pwc-archive/papers-with-abstracts huggingface dataset:
An efficient supervised dictionary learning method for audio signal recognition
Authors: Imad Rida, Romain Hérault, Gilles Gasso
Date: 2018-12-12 00:00:00
Tasks: Audio Signal Recognition, Chord Recognition, Dictionary Learning, Representation Learning, Scene Recognition

Machine hearing or listening represents an emerging area. Conventional
approaches rely on the design of handcrafted features specialized to a specific
audio task and that can hardly generalized to other audio fields. For example,
Mel-Frequency Cepstral Coefficients (MFCCs) and its variants were successfully
applied to computational auditory scene recognition while Chroma vectors are
good at music chord recognition. Unfortunately, these predefined features may
be of variable discrimination power while extended to other tasks or even
within the same task due to different nature of clips. Motivated by this need
of a principled framework across domain applications for machine listening, we
propose a generic and data-driven representation learning approach. For this
sake, a novel and efficient supervised dictionary learning method is presented.
The method learns dissimilar dictionaries, one per each class, in order to
extract heterogeneous information for classification. In other words, we are
seeking to minimize the intra-class homogeneity and maximize class
separability. This is made possible by promoting pairwise orthogonality between
class specific dictionaries and controlling the sparsity structure of the audio
clip's decomposition over these dictionaries. The resulting optimization
problem is non-convex and solved using a proximal gradient descent method.
Experiments are performed on both computational auditory scene (East Anglia and
Rouen) and synthetic music chord recognition datasets. Obtained results show
that our method is capable to reach state-of-the-art hand-crafted features for
both applications.
Generative Adversarial Networks for Recovering Missing Spectral Information
Authors: Dung N. Tran, Trac. D. Tran, Lam Nguyen
Date: 2018-12-11 00:00:00
Tasks: Generative Adversarial Network

Ultra-wideband (UWB) radar systems nowadays typical operate in the low
frequency spectrum to achieve penetration capability. However, this spectrum is
also shared by many others communication systems, which causes missing
information in the frequency bands. To recover this missing spectral
information, we propose a generative adversarial network, called SARGAN, that
learns the relationship between original and missing band signals by observing
these training pairs in a clever way. Initial results shows that this approach
is promising in tackling this challenging missing band problem.
The Jiminy Advisor: Moral Agreements Among Stakeholders Based on Norms and Argumentation
Authors: Beishui Liao, Pere Pardo, Marija Slavkovik, Leendert van der Torre
Date: 2018-12-11 00:00:00
Tasks: Decision Making

An autonomous system is constructed by a manufacturer, operates in a society subject to norms and laws, and interacts with end users. All of these actors are stakeholders affected by the behavior of the autonomous system. We address the challenge of how the ethical views of such stakeholders can be integrated in the behavior of an autonomous system. We propose an ethical recommendation component called Jiminy which uses techniques from normative systems and formal argumentation to reach moral agreements among stakeholders. A Jiminy represents the ethical views of each stakeholder by using normative systems, and has three ways of resolving moral dilemmas that involve the opinions of the stakeholders. First, the Jiminy considers how the arguments of the stakeholders relate to one another, which may already resolve the dilemma. Secondly, the Jiminy combines the normative systems of the stakeholders such that the combined expertise of the stakeholders may resolve the dilemma. Thirdly, and only if these two other methods have failed, the Jiminy uses context-sensitive rules to decide which of the stakeholders take preference over the others. At the abstract level, these three methods are characterized by adding arguments, adding attacks between arguments, and revising attacks between arguments. We show how a Jiminy can be used not only for ethical reasoning and collaborative decision-making, but also to provide explanations about ethical behavior.
Non-local Operational Anisotropic Diffusion Filter
Authors: Fábio A. M. Cappabianco, Petrus P. C. E. da Silva
Date: 2018-12-11 00:00:00

