pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Graph-based Rank Aggregation for Improved Retrieval
Authors: Icaro Cavalcante Dourado, Daniel Carlos Guimarães Pedronette, Ricardo da Silva Torres
Date: 2019-01-17 00:00:00
Tasks: Retrieval

This paper presents a robust and comprehensive graph-based rank aggregation
approach, used to combine results of isolated ranker models in retrieval tasks.
The method follows an unsupervised scheme, which is independent of how the
isolated ranks are formulated. Our approach is able to combine arbitrary
models, defined in terms of different ranking criteria, such as those based on
textual, image or hybrid content representations.
  We reformulate the ad-hoc retrieval problem as a document retrieval based on
fusion graphs, which we propose as a new unified representation model capable
of merging multiple ranks and expressing inter-relationships of retrieval
results automatically. By doing so, we claim that the retrieval system can
benefit from learning the manifold structure of datasets, thus leading to more
effective results. Another contribution is that our graph-based aggregation
formulation, unlike existing approaches, allows for encapsulating contextual
information encoded from multiple ranks, which can be directly used for
ranking, without further computations and post-processing steps over the
graphs. Based on the graphs, a novel similarity retrieval score is formulated
using an efficient computation of minimum common subgraphs. Finally, another
benefit over existing approaches is the absence of hyperparameters.
  A comprehensive experimental evaluation was conducted considering diverse
well-known public datasets, composed of textual, image, and multimodal
documents. Performed experiments demonstrate that our method reaches top
performance, yielding better effectiveness scores than state-of-the-art
baseline methods and promoting large gains over the rankers being fused, thus
demonstrating the successful capability of the proposal in representing queries
based on a unified graph-based model of rank fusions.
Interpolating Local and Global Search by Controlling the Variance of Standard Bit Mutation
Authors: Furong Ye, Carola Doerr, Thomas Bäck
Date: 2019-01-17 00:00:00
Tasks: Evolutionary Algorithms

A key property underlying the success of evolutionary algorithms (EAs) is
their global search behavior, which allows the algorithms to `jump' from a
current state to other parts of the search space, thereby avoiding to get stuck
in local optima. This property is obtained through a random choice of the
radius at which offspring are sampled from previously evaluated solutions. It
is well known that, thanks to this global search behavior, the probability that
an EA using standard bit mutation finds a global optimum of an arbitrary
function $f:\{0,1\}^n \to \mathbb{R}$ tends to one as the number of function
evaluations grows. This advantage over heuristics using a fixed search radius,
however, comes at the cost of using non-optimal step sizes also in those
regimes in which the optimal rate is stable for a long time. This downside
results in significant performance losses for many standard benchmark problems.
  We introduce in this work a simple way to interpolate between the random
global search of EAs and their deterministic counterparts which sample from a
fixed radius only. To this end, we introduce \emph{normalized standard bit
mutation}, in which the binomial choice of the search radius is replaced by a
normal distribution. Normalized standard bit mutation allows a straightforward
way to control its variance, and hence the degree of randomness involved. We
experiment with a self-adjusting choice of this variance, and demonstrate its
effectiveness for the two classic benchmark problems LeadingOnes and OneMax.
Our work thereby also touches a largely ignored question in discrete
evolutionary computation: multi-dimensional parameter control.
Voronoi-based Efficient Surrogate-assisted Evolutionary Algorithm for Very Expensive Problems
Authors: Hao Tong, Changwu Huang, Jialin Liu, Xin Yao
Date: 2019-01-17 00:00:00
Tasks: Evolutionary Algorithms

