pwc-archive/papers-with-abstracts huggingface dataset:
Deep Network for Capacitive ECG Denoising
Authors: Vignesh Ravichandran, Balamurali Murugesan, Sharath M. Shankaranarayana, Keerthi Ram, Preejith S. P, Jayaraj Joseph, Mohanasankar Sivaprakasam
Date: 2019-03-29 00:00:00
Tasks: Denoising, ECG Denoising, Electrocardiography (ECG), Morphological Analysis

Continuous monitoring of cardiac health under free living condition is
crucial to provide effective care for patients undergoing post operative
recovery and individuals with high cardiac risk like the elderly. Capacitive
Electrocardiogram (cECG) is one such technology which allows comfortable and
long term monitoring through its ability to measure biopotential in conditions
without having skin contact. cECG monitoring can be done using many household
objects like chairs, beds and even car seats allowing for seamless monitoring
of individuals. This method is unfortunately highly susceptible to motion
artifacts which greatly limits its usage in clinical practice. The current use
of cECG systems has been limited to performing rhythmic analysis. In this paper
we propose a novel end-to-end deep learning architecture to perform the task of
denoising capacitive ECG. The proposed network is trained using motion
corrupted three channel cECG and a reference LEAD I ECG collected on
individuals while driving a car. Further, we also propose a novel joint loss
function to apply loss on both signal and frequency domain. We conduct
extensive rhythmic analysis on the model predictions and the ground truth. We
further evaluate the signal denoising using Mean Square Error(MSE) and Cross
Correlation between model predictions and ground truth. We report MSE of 0.167
and Cross Correlation of 0.476. The reported results highlight the feasibility
of performing morphological analysis using the filtered cECG. The proposed
approach can allow for continuous and comprehensive monitoring of the
individuals in free living conditions.
Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild
Authors: Kibok Lee, Kimin Lee, Jinwoo Shin, Honglak Lee
Date: 2019-03-29 00:00:00
Proceeding: ICCV 2019 10
Tasks: class-incremental learning, Class Incremental Learning, Incremental Learning, Lifelong learning

Lifelong learning with deep neural networks is well-known to suffer from catastrophic forgetting: the performance on previous tasks drastically degrades when learning a new task. To alleviate this effect, we propose to leverage a large stream of unlabeled data easily obtainable in the wild. In particular, we design a novel class-incremental learning scheme with (a) a new distillation loss, termed global distillation, (b) a learning strategy to avoid overfitting to the most recent task, and (c) a confidence-based sampling method to effectively leverage unlabeled external data. Our experimental results on various datasets, including CIFAR and ImageNet, demonstrate the superiority of the proposed methods over prior methods, particularly when a stream of unlabeled data is accessible: our method shows up to 15.8% higher accuracy and 46.5% less forgetting compared to the state-of-the-art method. The code is available at https://github.com/kibok90/iccv2019-inc.
Yet Another Accelerated SGD: ResNet-50 Training on ImageNet in 74.7 seconds
Authors: Masafumi Yamazaki, Akihiko Kasagi, Akihiro Tabuchi, Takumi Honda, Masahiro Miwa, Naoto Fukumoto, Tsuguchika Tabaru, Atsushi Ike, Kohta Nakashima
Date: 2019-03-29 00:00:00
Tasks: Deep Learning

There has been a strong demand for algorithms that can execute machine
learning as faster as possible and the speed of deep learning has accelerated
by 30 times only in the past two years. Distributed deep learning using the
large mini-batch is a key technology to address the demand and is a great
challenge as it is difficult to achieve high scalability on large clusters
without compromising accuracy. In this paper, we introduce optimization methods
which we applied to this challenge. We achieved the training time of 74.7
seconds using 2,048 GPUs on ABCI cluster applying these methods. The training
throughput is over 1.73 million images/sec and the top-1 validation accuracy is
75.08%.
Snore-GANs: Improving Automatic Snore Sound Classification with Synthesized Data
Authors: Zixing Zhang, Jing Han, Kun Qian, Christoph Janott, Yanan Guo, Bjoern Schuller
Date: 2019-03-29 00:00:00
Tasks: Classification, Data Augmentation, Diversity, General Classification, Sound Classification

