pwc-archive/papers-with-abstracts huggingface dataset:
Hallucinating Dense Optical Flow from Sparse Lidar for Autonomous Vehicles
Authors: Victor Vaquero, Alberto Sanfeliu, Francesc Moreno-Noguer
Date: 2018-08-30 00:00:00
Tasks: Autonomous Vehicles, Optical Flow Estimation

In this paper we propose a novel approach to estimate dense optical flow from
sparse lidar data acquired on an autonomous vehicle. This is intended to be
used as a drop-in replacement of any image-based optical flow system when
images are not reliable due to e.g. adverse weather conditions or at night. In
order to infer high resolution 2D flows from discrete range data we devise a
three-block architecture of multiscale filters that combines multiple
intermediate objectives, both in the lidar and image domain. To train this
network we introduce a dataset with approximately 20K lidar samples of the
Kitti dataset which we have augmented with a pseudo ground-truth image-based
optical flow computed using FlowNet2. We demonstrate the effectiveness of our
approach on Kitti, and show that despite using the low-resolution and sparse
measurements of the lidar, we can regress dense optical flow maps which are at
par with those estimated with image-based methods.
Uniform Inference in High-Dimensional Gaussian Graphical Models
Authors: Sven Klaassen, Jannis Kück, Martin Spindler, Victor Chernozhukov
Date: 2018-08-30 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Graphical models have become a very popular tool for representing
dependencies within a large set of variables and are key for representing
causal structures. We provide results for uniform inference on high-dimensional
graphical models with the number of target parameters $d$ being possible much
larger than sample size. This is in particular important when certain features
or structures of a causal model should be recovered. Our results highlight how
in high-dimensional settings graphical models can be estimated and recovered
with modern machine learning methods in complex data sets. To construct
simultaneous confidence regions on many target parameters, sufficiently fast
estimation rates of the nuisance functions are crucial. In this context, we
establish uniform estimation rates and sparsity guarantees of the square-root
estimator in a random design under approximate sparsity conditions that might
be of independent interest for related problems in high-dimensions. We also
demonstrate in a comprehensive simulation study that our procedure has good
small sample properties.
Total Recall: Understanding Traffic Signs using Deep Hierarchical Convolutional Neural Networks
Authors: Sourajit Saha, Sharif Amit Kamran, Ali Shihab Sabbir
Date: 2018-08-30 00:00:00
Tasks: Self-Driving Cars, Traffic Sign Recognition

Recognizing Traffic Signs using intelligent systems can drastically reduce
the number of accidents happening world-wide. With the arrival of Self-driving
cars it has become a staple challenge to solve the automatic recognition of
Traffic and Hand-held signs in the major streets. Various machine learning
techniques like Random Forest, SVM as well as deep learning models has been
proposed for classifying traffic signs. Though they reach state-of-the-art
performance on a particular data-set, but fall short of tackling multiple
Traffic Sign Recognition benchmarks. In this paper, we propose a novel and
one-for-all architecture that aces multiple benchmarks with better overall
score than the state-of-the-art architectures. Our model is made of residual
convolutional blocks with hierarchical dilated skip connections joined in
steps. With this we score 99.33% Accuracy in German sign recognition benchmark
and 99.17% Accuracy in Belgian traffic sign classification benchmark. Moreover,
we propose a newly devised dilated residual learning representation technique
which is very low in both memory and computational complexity.
Iterative Recursive Attention Model for Interpretable Sequence Classification
Authors: Martin Tutek, Jan Šnajder
Date: 2018-08-30 00:00:00
Proceeding: WS 2018 11
Tasks: Classification, General Classification, model, Sentiment Analysis, Sentiment Classification

Natural language processing has greatly benefited from the introduction of
the attention mechanism. However, standard attention models are of limited
interpretability for tasks that involve a series of inference steps. We
describe an iterative recursive attention model, which constructs incremental
representations of input data through reusing results of previously computed
queries. We train our model on sentiment classification datasets and
demonstrate its capacity to identify and combine different aspects of the input
in an easily interpretable manner, while obtaining performance close to the
state of the art.
Data-Driven Debugging for Functional Side Channels
Authors: Saeid Tizpaz-Niari, Pavol Cerny, Ashutosh Trivedi
Date: 2018-08-30 00:00:00
Tasks: Clustering