Very expensive problems are very common in practical system that one fitness evaluation costs several hours or even days. Surrogate assisted evolutionary algorithms (SAEAs) have been widely used to solve this crucial problem in the past decades. However, most studied SAEAs focus on solving problems with a budget of at least ten times of the dimension of problems which is unacceptable in many very expensive real-world problems. In this paper, we employ Voronoi diagram to boost the performance of SAEAs and propose a novel framework named Voronoi-based efficient surrogate assisted evolutionary algorithm (VESAEA) for very expensive problems, in which the optimization budget, in terms of fitness evaluations, is only 5 times of the problem's dimension. In the proposed framework, the Voronoi diagram divides the whole search space into several subspace and then the local search is operated in some potentially better subspace. Additionally, in order to trade off the exploration and exploitation, the framework involves a global search stage developed by combining leave-one-out cross-validation and radial basis function surrogate model. A performance selector is designed to switch the search dynamically and automatically between the global and local search stages. The empirical results on a variety of benchmark problems demonstrate that the proposed framework significantly outperforms several state-of-art algorithms with extremely limited fitness evaluations. Besides, the efficacy of Voronoi-diagram is furtherly analyzed, and the results show its potential to optimize very expensive problems.
Robust Chinese Word Segmentation with Contextualized Word Representations
Authors: Yung-Sung Chuang
Date: 2019-01-17 00:00:00
Tasks: Chinese Word Segmentation, Language Modeling, Language Modelling

In recent years, after the neural-network-based method was proposed, the
accuracy of the Chinese word segmentation task has made great progress.
However, when dealing with out-of-vocabulary words, there is still a large
error rate. We used a simple bidirectional LSTM architecture and a large-scale
pretrained language model to generate high-quality contextualize character
representations, which successfully reduced the weakness of the ambiguous
meanings of each Chinese character that widely appears in Chinese characters,
and hence effectively reduced OOV error rate. State-of-the-art performance is
achieved on many datasets.
A new approach for digit recognition based on hand gesture analysis
Authors: Ahmed Ben Jmaa, Walid Mahdi, Yousra Ben Jemaa, Abdelmajid Ben Hamadou
Date: 2009-06-27 00:00:00
Tasks: Anatomy, Hand Detection, Translation

We present in this paper a new approach for hand gesture analysis that allows
digit recognition. The analysis is based on extracting a set of features from a
hand image and then combining them by using an induction graph. The most
important features we extract from each image are the fingers locations, their
heights and the distance between each pair of fingers. Our approach consists of
three steps: (i) Hand detection and localization, (ii) fingers extraction and
(iii) features identification and combination to digit recognition. Each input
image is assumed to contain only one person, thus we apply a fuzzy classifier
to identify the skin pixels. In the finger extraction step, we attempt to
remove all the hand components except the fingers, this process is based on the
hand anatomy properties. The final step consists on representing histogram of
the detected fingers in order to extract features that will be used for digit
recognition. The approach is invariant to scale, rotation and translation of
the hand. Some experiments have been undertaken to show the effectiveness of
the proposed approach.
English Sentence Recognition using Artificial Neural Network through Mouse-based Gestures
Authors: Firoj Parwej
Date: 2013-01-20 00:00:00
Tasks: Efficient Neural Network, Handwriting Recognition, Sentence

Handwriting is one of the most important means of daily communication.
Although the problem of handwriting recognition has been considered for more
than 60 years there are still many open issues, especially in the task of
unconstrained handwritten sentence recognition. This paper focuses on the
automatic system that recognizes continuous English sentence through a
mouse-based gestures in real-time based on Artificial Neural Network. The
proposed Artificial Neural Network is trained using the traditional
backpropagation algorithm for self supervised neural network which provides the
system with great learning ability and thus has proven highly successful in
training for feed-forward Artificial Neural Network. The designed algorithm is
not only capable of translating discrete gesture moves, but also continuous
gestures through the mouse. In this paper we are using the efficient neural
network approach for recognizing English sentence drawn by mouse. This approach
shows an efficient way of extracting the boundary of the English Sentence and
specifies the area of the recognition English sentence where it has been drawn
in an image and then used Artificial Neural Network to recognize the English
sentence. The proposed approach English sentence recognition (ESR) system is
designed and tested successfully. Experimental results show that the higher
speed and accuracy were examined.
Intelligent Approaches to interact with Machines using Hand Gesture Recognition in Natural way: A Survey
Authors: Ankit Chaudhary, J. L. Raheja, Karen Das, Sonia Raheja
Date: 2013-03-10 00:00:00
Tasks: Gesture Recognition, Hand Detection, Hand Gesture Recognition, Hand-Gesture Recognition

