 dataset wherein cells are grown alternatively in nitrogen rich or
deplete cultures. We use deep learning techniques to both successfully classify
cells and generate a mask segmenting the cells/condition from the background.
Further, we use the classification accuracy to guide a data-driven, iterative
feature selection method, allowing the design neural networks requiring 90%
fewer input features with little accuracy degradation.
SEGMENT3D: A Web-based Application for Collaborative Segmentation of 3D images used in the Shoot Apical Meristem
Authors: Thiago V. Spina, Johannes Stegmaier, Alexandre X. Falcão, Elliot Meyerowitz, Alexandre Cunha
Date: 2017-10-26 00:00:00
Tasks: Cell Segmentation, Image Segmentation, Segmentation, Semantic Segmentation

The quantitative analysis of 3D confocal microscopy images of the shoot
apical meristem helps understanding the growth process of some plants. Cell
segmentation in these images is crucial for computational plant analysis and
many automated methods have been proposed. However, variations in signal
intensity across the image mitigate the effectiveness of those approaches with
no easy way for user correction. We propose a web-based collaborative 3D image
segmentation application, SEGMENT3D, to leverage automatic segmentation
results. The image is divided into 3D tiles that can be either segmented
interactively from scratch or corrected from a pre-existing segmentation.
Individual segmentation results per tile are then automatically merged via
consensus analysis and then stitched to complete the segmentation for the
entire image stack. SEGMENT3D is a comprehensive application that can be
applied to other 3D imaging modalities and general objects. It also provides an
easy way to create supervised data to advance segmentation using machine
learning models.
Standard detectors aren't (currently) fooled by physical adversarial stop signs
Authors: Jiajun Lu, Hussein Sibai, Evan Fabry, David Forsyth
Date: 2017-10-09 00:00:00
Tasks: Adversarial Attack

An adversarial example is an example that has been adjusted to produce the
wrong label when presented to a system at test time. If adversarial examples
existed that could fool a detector, they could be used to (for example) wreak
havoc on roads populated with smart vehicles. Recently, we described our
difficulties creating physical adversarial stop signs that fool a detector.
More recently, Evtimov et al. produced a physical adversarial stop sign that
fools a proxy model of a detector. In this paper, we show that these physical
adversarial stop signs do not fool two standard detectors (YOLO and Faster
RCNN) in standard configuration. Evtimov et al.'s construction relies on a crop
of the image to the stop sign; this crop is then resized and presented to a
classifier. We argue that the cropping and resizing procedure largely
eliminates the effects of rescaling and of view angle. Whether an adversarial
attack is robust under rescaling and change of view direction remains moot. We
argue that attacking a classifier is very different from attacking a detector,
and that the structure of detectors - which must search for their own bounding
box, and which cannot estimate that box very accurately - likely makes it
difficult to make adversarial patterns. Finally, an adversarial pattern on a
physical object that could fool a detector would have to be adversarial in the
face of a wide family of parametric distortions (scale; view angle; box shift
inside the detector; illumination; and so on). Such a pattern would be of great
theoretical and practical interest. There is currently no evidence that such
patterns exist.
Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks
Authors: Zackory Erickson, Sonia Chernova, Charles C. Kemp
Date: 2017-07-10 00:00:00
Tasks: Material Recognition, Time Series, Time Series Analysis

Material recognition enables robots to incorporate knowledge of material
properties into their interactions with everyday objects. For example, material
recognition opens up opportunities for clearer communication with a robot, such
as "bring me the metal coffee mug", and recognizing plastic versus metal is
crucial when using a microwave or oven. However, collecting labeled training
data with a robot is often more difficult than unlabeled data. We present a
semi-supervised learning approach for material recognition that uses generative
adversarial networks (GANs) with haptic features such as force, temperature,
and vibration. Our approach achieves state-of-the-art results and enables a
robot to estimate the material class of household objects with ~90% accuracy
when 92% of the training data are unlabeled. We explore how well this approach
can recognize the material of new objects and we discuss challenges facing
generalization. To motivate learning from unlabeled training data, we also
compare results against several common supervised learning classifiers. In
addition, we have released the dataset used for this work which consists of
time-series haptic measurements from a robot that conducted thousands of
interactions with 72 household objects.
Modeling Relational Data with Graph Convolutional Networks
Authors: Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, Max Welling
Date: 2017-03-17 00:00:00
Tasks: Decoder, General Classification, Graph Classification, Heterogeneous Node Classification, Information Retrieval, Knowledge Base Completion, Knowledge Graphs, Link Prediction, Node Classification, Node Property Prediction, Retrieval

