pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning of Nonnegativity-Constrained Autoencoders for Enhanced Understanding of Data
Authors: Babajide O. Ayinde, Jacek M. Zurada
Date: 2018-01-31 00:00:00
Tasks: General Classification, L2 Regularization, Text Categorization

Unsupervised feature extractors are known to perform an efficient and
discriminative representation of data. Insight into the mappings they perform
and human ability to understand them, however, remain very limited. This is
especially prominent when multilayer deep learning architectures are used. This
paper demonstrates how to remove these bottlenecks within the architecture of
Nonnegativity Constrained Autoencoder (NCSAE). It is shown that by using both
L1 and L2 regularization that induce nonnegativity of weights, most of the
weights in the network become constrained to be nonnegative thereby resulting
into a more understandable structure with minute deterioration in
classification accuracy. Also, this proposed approach extracts features that
are more sparse and produces additional output layer sparsification. The method
is analyzed for accuracy and feature interpretation on the MNIST data, the NORB
normalized uniform object data, and the Reuters text categorization dataset.
Image Posterization Using Fuzzy Logic and Bilateral Filter
Authors: Mahmoud Afifi
Date: 2018-02-03 00:00:00
Tasks: Face Detection, Human Detection

Image posterization is converting images with a large number of tones into
synthetic images with distinct flat areas and a fewer number of tones. In this
technical report, we present the implementation and results of using fuzzy
logic in order to generate a posterized image in a simple and fast way. The
image filter is based on fuzzy logic and bilateral filtering; where, the given
image is blurred to remove small details. Then, the fuzzy logic is used to
classify each pixel into one of three specific categories in order to reduce
the number of colors. This filter was developed during building the Specs on
Face dataset in order to add a new level of difficulty to the original face
images in the dataset. This filter does not hurt the human detection
performance; however, it is considered a hindrance evading the face detection
process. This filter can be used generally for posterizing images, especially
those have a high contrast to get images with vivid colors.
Towards Multi-Object Detection and Tracking in Urban Scenario under Uncertainties
Authors: Achim Kampker, Mohsen Sefati, Arya Abdul Rachman, Kai Kreisköther, Pascual Campoy
Date: 2018-01-08 00:00:00
Tasks: Autonomous Vehicles, Object, object-detection, Object Detection

Urban-oriented autonomous vehicles require a reliable perception technology
to tackle the high amount of uncertainties. The recently introduced compact 3D
LIDAR sensor offers a surround spatial information that can be exploited to
enhance the vehicle perception. We present a real-time integrated framework of
multi-target object detection and tracking using 3D LIDAR geared toward urban
use. Our approach combines sensor occlusion-aware detection method with
computationally efficient heuristics rule-based filtering and adaptive
probabilistic tracking to handle uncertainties arising from sensing limitation
of 3D LIDAR and complexity of the target object movement. The evaluation
results using real-world pre-recorded 3D LIDAR data and comparison with
state-of-the-art works shows that our framework is capable of achieving
promising tracking performance in the urban situation.
On the Generalizability of Linear and Non-Linear Region of Interest-Based Multivariate Regression Models for fMRI Data
Authors: Ethan C. Jackson, James Alexander Hughes, Mark Daley
Date: 2018-02-03 00:00:00
Tasks: Experimental Design, regression, Symbolic Regression

In contrast to conventional, univariate analysis, various types of
multivariate analysis have been applied to functional magnetic resonance
imaging (fMRI) data. In this paper, we compare two contemporary approaches for
multivariate regression on task-based fMRI data: linear regression with ridge
regularization and non-linear symbolic regression using genetic programming.
The data for this project is representative of a contemporary fMRI experimental
design for visual stimuli. Linear and non-linear models were generated for 10
subjects, with another 4 withheld for validation. Model quality is evaluated by
comparing $R$ scores (Pearson product-moment correlation) in various contexts,
including single run self-fit, within-subject generalization, and
between-subject generalization. Propensity for modelling strategies to overfit
is estimated using a separate resting state scan. Results suggest that neither
method is objectively or inherently better than the other.
How to augment a small learning set for improving the performances of a CNN-based steganalyzer?
Authors: Mehdi Yedroudj, Marc Chaumont, Frédéric Comby
Date: 2018-01-12 00:00:00
Tasks: Steganalysis