Hand gestures recognition (HGR) is one of the main areas of research for the
engineers, scientists and bioinformatics. HGR is the natural way of Human
Machine interaction and today many researchers in the academia and industry are
working on different application to make interactions more easy, natural and
convenient without wearing any extra device. HGR can be applied from games
control to vision enabled robot control, from virtual reality to smart home
systems. In this paper we are discussing work done in the area of hand gesture
recognition where focus is on the intelligent approaches including soft
computing based methods like artificial neural network, fuzzy logic, genetic
algorithms etc. The methods in the preprocessing of image for segmentation and
hand image construction also taken into study. Most researchers used fingertips
for hand detection in appearance based modeling. Finally the comparison of
results given by different researchers is also presented.
Hand Gesture Recognition based on Radar Micro-Doppler Signature Envelopes
Authors: Moeness G. Amin, Zhengxin Zeng, Tao Shan
Date: 2018-11-29 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

We introduce a simple but effective technique in automatic hand gesture
recognition using radar. The proposed technique classifies hand gestures based
on the envelopes of their micro-Doppler signatures. These envelopes capture the
distinctions among different hand movements and their corresponding positive
and negative Doppler frequencies which are generated during each gesture act.
We detect the positive and negative envelopes separately, and form a feature
vector of their augmentation. We use the $k$-nearest neighbor ($k$NN)
classifier and Manhattan distance (L1) measure, in lieu of Euclidean distance
(L2), so as not to diminish small but critical envelope values. It is shown
that this method outperforms both low-dimension representation techniques based
on principal component analysis (PCA) and sparse reconstruction using
Gaussian-windowed Fourier dictionary, and can achieve very high classification
rates.
Kinect Sensor Based Gesture Recognition for Surveillance Application
Authors: Biswarup Ganguly, Amit Konar
Date: 2018-12-22 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Hand gesture recognition has been granted as one of the emerging fields in
research today providing a natural way of communication between man and a
machine. Gestures are some forms of body motions which a person expresses when
doing a work or giving a reply. Human body tracking is a well studied topic in
todays era of Human Computer Interaction and it can be formed by the virtue of
human skeleton structures. These skeleton structures have been detected
successfully due to the smart progress of some devices, used to measure depth.
Human body movements have been viewed using these depth sensors which can
provide sufficient accuracy while tracking full body in real time mode with low
cost. In reality action and reaction activities are hardly periodic in a multi
person perspective situation. Also recognizing their complex a-periodic
gestures are highly challenging for detection in surveillance system.
Block Argumentation
Authors: Ryuta Arisaka, Stefano Bistarelli, Francesco Santini
Date: 2019-01-18 00:00:00
Tasks: Abstract Argumentation

We contemplate a higher-level bipolar abstract argumentation for
non-elementary arguments such as: X argues against Ys sincerity with the fact
that Y has presented his argument to draw a conclusion C, by omitting other
facts which would not have validated C. Argumentation involving such arguments
requires us to potentially consider an argument as a coherent block of
argumentation, i.e. an argument may itself be an argumentation. In this work,
we formulate block argumentation as a specific instance of Dung-style bipolar
abstract argumentation with the dual nature of arguments. We consider internal
consistency of an argument(ation) under a set of constraints, of graphical
(syntactic) and of semantic nature, and formulate acceptability semantics in
relation to them. We discover that classical acceptability semantics do not in
general hold good with the constraints. In particular, acceptability of
unattacked arguments is not always warranted. Further, there may not be a
unique minimal member in complete semantics, thus sceptic (grounded) semantics
may not be its subset. To retain set-theoretically minimal semantics as a
subset of complete semantics, we define semi-grounded semantics. Through
comparisons, we show how the concept of block argumentation may further
generalise structured argumentation.
ULDor: A Universal Lesion Detector for CT Scans with Pseudo Masks and Hard Negative Example Mining
Authors: Youbao Tang, Ke Yan, Yu-Xing Tang, Jiamin Liu, Jing Xiao, Ronald M. Summers
Date: 2019-01-18 00:00:00
Tasks: Computed Tomography (CT), Lesion Detection

