pwc-archive/papers-with-abstracts huggingface dataset:
SentiBubbles: Topic Modeling and Sentiment Visualization of Entity-centric Tweets
Authors: João Oliveira, Mike Pinto, Pedro Saleiro, Jorge Teixeira
Date: 2016-07-01 00:00:00
Tasks: Data Visualization, Sentiment Analysis

Social Media users tend to mention entities when reacting to news events. The
main purpose of this work is to create entity-centric aggregations of tweets on
a daily basis. By applying topic modeling and sentiment analysis, we create
data visualization insights about current events and people reactions to those
events from an entity-centric perspective.
Evaluating Layers of Representation in Neural Machine Translation on Part-of-Speech and Semantic Tagging Tasks
Authors: Yonatan Belinkov, Lluís Màrquez, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, James Glass
Date: 2018-01-23 00:00:00
Proceeding: IJCNLP 2017 11
Tasks: Machine Translation, NMT, Part-Of-Speech Tagging, Representation Learning, Translation

While neural machine translation (NMT) models provide improved translation
quality in an elegant, end-to-end framework, it is less clear what they learn
about language. Recent work has started evaluating the quality of vector
representations learned by NMT models on morphological and syntactic tasks. In
this paper, we investigate the representations learned at different layers of
NMT encoders. We train NMT systems on parallel data and use the trained models
to extract features for training a classifier on two tasks: part-of-speech and
semantic tagging. We then measure the performance of the classifier as a proxy
to the quality of the original NMT model for the given task. Our quantitative
analysis yields interesting insights regarding representation learning in NMT
models. For instance, we find that higher layers are better at learning
semantics while lower layers tend to be better for part-of-speech tagging. We
also observe little effect of the target language on source-side
representations, especially with higher quality NMT models.
Image Compression: Sparse Coding vs. Bottleneck Autoencoders
Authors: Yijing Watkins, Mohammad Sayeh, Oleksandr Iaroshenko, Garrett Kenyon
Date: 2017-10-26 00:00:00
Tasks: General Classification, Image Compression, SSIM

Bottleneck autoencoders have been actively researched as a solution to image
compression tasks. However, we observed that bottleneck autoencoders produce
subjectively low quality reconstructed images. In this work, we explore the
ability of sparse coding to improve reconstructed image quality for the same
degree of compression. We observe that sparse image compression produces
visually superior reconstructed images and yields higher values of pixel-wise
measures of reconstruction quality (PSNR and SSIM) compared to bottleneck
autoencoders. % In addition, we find that using alternative metrics that
correlate better with human perception, such as feature perceptual loss and the
classification accuracy, sparse image compression scores up to 18.06\% and
2.7\% higher, respectively, compared to bottleneck autoencoders. Although
computationally much more intensive, we find that sparse coding is otherwise
superior to bottleneck autoencoders for the same degree of compression.
Quantified Degrees of Group Responsibility (Extended Abstract)
Authors: Vahid Yazdanpanah, Mehdi Dastani
Date: 2018-01-23 00:00:00

This paper builds on an existing notion of group responsibility and proposes
two ways to define the degree of group responsibility: structural and
functional degrees of responsibility. These notions measure the potential
responsibilities of (agent) groups for avoiding a state of affairs. According
to these notions, a degree of responsibility for a state of affairs can be
assigned to a group of agents if, and to the extent that, the group has the
potential to preclude the state of affairs.
Entity Retrieval and Text Mining for Online Reputation Monitoring
Authors: Pedro Saleiro
Date: 2018-01-23 00:00:00
Tasks: Entity Disambiguation, Entity Retrieval, Retrieval, Sentiment Analysis