Knowledge graphs enable a wide variety of applications, including question
answering and information retrieval. Despite the great effort invested in their
creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata)
remain incomplete. We introduce Relational Graph Convolutional Networks
(R-GCNs) and apply them to two standard knowledge base completion tasks: Link
prediction (recovery of missing facts, i.e. subject-predicate-object triples)
and entity classification (recovery of missing entity attributes). R-GCNs are
related to a recent class of neural networks operating on graphs, and are
developed specifically to deal with the highly multi-relational data
characteristic of realistic knowledge bases. We demonstrate the effectiveness
of R-GCNs as a stand-alone model for entity classification. We further show
that factorization models for link prediction such as DistMult can be
significantly improved by enriching them with an encoder model to accumulate
evidence over multiple inference steps in the relational graph, demonstrating a
large improvement of 29.8% on FB15k-237 over a decoder-only baseline.
Phase Transitions in Image Denoising via Sparsely Coding Convolutional Neural Networks
Authors: Jacob Carroll, Nils Carlson, Garrett T. Kenyon
Date: 2017-10-26 00:00:00
Tasks: Denoising, Image Denoising

Neural networks are analogous in many ways to spin glasses, systems which are
known for their rich set of dynamics and equally complex phase diagrams. We
apply well-known techniques in the study of spin glasses to a convolutional
sparsely encoding neural network and observe power law finite-size scaling
behavior in the sparsity and reconstruction error as the network denoises
32$\times$32 RGB CIFAR-10 images. This finite-size scaling indicates the
presence of a continuous phase transition at a critical value of this sparsity.
By using the power law scaling relations inherent to finite-size scaling, we
can determine the optimal value of sparsity for any network size by tuning the
system to the critical point and operate the system at the minimum denoising
error.
How far did we get in face spoofing detection?
Authors: Luiz Souza, Mauricio Pamplona, Luciano Oliveira, João Papa
Date: 2017-10-26 00:00:00
Tasks: Face Recognition

The growing use of control access systems based on face recognition shed
light over the need for even more accurate systems to detect face spoofing
attacks. In this paper, an extensive analysis on face spoofing detection works
published in the last decade is presented. The analyzed works are categorized
by their fundamental parts, i.e., descriptors and classifiers. This structured
survey also brings the temporal evolution of the face spoofing detection field,
as well as a comparative analysis of the works considering the most important
public data sets in the field. The methodology followed in this work is
particularly relevant to observe trends in the existing approaches, to discuss
still opened issues, and to propose new perspectives for the future of face
spoofing detection.
Understanding Early Word Learning in Situated Artificial Agents
Authors: Felix Hill, Stephen Clark, Karl Moritz Hermann, Phil Blunsom
Date: 2017-10-26 00:00:00
Proceeding: ICLR 2018 1
Tasks: Grounded language learning, Policy Gradient Methods

Neural network-based systems can now learn to locate the referents of words and phrases in images, answer questions about visual scenes, and execute symbolic instructions as first-person actors in partially-observable worlds. To achieve this so-called grounded language learning, models must overcome challenges that infants face when learning their first words. While it is notable that models with no meaningful prior knowledge overcome these obstacles, researchers currently lack a clear understanding of how they do so, a problem that we attempt to address in this paper. For maximum control and generality, we focus on a simple neural network-based language learning agent, trained via policy-gradient methods, which can interpret single-word instructions in a simulated 3D world. Whilst the goal is not to explicitly model infant word learning, we take inspiration from experimental paradigms in developmental psychology and apply some of these to the artificial agent, exploring the conditions under which established human biases and learning effects emerge. We further propose a novel method for visualising semantic representations in the agent.
Adversarial Examples Detection in Deep Networks with Convolutional Filter Statistics
Authors: Xin Li, Fuxin Li
Date: 2016-12-22 00:00:00
Proceeding: ICCV 2017 10

Deep learning has greatly improved visual recognition in recent years.
However, recent research has shown that there exist many adversarial examples
that can negatively impact the performance of such an architecture. This paper
focuses on detecting those adversarial examples by analyzing whether they come
from the same distribution as the normal examples. Instead of directly training
a deep neural network to detect adversarials, a much simpler approach was
proposed based on statistics on outputs from convolutional layers. A cascade
classifier was designed to efficiently detect adversarials. Furthermore,
trained from one particular adversarial generating mechanism, the resulting
classifier can successfully detect adversarials from a completely different
mechanism as well. The resulting classifier is non-subdifferentiable, hence
creates a difficulty for adversaries to attack by using the gradient of the
classifier. After detecting adversarial examples, we show that many of them can
be recovered by simply performing a small average filter on the image. Those
findings should lead to more insights about the classification mechanisms in
deep convolutional neural networks.
Kernel k-Groups via Hartigan's Method
Authors: Guilherme França, Maria L. Rizzo, Joshua T. Vogelstein
Date: 2017-10-26 00:00:00
Tasks: Clustering, Community Detection, graph partitioning