Information leaks through side channels are a pervasive problem, even in security-critical applications. Functional side channels arise when an attacker knows that a secret value of a server stays fixed for a certain time. Then, the attacker can observe the server executions on a sequence of different public inputs, each paired with the same secret input. Thus for each secret, the attacker observes a function from public inputs to execution time, for instance, and she can compare these functions for different secrets. First, we introduce a notion of noninterference for functional side channels. We focus on the case of noisy observations, where we demonstrate with examples that there is a practical functional side channel in programs that would be deemed information-leak-free or be underestimated using the standard definition. Second, we develop a framework and techniques for debugging programs for functional side channels. We extend evolutionary fuzzing techniques to generate inputs that exploit functional dependencies of response times on public inputs. We adapt existing results and algorithms in functional data analysis to model the functions and discover the existence of side channels. We use a functional extension of standard decision tree learning to pinpoint the code fragments causing a side channel if there is one. We empirically evaluate the performance of our tool FUCHSIA on a series of micro-benchmarks and realistic Java programs. On the set of benchmarks, we show that FUCHSIA outperforms the state-of-the-art techniques in detecting side channel classes. On the realistic programs, we show the scalability of FUCHSIA in analyzing functional side channels in Java programs with thousands of methods. Also, we show the usefulness of FUCHSIA in finding side channels including a zero-day vulnerability in OpenJDK and another vulnerability in Jetty that was since fixed by the developers.
Syntactic Scaffolds for Semantic Structures
Authors: Swabha Swayamdipta, Sam Thomson, Kenton Lee, Luke Zettlemoyer, Chris Dyer, Noah A. Smith
Date: 2018-08-30 00:00:00
Proceeding: EMNLP 2018 10
Tasks: coreference-resolution, Coreference Resolution

We introduce the syntactic scaffold, an approach to incorporating syntactic
information into semantic tasks. Syntactic scaffolds avoid expensive syntactic
processing at runtime, only making use of a treebank during training, through a
multitask objective. We improve over strong baselines on PropBank semantics,
frame semantics, and coreference resolution, achieving competitive performance
on all three tasks.
Application of Self-Play Reinforcement Learning to a Four-Player Game of Imperfect Information
Authors: Henry Charlesworth
Date: 2018-08-30 00:00:00
Tasks: Card Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce a new virtual environment for simulating a card game known as
"Big 2". This is a four-player game of imperfect information with a relatively
complicated action space (being allowed to play 1,2,3,4 or 5 card combinations
from an initial starting hand of 13 cards). As such it poses a challenge for
many current reinforcement learning methods. We then use the recently proposed
"Proximal Policy Optimization" algorithm to train a deep neural network to play
the game, purely learning via self-play, and find that it is able to reach a
level which outperforms amateur human players after only a relatively short
amount of training time and without needing to search a tree of future game
states.
Victory Probability in the Fire Emblem Arena
Authors: Andrew Brockmann
Date: 2018-08-29 00:00:00
Tasks: Relation

We demonstrate how to efficiently compute the probability of victory in Fire
Emblem arena battles. The probability can be expressed in terms of a
multivariate recurrence relation which lends itself to a straightforward
dynamic programming solution. Some implementation issues are addressed, and a
full implementation is provided in code.
Subgraph Matching Kernels for Attributed Graphs
Authors: Nils Kriege, Petra Mutzel
Date: 2012-06-27 00:00:00
Tasks: General Classification

We propose graph kernels based on subgraph matchings, i.e.
structure-preserving bijections between subgraphs. While recently proposed
kernels based on common subgraphs (Wale et al., 2008; Shervashidze et al.,
2009) in general can not be applied to attributed graphs, our approach allows
to rate mappings of subgraphs by a flexible scoring scheme comparing vertex and
edge attributes by kernels. We show that subgraph matching kernels generalize
several known kernels. To compute the kernel we propose a graph-theoretical
algorithm inspired by a classical relation between common subgraphs of two
graphs and cliques in their product graph observed by Levi (1973). Encouraging
experimental results on a classification task of real-world graphs are
presented.
Gesture Recognition with a Focus on Important Actions by Using a Path Searching Method in Weighted Graph
Authors: Kazumoto Tanaka
Date: 2009-12-09 00:00:00
Tasks: Gesture Recognition, Optical Flow Estimation

This paper proposes a method of gesture recognition with a focus on important
actions for distinguishing similar gestures. The method generates a partial
action sequence by using optical flow images, expresses the sequence in the
eigenspace, and checks the feature vector sequence by applying an optimum
path-searching method of weighted graph to focus the important actions. Also
presented are the results of an experiment on the recognition of similar sign
language words.
Facial Gesture Recognition Using Correlation And Mahalanobis Distance
Authors: Supriya Kapoor, Shruti Khanna, Rahul Bhatia
Date: 2010-03-09 00:00:00
Tasks: Gesture Recognition