High-frequency noise is present in several modalities of medical images. It
originates from the acquisition process and may be related to the scanner
configurations, the scanned body, or to other external factors. This way,
prospective filters are an important tool to improve the image quality. In this
paper, we propose a non-local weighted operational anisotropic diffusion filter
and evaluate its effect on magnetic resonance images and on kV/CBCT
radiotherapy images. We also provide a detailed analysis of non-local parameter
settings. Results show that the new filter enhances previous local
implementations and has potential application in radiotherapy treatments.
Rotation Invariant Descriptors for Galaxy Morphological Classification
Authors: Hubert Cecotti
Date: 2018-12-11 00:00:00
Tasks: Binary Classification, Classification, General Classification, Image Retrieval, Retrieval

The detection of objects that are multi-oriented is a difficult pattern recognition problem. In this paper, we propose to evaluate the performance of different families of descriptors for the classification of galaxy morphologies. We investigate the performance of the Hu moments, Flusser moments, Zernike moments, Fourier-Mellin moments, and ring projection techniques based on 1D moment and the Fourier transform. We consider two main datasets for the performance evaluation. The first dataset is an artificial dataset based on representative templates from 11 types of galaxies, which are evaluated with different transformations (noise, smoothing), alone or combined. The evaluation is based on image retrieval performance to estimate the robustness of the rotation invariant descriptors with this type of images. The second dataset is composed of real images extracted from the Galaxy Zoo 2 project. The binary classification of elliptical and spiral galaxies is achieved with pre-processing steps including morphological filtering and a Laplacian pyramid. For the binary classification, we compare the different set of features with Support Vector Machines (SVM), Extreme Learning Machine, and different types of linear discriminant analysis techniques. The results support the conclusion that the proposed framework for the binary classification of elliptical and spiral galaxies provides an area under the ROC curve reaching 99.54%, proving the robustness of the approach for helping astronomers to study galaxies.
Predictive Learning on Hidden Tree-Structured Ising Models
Authors: Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate
Date: 2018-12-11 00:00:00

We provide high-probability sample complexity guarantees for exact structure recovery and accurate predictive learning using noise-corrupted samples from an acyclic (tree-shaped) graphical model. The hidden variables follow a tree-structured Ising model distribution, whereas the observable variables are generated by a binary symmetric channel taking the hidden variables as its input (flipping each bit independently with some constant probability $q\in [0,1/2)$). In the absence of noise, predictive learning on Ising models was recently studied by Bresler and Karzand (2020); this paper quantifies how noise in the hidden model impacts the tasks of structure recovery and marginal distribution estimation by proving upper and lower bounds on the sample complexity. Our results generalize state-of-the-art bounds reported in prior work, and they exactly recover the noiseless case ($q=0$). In fact, for any tree with $p$ vertices and probability of incorrect recovery $\delta>0$, the sufficient number of samples remains logarithmic as in the noiseless case, i.e., $\mathcal{O}(\log(p/\delta))$, while the dependence on $q$ is $\mathcal{O}\big( 1/(1-2q)^{4} \big)$, for both aforementioned tasks. We also present a new equivalent of Isserlis' Theorem for sign-valued tree-structured distributions, yielding a new low-complexity algorithm for higher-order moment estimation.
Anomaly Generation using Generative Adversarial Networks in Host Based Intrusion Detection
Authors: Milad Salem, Shayan Taheri, Jiann Shiun Yuan
Date: 2018-12-11 00:00:00
Tasks: Anomaly Detection, Intrusion Detection

Generative adversarial networks have been able to generate striking results
in various domains. This generation capability can be general while the
networks gain deep understanding regarding the data distribution. In many
domains, this data distribution consists of anomalies and normal data, with the
anomalies commonly occurring relatively less, creating datasets that are
imbalanced. The capabilities that generative adversarial networks offer can be
leveraged to examine these anomalies and help alleviate the challenge that
imbalanced datasets propose via creating synthetic anomalies. This anomaly
generation can be specifically beneficial in domains that have costly data
creation processes as well as inherently imbalanced datasets. One of the
domains that fits this description is the host-based intrusion detection
domain. In this work, ADFA-LD dataset is chosen as the dataset of interest
containing system calls of small foot-print next generation attacks. The data
is first converted into images, and then a Cycle-GAN is used to create images
of anomalous data from images of normal data. The generated data is combined
with the original dataset and is used to train a model to detect anomalies. By
doing so, it is shown that the classification results are improved, with the
AUC rising from 0.55 to 0.71, and the anomaly detection rate rising from 17.07%
to 80.49%. The results are also compared to SMOTE, showing the potential
presented by generative adversarial networks in anomaly generation.
ECG Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features
Authors: Milad Salem, Shayan Taheri, Jiann Shiun-Yuan
Date: 2018-12-11 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Transfer Learning