Deep learning and convolutional neural networks (CNN) have been intensively
used in many image processing topics during last years. As far as steganalysis
is concerned, the use of CNN allows reaching the state-of-the-art results. The
performances of such networks often rely on the size of their learning
database. An obvious preliminary assumption could be considering that "the
bigger a database is, the better the results are". However, it appears that
cautions have to be taken when increasing the database size if one desire to
improve the classification accuracy i.e. enhance the steganalysis efficiency.
To our knowledge, no study has been performed on the enrichment impact of a
learning database on the steganalysis performance. What kind of images can be
added to the initial learning set? What are the sensitive criteria: the camera
models used for acquiring the images, the treatments applied to the images, the
cameras proportions in the database, etc? This article continues the work
carried out in a previous paper, and explores the ways to improve the
performances of CNN. It aims at studying the effects of "base augmentation" on
the performance of steganalysis using a CNN. We present the results of this
study using various experimental protocols and various databases to define the
good practices in base augmentation for steganalysis.
Pose Flow: Efficient Online Pose Tracking
Authors: Yuliang Xiu, Jiefeng Li, Haoyu Wang, Yinghong Fang, Cewu Lu
Date: 2018-02-03 00:00:00
Tasks: Pose Tracking

Multi-person articulated pose tracking in unconstrained videos is an
important while challenging problem. In this paper, going along the road of
top-down approaches, we propose a decent and efficient pose tracker based on
pose flows. First, we design an online optimization framework to build the
association of cross-frame poses and form pose flows (PF-Builder). Second, a
novel pose flow non-maximum suppression (PF-NMS) is designed to robustly reduce
redundant pose flows and re-link temporal disjoint ones. Extensive experiments
show that our method significantly outperforms best-reported results on two
standard Pose Tracking datasets by 13 mAP 25 MOTA and 6 mAP 3 MOTA
respectively. Moreover, in the case of working on detected poses in individual
frames, the extra computation of pose tracker is very minor, guaranteeing
online 10FPS tracking. Our source codes are made publicly
available(https://github.com/YuliangXiu/PoseFlow).
Learning Structured Text Representations
Authors: Yang Liu, Mirella Lapata
Date: 2017-05-25 00:00:00
Proceeding: TACL 2018 1

In this paper, we focus on learning structure-aware document representations
from data without recourse to a discourse parser or additional annotations.
Drawing inspiration from recent efforts to empower neural networks with a
structural bias, we propose a model that can encode a document while
automatically inducing rich structural dependencies. Specifically, we embed a
differentiable non-projective parsing algorithm into a neural model and use
attention mechanisms to incorporate the structural biases. Experimental
evaluation across different tasks and datasets shows that the proposed model
achieves state-of-the-art results on document modeling tasks while inducing
intermediate structures which are both interpretable and meaningful.
Resset: A Recurrent Model for Sequence of Sets with Applications to Electronic Medical Records
Authors: Phuoc Nguyen, Truyen Tran, Svetha Venkatesh
Date: 2018-02-03 00:00:00
Tasks: Readmission Prediction

Modern healthcare is ripe for disruption by AI. A game changer would be
automatic understanding the latent processes from electronic medical records,
which are being collected for billions of people worldwide. However, these
healthcare processes are complicated by the interaction between at least three
dynamic components: the illness which involves multiple diseases, the care
which involves multiple treatments, and the recording practice which is biased
and erroneous. Existing methods are inadequate in capturing the dynamic
structure of care. We propose Resset, an end-to-end recurrent model that reads
medical record and predicts future risk. The model adopts the algebraic view in
that discrete medical objects are embedded into continuous vectors lying in the
same space. We formulate the problem as modeling sequences of sets, a novel
setting that have rarely, if not, been addressed. Within Resset, the bag of
diseases recorded at each clinic visit is modeled as function of sets. The same
hold for the bag of treatments. The interaction between the disease bag and the
treatment bag at a visit is modeled in several, one of which as residual of
diseases minus the treatments. Finally, the health trajectory, which is a
sequence of visits, is modeled using a recurrent neural network. We report
results on over a hundred thousand hospital visits by patients suffered from
two costly chronic diseases -- diabetes and mental health. Resset shows
promises in multiple predictive tasks such as readmission prediction,
treatments recommendation and diseases progression.
Efficient Data Representation by Selecting Prototypes with Importance Weights
Authors: Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo Cecchi, Charu Aggarwal
Date: 2017-07-05 00:00:00