Augmenting human computer interaction with automated analysis and synthesis
of facial expressions is a goal towards which much research effort has been
devoted recently. Facial gesture recognition is one of the important component
of natural human-machine interfaces; it may also be used in behavioural
science, security systems and in clinical practice. Although humans recognise
facial expressions virtually without effort or delay, reliable expression
recognition by machine is still a challenge. The face expression recognition
problem is challenging because different individuals display the same
expression differently. This paper presents an overview of gesture recognition
in real time using the concepts of correlation and Mahalanobis distance.We
consider the six universal emotional categories namely joy, anger, fear,
disgust, sadness and surprise.
Survey on Various Gesture Recognition Techniques for Interfacing Machines Based on Ambient Intelligence
Authors: Harshith C, Karthik R. Shastry, Manoj Ravindran, M. V. V. N. S. Srikanth, Naveen Lakshmikhanth
Date: 2010-12-01 00:00:00
Tasks: Contour Detection, Gesture Recognition

Gesture recognition is mainly apprehensive on analyzing the functionality of
human wits. The main goal of gesture recognition is to create a system which
can recognize specific human gestures and use them to convey information or for
device control. Hand gestures provide a separate complementary modality to
speech for expressing ones ideas. Information associated with hand gestures in
a conversation is degree,discourse structure, spatial and temporal structure.
The approaches present can be mainly divided into Data-Glove Based and Vision
Based approaches. An important face feature point is the nose tip. Since nose
is the highest protruding point from the face. Besides that, it is not affected
by facial expressions.Another important function of the nose is that it is able
to indicate the head pose. Knowledge of the nose location will enable us to
align an unknown 3D face with those in a face database. Eye detection is
divided into eye position detection and eye contour detection. Existing works
in eye detection can be classified into two major categories: traditional
image-based passive approaches and the active IR based approaches. The former
uses intensity and shape of eyes for detection and the latter works on the
assumption that eyes have a reflection under near IR illumination and produce
bright/dark pupil effect. The traditional methods can be broadly classified
into three categories: template based methods,appearance based methods and
feature based methods. The purpose of this paper is to compare various human
Gesture recognition systems for interfacing machines directly to human wits
without any corporeal media in an ambient environment.
Deciding of HMM parameters based on number of critical points for gesture recognition from motion capture data
Authors: Michał Cholewa, Przemysław Głomb
Date: 2011-10-28 00:00:00
Tasks: Gesture Recognition

This paper presents a method of choosing number of states of a HMM based on
number of critical points of the motion capture data. The choice of Hidden
Markov Models(HMM) parameters is crucial for recognizer's performance as it is
the first step of the training and cannot be corrected automatically within
HMM. In this article we define predictor of number of states based on number of
critical points of the sequence and test its effectiveness against sample data.
Hand Gesture Recognition Using Ultrasonic Waves
Authors: Mohammed H. AlSharif, Mohamed Saad, Tareq Y. Al-Naffouri
Date: 2017-10-24 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

This paper presents a new method for detecting and classifying a predefined
set of hand gestures using a single transmitter and a single receiver utilizing
a linearly frequency modulated ultrasonic signal. Gestures are identified based
on estimated range and received signal strength (RSS) of reflected signal from
the hand. Support Vector Machine (SVM) was used for gesture detection and
classification. The system was tested using experimental setup and achieved an
average accuracy of 88%.
Micro Hand Gesture Recognition System Using Ultrasonic Active Sensing
Authors: Yu Sang, Laixi Shi, Yimin Liu
Date: 2017-12-01 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

In this paper, we propose a micro hand gesture recognition system and methods using ultrasonic active sensing. This system uses micro dynamic hand gestures for recognition to achieve human-computer interaction (HCI). The implemented system, called hand-ultrasonic gesture (HUG), consists of ultrasonic active sensing, pulsed radar signal processing, and time-sequence pattern recognition by machine learning. We adopt lower frequency (300 kHz) ultrasonic active sensing to obtain high resolution range-Doppler image features. Using high quality sequential range-Doppler features, we propose a state-transition-based hidden Markov model for gesture recognition. This method achieves a recognition accuracy of nearly 90\% by using symbolized range-Doppler features and significantly reduces the computational complexity and power consumption. Furthermore, to achieve higher classification accuracy, we utilize an end-to-end neural network model and obtain a recognition accuracy of 96.32\%. In addition to offline analysis, a real-time prototype is released to verify our method's potential for application in the real world.
DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency
Authors: Yuliang Zou, Zelun Luo, Jia-Bin Huang
Date: 2018-09-05 00:00:00
Proceeding: ECCV 2018 9
Tasks: Depth And Camera Motion, Depth Estimation, Depth Prediction, Optical Flow Estimation