Due to the recent advances in the area of deep learning, it has been
demonstrated that a deep neural network, trained on a huge amount of data, can
recognize cardiac arrhythmias better than cardiologists. Moreover,
traditionally feature extraction was considered an integral part of ECG pattern
recognition; however, recent findings have shown that deep neural networks can
carry out the task of feature extraction directly from the data itself. In
order to use deep neural networks for their accuracy and feature extraction,
high volume of training data is required, which in the case of independent
studies is not pragmatic. To arise to this challenge, in this work, the
identification and classification of four ECG patterns are studied from a
transfer learning perspective, transferring knowledge learned from the image
classification domain to the ECG signal classification domain. It is
demonstrated that feature maps learned in a deep neural network trained on
great amounts of generic input images can be used as general descriptors for
the ECG signal spectrograms and result in features that enable classification
of arrhythmias. Overall, an accuracy of 97.23 percent is achieved in
classifying near 7000 instances by ten-fold cross validation.
Learning representations of molecules and materials with atomistic neural networks
Authors: Kristof T. Schütt, Alexandre Tkatchenko, Klaus-Robert Müller
Date: 2018-12-11 00:00:00

Deep Learning has been shown to learn efficient representations for
structured data such as image, text or audio. In this chapter, we present
neural network architectures that are able to learn efficient representations
of molecules and materials. In particular, the continuous-filter convolutional
network SchNet accurately predicts chemical properties across compositional and
configurational space on a variety of datasets. Beyond that, we analyze the
obtained representations to find evidence that their spatial and chemical
properties agree with chemical intuition.
Femural Head Autosegmentation for 3D Radiotherapy Planning: Preliminary Results
Authors: Bruno A. G. da Silva, Alvaro L. Fazenda, Fabiano C. Paixao
Date: 2018-12-11 00:00:00

Contouring of organs at risk is an important but time consuming part of
radiotherapy treatment planning. Several authors proposed methods for automatic
delineation but the clinical experts eye remains the gold standard method. In
this paper, we present a totally visual software for automated delineation of
the femural head. The software was successfully characterized in pelvic CT Scan
of prostate patients (n=11). The automatic delineation was compared with manual
and approved delineation through blind test evaluated by a panel of seniors
radiation oncologists (n=9). Clinical experts evaluated that no any contouring
correction were need in 77.8% and 67.8% of manual and automatic delineation
respectively. Our results show that the software is robust, the automated
delineation was reproducible in all patient, and its performance was similar to
manually delineation.
Contrastive Training for Models of Information Cascades
Authors: Shaobin Xu, David A. Smith
Date: 2018-12-11 00:00:00

This paper proposes a model of information cascades as directed spanning
trees (DSTs) over observed documents. In addition, we propose a contrastive
training procedure that exploits partial temporal ordering of node infections
in lieu of labeled training links. This combination of model and unsupervised
training makes it possible to improve on models that use infection times alone
and to exploit arbitrary features of the nodes and of the text content of
messages in information cascades. With only basic node and time lag features
similar to previous models, the DST model achieves performance with
unsupervised training comparable to strong baselines on a blog network
inference task. Unsupervised training with additional content features achieves
significantly better results, reaching half the accuracy of a fully supervised
model.
Deep Semantic Segmentation in an AUV for Online Posidonia Oceanica Meadows identification
Authors: Miguel Martin-Abadal, Eric Guerrero-Font, Francisco Bonin-Font, Yolanda Gonzalez-Cid
Date: 2018-06-22 00:00:00
Tasks: Segmentation, Semantic Segmentation