Automatic lesion detection from computed tomography (CT) scans is an
important task in medical imaging analysis. It is still very challenging due to
similar appearances (e.g. intensity and texture) between lesions and other
tissues, making it especially difficult to develop a universal lesion detector.
Instead of developing a specific-type lesion detector, this work builds a
Universal Lesion Detector (ULDor) based on Mask R-CNN, which is able to detect
all different kinds of lesions from whole body parts. As a state-of-the-art
object detector, Mask R-CNN adds a branch for predicting segmentation masks on
each Region of Interest (RoI) to improve the detection performance. However, it
is almost impossible to manually annotate a large-scale dataset with
pixel-level lesion masks to train the Mask R-CNN for lesion detection. To
address this problem, this work constructs a pseudo mask for each lesion region
that can be considered as a surrogate of the real mask, based on which the Mask
R-CNN is employed for lesion detection. On the other hand, this work proposes a
hard negative example mining strategy to reduce the false positives for
improving the detection performance. Experimental results on the NIH DeepLesion
dataset demonstrate that the ULDor is enhanced using pseudo masks and the
proposed hard negative example mining strategy and achieves a sensitivity of
86.21% with five false positives per image.
Embedded CNN based vehicle classification and counting in non-laned road traffic
Authors: Mayank Singh Chauhan, Arshdeep Singh, Mansi Khemka, Arneish Prateek, Rijurekha Sen
Date: 2019-01-18 00:00:00
Tasks: General Classification, object-detection, Object Detection

Classifying and counting vehicles in road traffic has numerous applications
in the transportation engineering domain. However, the wide variety of vehicles
(two-wheelers, three-wheelers, cars, buses, trucks etc.) plying on roads of
developing regions without any lane discipline, makes vehicle classification
and counting a hard problem to automate. In this paper, we use state of the art
Convolutional Neural Network (CNN) based object detection models and train them
for multiple vehicle classes using data from Delhi roads. We get upto 75% MAP
on an 80-20 train-test split using 5562 video frames from four different
locations. As robust network connectivity is scarce in developing regions for
continuous video transmissions from the road to cloud servers, we also evaluate
the latency, energy and hardware cost of embedded implementations of our CNN
model based inferences.
Robust Anomaly Detection in Images using Adversarial Autoencoders
Authors: Laura Beggel, Michael Pfeiffer, Bernd Bischl
Date: 2019-01-18 00:00:00
Tasks: Anomaly Detection, Medical Image Analysis

Reliably detecting anomalies in a given set of images is a task of high
practical relevance for visual quality inspection, surveillance, or medical
image analysis. Autoencoder neural networks learn to reconstruct normal images,
and hence can classify those images as anomalies, where the reconstruction
error exceeds some threshold. Here we analyze a fundamental problem of this
approach when the training set is contaminated with a small fraction of
outliers. We find that continued training of autoencoders inevitably reduces
the reconstruction error of outliers, and hence degrades the anomaly detection
performance. In order to counteract this effect, an adversarial autoencoder
architecture is adapted, which imposes a prior distribution on the latent
representation, typically placing anomalies into low likelihood-regions.
Utilizing the likelihood model, potential anomalies can be identified and
rejected already during training, which results in an anomaly detector that is
significantly more robust to the presence of outliers during training.
Adapting Convolutional Neural Networks for Geographical Domain Shift
Authors: Pavel Ostyakov, Sergey I. Nikolenko
Date: 2019-01-18 00:00:00