We present an unsupervised learning framework for simultaneously training
single-view depth prediction and optical flow estimation models using unlabeled
video sequences. Existing unsupervised methods often exploit brightness
constancy and spatial smoothness priors to train depth or flow models. In this
paper, we propose to leverage geometric consistency as additional supervisory
signals. Our core idea is that for rigid regions we can use the predicted scene
depth and camera motion to synthesize 2D optical flow by backprojecting the
induced 3D scene flow. The discrepancy between the rigid flow (from depth
prediction and camera motion) and the estimated flow (from optical flow model)
allows us to impose a cross-task consistency loss. While all the networks are
jointly optimized during training, they can be applied independently at test
time. Extensive experiments demonstrate that our depth and flow models compare
favorably with state-of-the-art unsupervised methods.
Efficient Egocentric Visual Perception Combining Eye-tracking, a Software Retina and Deep Learning
Authors: Nina Hristozova, Piotr Ozimek, Jan Paul Siebert
Date: 2018-09-05 00:00:00
Tasks: General Classification

We present ongoing work to harness biological approaches to achieving highly
efficient egocentric perception by combining the space-variant imaging
architecture of the mammalian retina with Deep Learning methods. By
pre-processing images collected by means of eye-tracking glasses to control the
fixation locations of a software retina model, we demonstrate that we can
reduce the input to a DCNN by a factor of 3, reduce the required number of
training epochs and obtain over 98% classification rates when training and
validating the system on a database of over 26,000 images of 9 object classes.
Online local pool generation for dynamic classifier selection: an extended version
Authors: Mariana A. Souza, George D. C. Cavalcanti, Rafael M. O. Cruz, Robert Sabourin
Date: 2018-09-05 00:00:00
Tasks: General Classification

Dynamic Classifier Selection (DCS) techniques have difficulty in selecting
the most competent classifier in a pool, even when its presence is assured.
Since the DCS techniques rely only on local data to estimate a classifier's
competence, the manner in which the pool is generated could affect the choice
of the best classifier for a given sample. That is, the global perspective in
which pools are generated may not help the DCS techniques in selecting a
competent classifier for samples that are likely to be mislabelled. Thus, we
propose in this work an online pool generation method that produces a locally
accurate pool for test samples in difficult regions of the feature space. The
difficulty of a given area is determined by the classification difficulty of
the samples in it. That way, by using classifiers that were generated in a
local scope, it could be easier for the DCS techniques to select the best one
for the difficult samples. For the query samples in easy regions, a simple
nearest neighbors rule is used. In the extended version of this work, a deep
analysis on the correlation between instance hardness and the performance of
DCS techniques is presented. An instance hardness measure that conveys the
degree of local class overlap is then used to decide when the local pool is
used in the proposed scheme. The proposed method yielded significantly greater
recognition rates in comparison to a Bagging-generated pool and two other
global pool generation schemes for all DCS techniques evaluated. The proposed
scheme's performance was also significantly superior to three state-of-the-art
classification models and statistically equivalent to five of them. Moreover,
an extended analysis on the computational complexity of the proposed method and
of several DS techniques is presented in this version. We also provide the
implementation of the proposed technique using the DESLib library on GitHub.
Gene Shaving using influence function of a kernel method
Authors: Md. Ashad Alam, Mohammad Shahjama, Md. Ferdush Rahman
Date: 2018-09-05 00:00:00

Identifying significant subsets of the genes, gene shaving is an essential
and challenging issue for biomedical research for a huge number of genes and
the complex nature of biological networks,. Since positive definite kernel
based methods on genomic information can improve the prediction of diseases, in
this paper we proposed a new method, "kernel gene shaving (kernel canonical
correlation analysis (kernel CCA) based gene shaving). This problem is
addressed using the influence function of the kernel CCA. To investigate the
performance of the proposed method in a comparison of three popular gene
selection methods (T-test, SAM and LIMMA), we were used extensive simulated and
real microarray gene expression datasets. The performance measures AUC was
computed for each of the methods. The achievement of the proposed method has
improved than the three well-known gene selection methods. In real data
analysis, the proposed method identified a subsets of $210$ genes out of $2000$
genes. The network of these genes has significantly more interactions than
expected, which indicates that they may function in a concerted effort on colon
cancer.
Utilizing Character and Word Embeddings for Text Normalization with Sequence-to-Sequence Models
Authors: Daniel Watson, Nasser Zalmout, Nizar Habash
Date: 2018-09-05 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Text Normalization, Word Embeddings

Text normalization is an important enabling technology for several NLP tasks.
Recently, neural-network-based approaches have outperformed well-established
models in this task. However, in languages other than English, there has been
little exploration in this direction. Both the scarcity of annotated data and
the complexity of the language increase the difficulty of the problem. To
address these challenges, we use a sequence-to-sequence model with
character-based attention, which in addition to its self-learned character
embeddings, uses word embeddings pre-trained with an approach that also models
subword information. This provides the neural model with access to more
linguistic information especially suitable for text normalization, without
large parallel corpora. We show that providing the model with word-level
features bridges the gap for the neural network approach to achieve a
state-of-the-art F1 score on a standard Arabic language correction shared task
dataset.