Recent studies have shown evidence of a significant decline of the Posidonia
oceanica (P.O.) meadows on a global scale. The monitoring and mapping of these
meadows are fundamental tools for measuring their status. We present an
approach based on a deep neural network to automatically perform a
high-precision semantic segmentation of P.O. meadows in sea-floor images,
offering several improvements over the state of the art techniques. Our network
demonstrates outstanding performance over diverse test sets, reaching a
precision of 96.57% and an accuracy of 96.81%, surpassing the reliability of
labelling the images manually. Also, the network is implemented in an
Autonomous Underwater Vehicle (AUV), performing an online P.O. segmentation,
which will be used to generate real-time semantic coverage maps.
Unsupervised domain-agnostic identification of product names in social media posts
Authors: Nicolai Pogrebnyakov
Date: 2018-12-11 00:00:00
Tasks: Clustering, Part-Of-Speech Tagging, Word Embeddings

Product name recognition is a significant practical problem, spurred by the
greater availability of platforms for discussing products such as social media
and product review functionalities of online marketplaces. Customers, product
manufacturers and online marketplaces may want to identify product names in
unstructured text to extract important insights, such as sentiment, surrounding
a product. Much extant research on product name identification has been
domain-specific (e.g., identifying mobile phone models) and used supervised or
semi-supervised methods. With massive numbers of new products released to the
market every year such methods may require retraining on updated labeled data
to stay relevant, and may transfer poorly across domains. This research
addresses this challenge and develops a domain-agnostic, unsupervised algorithm
for identifying product names based on Facebook posts. The algorithm consists
of two general steps: (a) candidate product name identification using an
off-the-shelf pretrained conditional random fields (CRF) model, part-of-speech
tagging and a set of simple patterns; and (b) filtering of candidate names to
remove spurious entries using clustering and word embeddings generated from the
data.
Evaluating the Impact of Intensity Normalization on MR Image Synthesis
Authors: Jacob C. Reinhold, Blake E. Dewey, Aaron Carass, Jerry L. Prince
Date: 2018-12-11 00:00:00
Tasks: Image Generation, Imputation, Medical Image Analysis

Image synthesis learns a transformation from the intensity features of an
input image to yield a different tissue contrast of the output image. This
process has been shown to have application in many medical image analysis tasks
including imputation, registration, and segmentation. To carry out synthesis,
the intensities of the input images are typically scaled--i.e.,
normalized--both in training to learn the transformation and in testing when
applying the transformation, but it is not presently known what type of input
scaling is optimal. In this paper, we consider seven different intensity
normalization algorithms and three different synthesis methods to evaluate the
impact of normalization. Our experiments demonstrate that intensity
normalization as a preprocessing step improves the synthesis results across all
investigated synthesis algorithms. Furthermore, we show evidence that suggests
intensity normalization is vital for successful deep learning-based MR image
synthesis.
Reproduction Report on "Learn to Pay Attention"
Authors: Levan Shugliashvili, Davit Soselia, Shota Amashukeli, Irakli Koberidze
Date: 2018-12-11 00:00:00
Tasks: General Classification, image-classification, Image Classification

We have successfully implemented the "Learn to Pay Attention" model of
attention mechanism in convolutional neural networks, and have replicated the
results of the original paper in the categories of image classification and
fine-grained recognition.
Scalable language model adaptation for spoken dialogue systems
Authors: Ankur Gandhe, Ariya Rastrow, Bjorn Hoffmeister
Date: 2018-12-11 00:00:00
Tasks: Language Modeling, Language Modelling, model, speech-recognition, Speech Recognition, Spoken Dialogue Systems