We present the winning solution for the Inclusive Images Competition
organized as part of the Conference on Neural Information Processing Systems
(NeurIPS 2018) Competition Track. The competition was organized to study ways
to cope with domain shift in image processing, specifically geographical shift:
the training and two test sets in the competition had different geographical
distributions. Our solution has proven to be relatively straightforward and
simple: it is an ensemble of several CNNs where only the last layer is
fine-tuned with the help of a small labeled set of tuning labels made available
by the organizers. We believe that while domain shift remains a formidable
problem, our approach opens up new possibilities for alleviating this problem
in practice, where small labeled datasets from the target domain are usually
either available or can be obtained and labeled cheaply.
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection
Authors: Fan Yang, Lei Zhang, Sijia Yu, Danil Prokhorov, Xue Mei, Haibin Ling
Date: 2019-01-18 00:00:00
Tasks: Edge Detection, Semantic Segmentation

Pavement crack detection is a critical task for insuring road safety. Manual
crack detection is extremely time-consuming. Therefore, an automatic road crack
detection method is required to boost this progress. However, it remains a
challenging task due to the intensity inhomogeneity of cracks and complexity of
the background, e.g., the low contrast with surrounding pavements and possible
shadows with similar intensity. Inspired by recent advances of deep learning in
computer vision, we propose a novel network architecture, named Feature Pyramid
and Hierarchical Boosting Network (FPHBN), for pavement crack detection. The
proposed network integrates semantic information to low-level features for
crack detection in a feature pyramid way. And, it balances the contribution of
both easy and hard samples to loss by nested sample reweighting in a
hierarchical way. To demonstrate the superiority and generality of the proposed
method, we evaluate the proposed method on five crack datasets and compare it
with state-of-the-art crack detection, edge detection, semantic segmentation
methods. Extensive experiments show that the proposed method outperforms these
state-of-the-art methods in terms of accuracy and generality.
Estimating the effective dimension of large biological datasets using Fisher separability analysis
Authors: Luca Albergante, Jonathan Bac, Andrei Zinovyev
Date: 2019-01-18 00:00:00
Tasks: valid

Modern large-scale datasets are frequently said to be high-dimensional.
However, their data point clouds frequently possess structures, significantly
decreasing their intrinsic dimensionality (ID) due to the presence of clusters,
points being located close to low-dimensional varieties or fine-grained
lumping. We test a recently introduced dimensionality estimator, based on
analysing the separability properties of data points, on several benchmarks and
real biological datasets. We show that the introduced measure of ID has
performance competitive with state-of-the-art measures, being efficient across
a wide range of dimensions and performing better in the case of noisy samples.
Moreover, it allows estimating the intrinsic dimension in situations where the
intrinsic manifold assumption is not valid.
Learning Spatial Pyramid Attentive Pooling in Image Synthesis and Image-to-Image Translation
Authors: Wei Sun, Tianfu Wu
Date: 2019-01-18 00:00:00
Tasks: Image Generation, Image-to-Image Translation, Translation

Image synthesis and image-to-image translation are two important generative
learning tasks. Remarkable progress has been made by learning Generative
Adversarial Networks (GANs)~\cite{goodfellow2014generative} and
cycle-consistent GANs (CycleGANs)~\cite{zhu2017unpaired} respectively. This
paper presents a method of learning Spatial Pyramid Attentive Pooling (SPAP)
which is a novel architectural unit and can be easily integrated into both
generators and discriminators in GANs and CycleGANs. The proposed SPAP
integrates Atrous spatial pyramid~\cite{chen2018deeplab}, a proposed cascade
attention mechanism and residual connections~\cite{he2016deep}. It leverages
the advantages of the three components to facilitate effective end-to-end
generative learning: (i) the capability of fusing multi-scale information by
ASPP; (ii) the capability of capturing relative importance between both spatial
locations (especially multi-scale context) or feature channels by attention;
(iii) the capability of preserving information and enhancing optimization
feasibility by residual connections. Coarse-to-fine and fine-to-coarse SPAP are
studied and intriguing attention maps are observed in both tasks. In
experiments, the proposed SPAP is tested in GANs on the Celeba-HQ-128
dataset~\cite{karras2017progressive}, and tested in CycleGANs on the
Image-to-Image translation datasets including the Cityscape
dataset~\cite{cordts2016cityscapes}, Facade and Aerial Maps
dataset~\cite{zhu2017unpaired}, both obtaining better performance.