Online Reputation Monitoring (ORM) is concerned with the use of computational
tools to measure the reputation of entities online, such as politicians or
companies. In practice, current ORM methods are constrained to the generation
of data analytics reports, which aggregate statistics of popularity and
sentiment on social media. We argue that this format is too restrictive as end
users often like to have the flexibility to search for entity-centric
information that is not available in predefined charts. As such, we propose the
inclusion of entity retrieval capabilities as a first step towards the
extension of current ORM capabilities. However, an entity's reputation is also
influenced by the entity's relationships with other entities. Therefore, we
address the problem of Entity-Relationship (E-R) retrieval in which the goal is
to search for multiple connected entities. This is a challenging problem which
traditional entity search systems cannot cope with. Besides E-R retrieval we
also believe ORM would benefit of text-based entity-centric prediction
capabilities, such as predicting entity popularity on social media based on
news events or the outcome of political surveys. However, none of these tasks
can provide useful results if there is no effective entity disambiguation and
sentiment analysis tailored to the context of ORM. Consequently, this thesis
address two computational problems in Online Reputation Monitoring: Entity
Retrieval and Text Mining. We researched and developed methods to extract,
retrieve and predict entity-centric information spread across the Web.
Estimation of Variance and Spatial Correlation Width for Fine-scale Measurement Error in Digital Elevation Model
Authors: Mykhail Uss, Benoit Vozel, Vladimir Lukin, Kacem Chehdi
Date: 2018-01-23 00:00:00
Tasks: parameter estimation

In this paper, we borrow from blind noise parameter estimation (BNPE)
methodology early developed in the image processing field an original and
innovative no-reference approach to estimate Digital Elevation Model (DEM)
vertical error parameters without resorting to a reference DEM. The challenges
associated with the proposed approach related to the physical nature of the
error and its multifactor structure in DEM are discussed in detail. A suitable
multivariate method is then developed for estimating the error in gridded DEM.
It is built on a recently proposed vectorial BNPE method for estimating
spatially correlated noise using Noise Informative areas and Fractal Brownian
Motion. The newly multivariate method is derived to estimate the effect of the
stacking procedure and that of the epipolar line error on local (fine-scale)
standard deviation and autocorrelation function width of photogrammetric DEM
measurement error. Applying the new estimator to ASTER GDEM2 and ALOS World 3D
DEMs, good agreement of derived estimates with results available in the
literature is evidenced. In future works, the proposed no-reference method for
analyzing DEM error can be extended to a larger number of predictors for
accounting for other factors influencing remote sensing (RS) DEM accuracy.
SentiPers: A Sentiment Analysis Corpus for Persian
Authors: Pedram Hosseini, Ali Ahmadian Ramaki, Hassan Maleki, Mansoureh Anvari, Seyed Abolghasem Mirroshandel
Date: 2018-01-23 00:00:00
Tasks: Information Retrieval, Opinion Mining, Retrieval, Sentence, Sentiment Analysis

Sentiment Analysis (SA) is a major field of study in natural language processing, computational linguistics and information retrieval. Interest in SA has been constantly growing in both academia and industry over the recent years. Moreover, there is an increasing need for generating appropriate resources and datasets in particular for low resource languages including Persian. These datasets play an important role in designing and developing appropriate opinion mining platforms using supervised, semi-supervised or unsupervised methods. In this paper, we outline the entire process of developing a manually annotated sentiment corpus, SentiPers, which covers formal and informal written contemporary Persian. To the best of our knowledge, SentiPers is a unique sentiment corpus with such a rich annotation in three different levels including document-level, sentence-level, and entity/aspect-level for Persian. The corpus contains more than 26000 sentences of users opinions from digital product domain and benefits from special characteristics such as quantifying the positiveness or negativity of an opinion through assigning a number within a specific range to any given sentence. Furthermore, we present statistics on various components of our corpus as well as studying the inter-annotator agreement among the annotators. Finally, some of the challenges that we faced during the annotation process will be discussed as well.
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Authors: Jiankang Deng, Jia Guo, Jing Yang, Niannan Xue, Irene Kotsia, Stefanos Zafeiriou
Date: 2018-01-23 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Generation, Face Identification, Face Recognition, Face Verification