Language models (LM) for interactive speech recognition systems are trained
on large amounts of data and the model parameters are optimized on past user
data. New application intents and interaction types are released for these
systems over time, imposing challenges to adapt the LMs since the existing
training data is no longer sufficient to model the future user interactions. It
is unclear how to adapt LMs to new application intents without degrading the
performance on existing applications. In this paper, we propose a solution to
(a) estimate n-gram counts directly from the hand-written grammar for training
LMs and (b) use constrained optimization to optimize the system parameters for
future use cases, while not degrading the performance on past usage. We
evaluated our approach on new applications intents for a personal assistant
system and find that the adaptation improves the word error rate by up to 15%
on new applications even when there is no adaptation data available for an
application.
On the Curved Geometry of Accelerated Optimization
Authors: Aaron Defazio
Date: 2018-12-11 00:00:00
Proceeding: NeurIPS 2019 12

In this work we propose a differential geometric motivation for Nesterov's accelerated gradient method (AGM) for strongly-convex problems. By considering the optimization procedure as occurring on a Riemannian manifold with a natural structure, The AGM method can be seen as the proximal point method applied in this curved space. This viewpoint can also be extended to the continuous time case, where the accelerated gradient method arises from the natural block-implicit Euler discretization of an ODE on the manifold. We provide an analysis of the convergence rate of this ODE for quadratic objectives.
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in Video
Authors: Radu Tudor Ionescu, Fahad Shahbaz Khan, Mariana-Iuliana Georgescu, Ling Shao
Date: 2018-12-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Abnormal Event Detection In Video, Anomaly Detection, Binary Classification, Clustering, Event Detection, General Classification, Outlier Detection

Abnormal event detection in video is a challenging vision problem. Most
existing approaches formulate abnormal event detection as an outlier detection
task, due to the scarcity of anomalous data during training. Because of the
lack of prior information regarding abnormal events, these methods are not
fully-equipped to differentiate between normal and abnormal events. In this
work, we formalize abnormal event detection as a one-versus-rest binary
classification problem. Our contribution is two-fold. First, we introduce an
unsupervised feature learning framework based on object-centric convolutional
auto-encoders to encode both motion and appearance information. Second, we
propose a supervised classification approach based on clustering the training
samples into normality clusters. A one-versus-rest abnormal event classifier is
then employed to separate each normality cluster from the rest. For the purpose
of training the classifier, the other clusters act as dummy anomalies. During
inference, an object is labeled as abnormal if the highest classification score
assigned by the one-versus-rest classifiers is negative. Comprehensive
experiments are performed on four benchmarks: Avenue, ShanghaiTech, UCSD and
UMN. Our approach provides superior results on all four data sets. On the
large-scale ShanghaiTech data set, our method provides an absolute gain of 8.4%
in terms of frame-level AUC compared to the state-of-the-art method [Sultani et
al., CVPR 2018].
Prior-Knowledge and Attention-based Meta-Learning for Few-Shot Learning
Authors: Yunxiao Qin, WeiGuo Zhang, Chenxu Zhao, Zezheng Wang, Xiangyu Zhu, Guo-Jun Qi, Jingping Shi, Zhen Lei
Date: 2018-12-11 00:00:00
Tasks: Few-Shot Learning, Meta-Learning

Recently, meta-learning has been shown as a promising way to solve few-shot learning. In this paper, inspired by the human cognition process which utilizes both prior-knowledge and vision attention in learning new knowledge, we present a novel paradigm of meta-learning approach with three developments to introduce attention mechanism and prior-knowledge for meta-learning. In our approach, prior-knowledge is responsible for helping meta-learner expressing the input data into high-level representation space, and attention mechanism enables meta-learner focusing on key features of the data in the representation space. Compared with existing meta-learning approaches that pay little attention to prior-knowledge and vision attention, our approach alleviates the meta-learner's few-shot cognition burden. Furthermore, a Task-Over-Fitting (TOF) problem, which indicates that the meta-learner has poor generalization on different K-shot learning tasks, is discovered and we propose a Cross-Entropy across Tasks (CET) metric to model and solve the TOF problem. Extensive experiments demonstrate that we improve the meta-learner with state-of-the-art performance on several few-shot learning benchmarks, and at the same time the TOF problem can also be released greatly.