Prototypical examples that best summarizes and compactly represents an underlying complex data distribution communicate meaningful insights to humans in domains where simple explanations are hard to extract. In this paper we present algorithms with strong theoretical guarantees to mine these data sets and select prototypes a.k.a. representatives that optimally describes them. Our work notably generalizes the recent work by Kim et al. (2016) where in addition to selecting prototypes, we also associate non-negative weights which are indicative of their importance. This extension provides a single coherent framework under which both prototypes and criticisms (i.e. outliers) can be found. Furthermore, our framework works for any symmetric positive definite kernel thus addressing one of the key open questions laid out in Kim et al. (2016). By establishing that our objective function enjoys a key property of that of weak submodularity, we present a fast ProtoDash algorithm and also derive approximation guarantees for the same. We demonstrate the efficacy of our method on diverse domains such as retail, digit recognition (MNIST) and on publicly available 40 health questionnaires obtained from the Center for Disease Control (CDC) website maintained by the US Dept. of Health. We validate the results quantitatively as well as qualitatively based on expert feedback and recently published scientific studies on public health, thus showcasing the power of our technique in providing actionability (for retail), utility (for MNIST) and insight (on CDC datasets) which arguably are the hallmarks of an effective data mining method.
Ensembling Neural Networks for Digital Pathology Images Classification and Segmentation
Authors: Gleb Makarchuk, Vladimir Kondratenko, Maxim Pisov, Artem Pimkin, Egor Krivov, Mikhail Belyaev
Date: 2018-02-03 00:00:00
Tasks: Classification, General Classification

In the last years, neural networks have proven to be a powerful framework for various image analysis problems. However, some application domains have specific limitations. Notably, digital pathology is an example of such fields due to tremendous image sizes and quite limited number of training examples available. In this paper, we adopt state-of-the-art convolutional neural networks (CNN) architectures for digital pathology images analysis. We propose to classify image patches to increase effective sample size and then to apply an ensembling technique to build prediction for the original images. To validate the developed approaches, we conducted experiments with \textit{Breast Cancer Histology Challenge} dataset and obtained 90\% accuracy for the 4-class tissue classification task.
Content based Weighted Consensus Summarization
Authors: Parth Mehta, Prasenjit Majumder
Date: 2018-02-03 00:00:00
Tasks: Document Summarization, Multi-Document Summarization, Sentence

Multi-document summarization has received a great deal of attention in the
past couple of decades. Several approaches have been proposed, many of which
perform equally well and it is becoming in- creasingly difficult to choose one
particular system over another. An ensemble of such systems that is able to
leverage the strengths of each individual systems can build a better and more
robust summary. Despite this, few attempts have been made in this direction. In
this paper, we describe a category of ensemble systems which use consensus
between the candidate systems to build a better meta-summary. We highlight two
major shortcomings of such systems: the inability to take into account relative
performance of individual systems and overlooking content of candidate
summaries in favour of the sentence rankings. We propose an alternate method,
content-based weighted consensus summarization, which address these concerns.
We use pseudo-relevant summaries to estimate the performance of individual
candidate systems, and then use this information to generate a better aggregate
ranking. Experiments on DUC 2003 and DUC 2004 datasets show that the proposed
system outperforms existing consensus-based techniques by a large margin.
SWRL2SPIN: A tool for transforming SWRL rule bases in OWL ontologies to object-oriented SPIN rules
Authors: Nick Bassiliades
Date: 2018-01-27 00:00:00