Recently, a popular line of research in face recognition is adopting margins in the well-established softmax loss function to maximize class separability. In this paper, we first introduce an Additive Angular Margin Loss (ArcFace), which not only has a clear geometric interpretation but also significantly enhances the discriminative power. Since ArcFace is susceptible to the massive label noise, we further propose sub-center ArcFace, in which each class contains $K$ sub-centers and training samples only need to be close to any of the $K$ positive sub-centers. Sub-center ArcFace encourages one dominant sub-class that contains the majority of clean faces and non-dominant sub-classes that include hard or noisy faces. Based on this self-propelled isolation, we boost the performance through automatically purifying raw web faces under massive real-world noise. Besides discriminative feature embedding, we also explore the inverse problem, mapping feature vectors to face images. Without training any additional generator or discriminator, the pre-trained ArcFace model can generate identity-preserved face images for both subjects inside and outside the training data only by using the network gradient and Batch Normalization (BN) priors. Extensive experiments demonstrate that ArcFace can enhance the discriminative feature embedding as well as strengthen the generative face synthesis.
Tractable Learning and Inference for Large-Scale Probabilistic Boolean Networks
Authors: Ifigeneia Apostolopoulou, Diana Marculescu
Date: 2018-01-23 00:00:00

Probabilistic Boolean Networks (PBNs) have been previously proposed so as to
gain insights into complex dy- namical systems. However, identification of
large networks and of the underlying discrete Markov Chain which describes
their temporal evolution, still remains a challenge. In this paper, we
introduce an equivalent representation for the PBN, the Stochastic Conjunctive
Normal Form (SCNF), which paves the way to a scalable learning algorithm and
helps predict long- run dynamic behavior of large-scale systems. Moreover, SCNF
allows its efficient sampling so as to statistically infer multi- step
transition probabilities which can provide knowledge on the activity levels of
individual nodes in the long run.
3D Reconstruction in Canonical Co-ordinate Space from Arbitrarily Oriented 2D Images
Authors: Benjamin Hou, Bishesh Khanal, Amir Alansary, Steven McDonagh, Alice Davidson, Mary Rutherford, Jo V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz
Date: 2017-09-19 00:00:00
Tasks: 3D Reconstruction, Image Reconstruction, Image Registration, Motion Compensation

Limited capture range, and the requirement to provide high quality
initialization for optimization-based 2D/3D image registration methods, can
significantly degrade the performance of 3D image reconstruction and motion
compensation pipelines. Challenging clinical imaging scenarios, which contain
significant subject motion such as fetal in-utero imaging, complicate the 3D
image and volume reconstruction process. In this paper we present a learning
based image registration method capable of predicting 3D rigid transformations
of arbitrarily oriented 2D image slices, with respect to a learned canonical
atlas co-ordinate system. Only image slice intensity information is used to
perform registration and canonical alignment, no spatial transform
initialization is required. To find image transformations we utilize a
Convolutional Neural Network (CNN) architecture to learn the regression
function capable of mapping 2D image slices to a 3D canonical atlas space. We
extensively evaluate the effectiveness of our approach quantitatively on
simulated Magnetic Resonance Imaging (MRI), fetal brain imagery with synthetic
motion and further demonstrate qualitative results on real fetal MRI data where
our method is integrated into a full reconstruction and motion compensation
pipeline. Our learning based registration achieves an average spatial
prediction error of 7 mm on simulated data and produces qualitatively improved
reconstructions for heavily moving fetuses with gestational ages of
approximately 20 weeks. Our model provides a general and computationally
efficient solution to the 2D/3D registration initialization problem and is
suitable for real-time scenarios.
A Classification Refinement Strategy for Semantic Segmentation
Authors: James W. Davis, Christopher Menart, Muhammad Akbar, Roman Ilin
Date: 2018-01-23 00:00:00
Tasks: Classification, General Classification, Segmentation, Semantic Segmentation