Energy statistics was proposed by Sz\' ekely in the 80's inspired by Newton's gravitational potential in classical mechanics and it provides a model-free hypothesis test for equality of distributions. In its original form, energy statistics was formulated in Euclidean spaces. More recently, it was generalized to metric spaces of negative type. In this paper, we consider a formulation for the clustering problem using a weighted version of energy statistics in spaces of negative type. We show that this approach leads to a quadratically constrained quadratic program in the associated kernel space, establishing connections with graph partitioning problems and kernel methods in machine learning. To find local solutions of such an optimization problem, we propose kernel k-groups, which is an extension of Hartigan's method to kernel spaces. Kernel k-groups is cheaper than spectral clustering and has the same computational cost as kernel k-means (which is based on Lloyd's heuristic) but our numerical results show an improved performance, especially in higher dimensions. Moreover, we verify the efficiency of kernel k-groups in community detection in sparse stochastic block models which has fascinating applications in several areas of science.
Gradient Sparsification for Communication-Efficient Distributed Optimization
Authors: Jianqiao Wangni, Jialei Wang, Ji Liu, Tong Zhang
Date: 2017-10-26 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: BIG-bench Machine Learning, Distributed Optimization, regression, Stochastic Optimization

Modern large scale machine learning applications require stochastic
optimization algorithms to be implemented on distributed computational
architectures. A key bottleneck is the communication overhead for exchanging
information such as stochastic gradients among different workers. In this
paper, to reduce the communication cost we propose a convex optimization
formulation to minimize the coding length of stochastic gradients. To solve the
optimal sparsification efficiently, several simple and fast algorithms are
proposed for approximate solution, with theoretical guaranteed for sparseness.
Experiments on $\ell_2$ regularized logistic regression, support vector
machines, and convolutional neural networks validate our sparsification
approaches.
Klout Topics for Modeling Interests and Expertise of Users Across Social Networks
Authors: Sarah Ellinger, Prantik Bhattacharyya, Preeti Bhargava, Nemanja Spasojevic
Date: 2017-10-26 00:00:00

This paper presents Klout Topics, a lightweight ontology to describe social
media users' topics of interest and expertise. Klout Topics is designed to: be
human-readable and consumer-friendly; cover multiple domains of knowledge in
depth; and promote data extensibility via knowledge base entities. We discuss
why this ontology is well-suited for text labeling and interest modeling
applications, and how it compares to available alternatives. We show its
coverage against common social media interest sets, and examples of how it is
used to model the interests of over 780M social media users on Klout.com.
Finally, we open the ontology for external use.
Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System
Authors: Germain Haessig, Andrew Cassidy, Rodrigo Alvarez, Ryad Benosman, Garrick Orchard
Date: 2017-10-26 00:00:00
Tasks: Optical Flow Estimation

This paper describes a fully spike-based neural network for optical flow
estimation from Dynamic Vision Sensor data. A low power embedded implementation
of the method which combines the Asynchronous Time-based Image Sensor with
IBM's TrueNorth Neurosynaptic System is presented. The sensor generates spikes
with sub-millisecond resolution in response to scene illumination changes.
These spike are processed by a spiking neural network running on TrueNorth with
a 1 millisecond resolution to accurately determine the order and time
difference of spikes from neighboring pixels, and therefore infer the velocity.
The spiking neural network is a variant of the Barlow Levick method for optical
flow estimation. The system is evaluated on two recordings for which ground
truth motion is available, and achieves an Average Endpoint Error of 11% at an
estimated power budget of under 80mW for the sensor and computation.
Comparing Apples and Oranges: Off-Road Pedestrian Detection on the NREC Agricultural Person-Detection Dataset
Authors: Zachary Pezzementi, Trenton Tabor, Peiyun Hu, Jonathan K. Chang, Deva Ramanan, Carl Wellington, Benzun P. Wisely Babu, Herman Herman
Date: 2017-07-22 00:00:00
Tasks: Human Detection, Pedestrian Detection

Person detection from vehicles has made rapid progress recently with the
advent of multiple highquality datasets of urban and highway driving, yet no
large-scale benchmark is available for the same problem in off-road or
agricultural environments. Here we present the NREC Agricultural
Person-Detection Dataset to spur research in these environments. It consists of
labeled stereo video of people in orange and apple orchards taken from two
perception platforms (a tractor and a pickup truck), along with vehicle
position data from RTK GPS. We define a benchmark on part of the dataset that
combines a total of 76k labeled person images and 19k sampled person-free
images. The dataset highlights several key challenges of the domain, including
varying environment, substantial occlusion by vegetation, people in motion and
in non-standard poses, and people seen from a variety of distances; meta-data
are included to allow targeted evaluation of each of these effects. Finally, we
present baseline detection performance results for three leading approaches
from urban pedestrian detection and our own convolutional neural network
approach that benefits from the incorporation of additional image context. We
show that the success of existing approaches on urban data does not transfer
directly to this domain.
Sparse Diffusion-Convolutional Neural Networks
Authors: James Atwood, Siddharth Pal, Don Towsley, Ananthram Swami
Date: 2017-10-26 00:00:00
Tasks: Computational Efficiency, General Classification, Node Classification

The predictive power and overall computational efficiency of
Diffusion-convolutional neural networks make them an attractive choice for node
classification tasks. However, a naive dense-tensor-based implementation of
DCNNs leads to $\mathcal{O}(N^2)$ memory complexity which is prohibitive for
large graphs. In this paper, we introduce a simple method for thresholding
input graphs that provably reduces memory requirements of DCNNs to O(N) (i.e.
linear in the number of nodes in the input) without significantly affecting
predictive performance.