Semantic Web Rule Language (SWRL) combines OWL (Web Ontology Language) ontologies with Horn Logic rules of the Rule Markup Language (RuleML) family. Being supported by ontology editors, rule engines and ontology reasoners, it has become a very popular choice for developing rule-based applications on top of ontologies. However, SWRL is probably not go-ing to become a WWW Consortium standard, prohibiting industrial acceptance. On the other hand, SPIN (SPARQL Inferencing Notation) has become a de-facto industry standard to rep-resent SPARQL rules and constraints on Semantic Web models, building on the widespread acceptance of SPARQL (SPARQL Protocol and RDF Query Language). In this paper, we ar-gue that the life of existing SWRL rule-based ontology applications can be prolonged by con-verting them to SPIN. To this end, we have developed the SWRL2SPIN tool in Prolog that transforms SWRL rules into SPIN rules, considering the object-orientation of SPIN, i.e. linking rules to the appropriate ontology classes and optimizing them, as derived by analysing the rule conditions.
Learning the Synthesizability of Dynamic Texture Samples
Authors: Feng Yang, Gui-Song Xia, Dengxin Dai, Liangpei Zhang
Date: 2018-02-03 00:00:00
Tasks: regression, Texture Synthesis

A dynamic texture (DT) refers to a sequence of images that exhibit temporal
regularities and has many applications in computer vision and graphics. Given
an exemplar of dynamic texture, it is a dynamic but challenging task to
generate new samples with high quality that are perceptually similar to the
input exemplar, which is known to be {\em example-based dynamic texture
synthesis (EDTS)}. Numerous approaches have been devoted to this problem, in
the past decades, but none them are able to tackle all kinds of dynamic
textures equally well. In this paper, we investigate the synthesizability of
dynamic texture samples: {\em given a dynamic texture sample, how synthesizable
it is by using EDTS, and which EDTS method is the most suitable to synthesize
it?} To this end, we propose to learn regression models to connect dynamic
texture samples with synthesizability scores, with the help of a compiled
dynamic texture dataset annotated in terms of synthesizability. More precisely,
we first define the synthesizability of DT samples and characterize them by a
set of spatiotemporal features. Based on these features and an annotated
dynamic texture dataset, we then train regression models to predict the
synthesizability scores of texture samples and learn classifiers to select the
most suitable EDTS methods. We further complete the selection, partition and
synthesizability prediction of dynamic texture samples in a hierarchical
scheme. We finally apply the learned synthesizability to detecting
synthesizable regions in videos. The experiments demonstrate that our method
can effectively learn and predict the synthesizability of DT samples.
DeepProcess: Supporting business process execution using a MANN-based recommender system
Authors: Asjad Khan, Hung Le, Kien Do, Truyen Tran, Aditya Ghose, Hoa Dam, Renuka Sindhgatta
Date: 2018-02-03 00:00:00
Tasks: Activity Prediction, Recommendation Systems

Process-aware Recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions to take next. Based on recent advances in the field of deep learning, we present a novel memory-augmented neural network (MANN) based approach for constructing a process-aware recommender system. We propose a novel network architecture, namely Write-Protected Dual Controller Memory-Augmented Neural Network (DCw-MANN), for building prescriptive models. To evaluate the feasibility and usefulness of our approach, we consider three real-world datasets and show that our approach leads to better performance on several baselines for the task of suffix recommendation and next task prediction.
Asynchronous Bidirectional Decoding for Neural Machine Translation
Authors: Xiangwen Zhang, Jinsong Su, Yue Qin, Yang Liu, Rongrong Ji, Hongji Wang
Date: 2018-01-16 00:00:00
Tasks: Decoder, Machine Translation, NMT, Translation