Based on the observation that semantic segmentation errors are partially
predictable, we propose a compact formulation using confusion statistics of the
trained classifier to refine (re-estimate) the initial pixel label hypotheses.
The proposed strategy is contingent upon computing the classifier confusion
probabilities for a given dataset and estimating a relevant prior on the object
classes present in the image to be classified. We provide a procedure to
robustly estimate the confusion probabilities and explore multiple prior
definitions. Experiments are shown comparing performances on multiple
challenging datasets using different priors to improve a state-of-the-art
semantic segmentation classifier. This study demonstrates the potential to
significantly improve semantic labeling and motivates future work for reliable
label prior estimation from images.
Pruning Techniques for Mixed Ensembles of Genetic Programming Models
Authors: Mauro Castelli, Ivo Gonçalves, Luca Manzoni, Leonardo Vanneschi
Date: 2018-01-23 00:00:00
Tasks: Diversity

The objective of this paper is to define an effective strategy for building
an ensemble of Genetic Programming (GP) models. Ensemble methods are widely
used in machine learning due to their features: they average out biases, they
reduce the variance and they usually generalize better than single models.
Despite these advantages, building ensemble of GP models is not a
well-developed topic in the evolutionary computation community. To fill this
gap, we propose a strategy that blends individuals produced by standard
syntax-based GP and individuals produced by geometric semantic genetic
programming, one of the newest semantics-based method developed in GP. In fact,
recent literature showed that combining syntax and semantics could improve the
generalization ability of a GP model. Additionally, to improve the diversity of
the GP models used to build up the ensemble, we propose different pruning
criteria that are based on correlation and entropy, a commonly used measure in
information theory. Experimental results,obtained over different complex
problems, suggest that the pruning criteria based on correlation and entropy
could be effective in improving the generalization ability of the ensemble
model and in reducing the computational burden required to build it.
Expectation Learning for Adaptive Crossmodal Stimuli Association
Authors: Pablo Barros, German I. Parisi, Di Fu, Xun Liu, Stefan Wermter
Date: 2018-01-23 00:00:00

The human brain is able to learn, generalize, and predict crossmodal stimuli.
Learning by expectation fine-tunes crossmodal processing at different levels,
thus enhancing our power of generalization and adaptation in highly dynamic
environments. In this paper, we propose a deep neural architecture trained by
using expectation learning accounting for unsupervised learning tasks. Our
learning model exhibits a self-adaptable behavior, setting the first steps
towards the development of deep learning architectures for crossmodal stimuli
association.
CaosDB - Research Data Management for Complex, Changing, and Automated Research Workflows
Authors: Timm Fitschen, Alexander Schlemmer, Daniel Hornung, Henrik tom Wörden, Ulrich Parlitz, Stefan Luther
Date: 2018-01-23 00:00:00
Tasks: Management

Here we present CaosDB, a Research Data Management System (RDMS) designed to
ensure seamless integration of inhomogeneous data sources and repositories of
legacy data. Its primary purpose is the management of data from biomedical
sciences, both from simulations and experiments during the complete research
data lifecycle. An RDMS for this domain faces particular challenges: Research
data arise in huge amounts, from a wide variety of sources, and traverse a
highly branched path of further processing. To be accepted by its users, an
RDMS must be built around workflows of the scientists and practices and thus
support changes in workflow and data structure. Nevertheless it should
encourage and support the development and observation of standards and
furthermore facilitate the automation of data acquisition and processing with
specialized software. The storage data model of an RDMS must reflect these
complexities with appropriate semantics and ontologies while offering simple
methods for finding, retrieving, and understanding relevant data. We show how
CaosDB responds to these challenges and give an overview of the CaosDB Server,
its data model and its easy-to-learn CaosDB Query Language. We briefly discuss
the status of the implementation, how we currently use CaosDB, and how we plan
to use and extend it.
Dynamic Optimization of Neural Network Structures Using Probabilistic Modeling
Authors: Shinichi Shirakawa, Yasushi Iwata, Youhei Akimoto
Date: 2018-01-23 00:00:00