The dominant neural machine translation (NMT) models apply unified
attentional encoder-decoder neural networks for translation. Traditionally, the
NMT decoders adopt recurrent neural networks (RNNs) to perform translation in a
left-toright manner, leaving the target-side contexts generated from right to
left unexploited during translation. In this paper, we equip the conventional
attentional encoder-decoder NMT framework with a backward decoder, in order to
explore bidirectional decoding for NMT. Attending to the hidden state sequence
produced by the encoder, our backward decoder first learns to generate the
target-side hidden state sequence from right to left. Then, the forward decoder
performs translation in the forward direction, while in each translation
prediction timestep, it simultaneously applies two attention models to consider
the source-side and reverse target-side hidden states, respectively. With this
new architecture, our model is able to fully exploit source- and target-side
contexts to improve translation quality altogether. Experimental results on
NIST Chinese-English and WMT English-German translation tasks demonstrate that
our model achieves substantial improvements over the conventional NMT by 3.14
and 1.38 BLEU points, respectively. The source code of this work can be
obtained from https://github.com/DeepLearnXMU/ABDNMT.
Weighting Scheme for a Pairwise Multi-label Classifier Based on the Fuzzy Confusion Matrix
Authors: Pawel Trajdos, Marek Kurzynski
Date: 2017-10-25 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

In this work we addressed the issue of applying a stochastic classifier and a
local, fuzzy confusion matrix under the framework of multi-label
classification. We proposed a novel solution to the problem of correcting label
pairwise ensembles. The main step of the correction procedure is to compute
classifier-specific competence and cross-competence measures, which estimates
error pattern of the underlying classifier. At the fusion phase we employed two
weighting approaches based on information theory. The classifier weights
promote base classifiers which are the most susceptible to the correction based
on the fuzzy confusion matrix. During the experimental study, the proposed
approach was compared against two reference methods. The comparison was made in
terms of six different quality criteria. The conducted experiments reveals that
the proposed approach eliminates one of main drawbacks of the original
FCM-based approach i.e. the original approach is vulnerable to the imbalanced
class/label distribution. What is more, the obtained results shows that the
introduced method achieves satisfying classification quality under all
considered quality criteria. Additionally, the impact of fluctuations of data
set characteristics is reduced.
Deep Learning Framework for Multi-class Breast Cancer Histology Image Classification
Authors: Yeeleng S. Vang, Zhen Chen, Xiaohui Xie
Date: 2018-02-03 00:00:00
Tasks: Breast Cancer Histology Image Classification, General Classification, GPU, image-classification, Image Classification, regression

In this work, we present a deep learning framework for multi-class breast
cancer image classification as our submission to the International Conference
on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer
Histology images (BACH). As these histology images are too large to fit into
GPU memory, we first propose using Inception V3 to perform patch level
classification. The patch level predictions are then passed through an ensemble
fusion framework involving majority voting, gradient boosting machine (GBM),
and logistic regression to obtain the image level prediction. We improve the
sensitivity of the Normal and Benign predicted classes by designing a Dual Path
Network (DPN) to be used as a feature extractor where these extracted features
are further sent to a second layer of ensemble prediction fusion using GBM,
logistic regression, and support vector machine (SVM) to refine predictions.
Experimental results demonstrate our framework shows a 12.5$\%$ improvement
over the state-of-the-art model.
Memory Fusion Network for Multi-view Sequential Learning
Authors: Amir Zadeh, Paul Pu Liang, Navonil Mazumder, Soujanya Poria, Erik Cambria, Louis-Philippe Morency
Date: 2018-02-03 00:00:00

Multi-view sequential learning is a fundamental problem in machine learning
dealing with multi-view sequences. In a multi-view sequence, there exists two
forms of interactions between different views: view-specific interactions and
cross-view interactions. In this paper, we present a new neural architecture
for multi-view sequential learning called the Memory Fusion Network (MFN) that
explicitly accounts for both interactions in a neural architecture and
continuously models them through time. The first component of the MFN is called
the System of LSTMs, where view-specific interactions are learned in isolation
through assigning an LSTM function to each view. The cross-view interactions
are then identified using a special attention mechanism called the Delta-memory
Attention Network (DMAN) and summarized through time with a Multi-view Gated
Memory. Through extensive experimentation, MFN is compared to various proposed
approaches for multi-view sequential learning on multiple publicly available
benchmark datasets. MFN outperforms all the existing multi-view approaches.
Furthermore, MFN outperforms all current state-of-the-art models, setting new
state-of-the-art results for these multi-view datasets.