Deep neural networks (DNNs) are powerful machine learning models and have
succeeded in various artificial intelligence tasks. Although various
architectures and modules for the DNNs have been proposed, selecting and
designing the appropriate network structure for a target problem is a
challenging task. In this paper, we propose a method to simultaneously optimize
the network structure and weight parameters during neural network training. We
consider a probability distribution that generates network structures, and
optimize the parameters of the distribution instead of directly optimizing the
network structure. The proposed method can apply to the various network
structure optimization problems under the same framework. We apply the proposed
method to several structure optimization problems such as selection of layers,
selection of unit types, and selection of connections using the MNIST,
CIFAR-10, and CIFAR-100 datasets. The experimental results show that the
proposed method can find the appropriate and competitive network structures.
Clustering with Deep Learning: Taxonomy and New Methods
Authors: Elie Aljalbout, Vladimir Golkov, Yawar Siddiqui, Maximilian Strobel, Daniel Cremers
Date: 2018-01-23 00:00:00
Tasks: Clustering, Deep Learning

Clustering methods based on deep neural networks have proven promising for
clustering real-world data because of their high representational power. In
this paper, we propose a systematic taxonomy of clustering methods that utilize
deep neural networks. We base our taxonomy on a comprehensive review of recent
work and validate the taxonomy in a case study. In this case study, we show
that the taxonomy enables researchers and practitioners to systematically
create new clustering methods by selectively recombining and replacing distinct
aspects of previous methods with the goal of overcoming their individual
limitations. The experimental evaluation confirms this and shows that the
method created for the case study achieves state-of-the-art clustering quality
and surpasses it in some cases.
DeepGestalt - Identifying Rare Genetic Syndromes Using Deep Learning
Authors: Yaron Gurovich, Yair Hanani, Omri Bar, Nicole Fleischer, Dekel Gelbman, Lina Basel-Salmon, Peter Krawitz, Susanne B Kamphausen, Martin Zenker, Lynne M. Bird, Karen W. Gripp
Date: 2018-01-23 00:00:00
Tasks: Deep Learning, Medical Genetics

Facial analysis technologies have recently measured up to the capabilities of
expert clinicians in syndrome identification. To date, these technologies could
only identify phenotypes of a few diseases, limiting their role in clinical
settings where hundreds of diagnoses must be considered.
  We developed a facial analysis framework, DeepGestalt, using computer vision
and deep learning algorithms, that quantifies similarities to hundreds of
genetic syndromes based on unconstrained 2D images. DeepGestalt is currently
trained with over 26,000 patient cases from a rapidly growing
phenotype-genotype database, consisting of tens of thousands of validated
clinical cases, curated through a community-driven platform. DeepGestalt
currently achieves 91% top-10-accuracy in identifying over 215 different
genetic syndromes and has outperformed clinical experts in three separate
experiments.
  We suggest that this form of artificial intelligence is ready to support
medical genetics in clinical and laboratory practices and will play a key role
in the future of precision medicine.
Human Activity Recognition for Mobile Robot
Authors: Iyiola E. Olatunji
Date: 2018-01-23 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Due to the increasing number of mobile robots including domestic robots for
cleaning and maintenance in developed countries, human activity recognition is
inevitable for congruent human-robot interaction. Needless to say that this is
indeed a challenging task for robots, it is expedient to learn human activities
for autonomous mobile robots (AMR) for navigating in an uncontrolled
environment without any guidance. Building a correct classifier for complex
human action is non-trivial since simple actions can be combined to recognize a
complex human activity. In this paper, we trained a model for human activity
recognition using convolutional neural network. We trained and validated the
model using the Vicon physical action dataset and also tested the model on our
generated dataset (VMCUHK). Our experiment shows that our method performs with
high accuracy, human activity recognition task both on the Vicon physical
action dataset and VMCUHK dataset.