One of the frontier issues that severely hamper the development of automatic
snore sound classification (ASSC) associates to the lack of sufficient
supervised training data. To cope with this problem, we propose a novel data
augmentation approach based on semi-supervised conditional Generative
Adversarial Networks (scGANs), which aims to automatically learn a mapping
strategy from a random noise space to original data distribution. The proposed
approach has the capability of well synthesizing 'realistic' high-dimensional
data, while requiring no additional annotation process. To handle the mode
collapse problem of GANs, we further introduce an ensemble strategy to enhance
the diversity of the generated data. The systematic experiments conducted on a
widely used Munich-Passau snore sound corpus demonstrate that the scGANs-based
systems can remarkably outperform other classic data augmentation systems, and
are also competitive to other recently reported systems for ASSC.
MuSE-ing on the Impact of Utterance Ordering On Crowdsourced Emotion Annotations
Authors: Mimansa Jaiswal, Zakaria Aldeneh, Cristian-Paul Bara, Yuanhang Luo, Mihai Burzo, Rada Mihalcea, Emily Mower Provost
Date: 2019-03-27 00:00:00
Tasks: Emotion Recognition

Emotion recognition algorithms rely on data annotated with high quality
labels. However, emotion expression and perception are inherently subjective.
There is generally not a single annotation that can be unambiguously declared
"correct". As a result, annotations are colored by the manner in which they
were collected. In this paper, we conduct crowdsourcing experiments to
investigate this impact on both the annotations themselves and on the
performance of these algorithms. We focus on one critical question: the effect
of context. We present a new emotion dataset, Multimodal Stressed Emotion
(MuSE), and annotate the dataset using two conditions: randomized, in which
annotators are presented with clips in random order, and contextualized, in
which annotators are presented with clips in order. We find that contextual
labeling schemes result in annotations that are more similar to a speaker's own
self-reported labels and that labels generated from randomized schemes are most
easily predictable by automated systems.
Neuromorphic In-Memory Computing Framework using Memtransistor Cross-bar based Support Vector Machines
Authors: P. Kumar, A. R. Nair, O. Chatterjee, T. Paul, A. Ghosh, S. Chakrabartty, C. S. Thakur
Date: 2019-03-29 00:00:00

This paper presents a novel framework for designing support vector machines (SVMs), which does not impose restriction on the SVM kernel to be positive-definite and allows the user to define memory constraint in terms of fixed template vectors. This makes the framework scalable and enables its implementation for low-power, high-density and memory constrained embedded application. An efficient hardware implementation of the same is also discussed, which utilizes novel low power memtransistor based cross-bar architecture, and is robust to device mismatch and randomness. We used memtransistor measurement data, and showed that the designed SVMs can achieve classification accuracy comparable to traditional SVMs on both synthetic and real-world benchmark datasets. This framework would be beneficial for design of SVM based wake-up systems for internet of things (IoTs) and edge devices where memtransistors can be used to optimize system's energy-efficiency and perform in-memory matrix-vector multiplication (MVM).
A Machine Learning Framework for Biometric Authentication using Electrocardiogram
Authors: Song-Kyoo Kim, Chan Yeob Yeun, Ernesto Damiani, Nai-Wei Lo
Date: 2019-03-29 00:00:00
Tasks: BIG-bench Machine Learning

This paper introduces a framework for how to appropriately adopt and adjust Machine Learning (ML) techniques used to construct Electrocardiogram (ECG) based biometric authentication schemes. The proposed framework can help investigators and developers on ECG based biometric authentication mechanisms define the boundaries of required datasets and get training data with good quality. To determine the boundaries of datasets, use case analysis is adopted. Based on various application scenarios on ECG based authentication, three distinct use cases (or authentication categories) are developed. With more qualified training data given to corresponding machine learning schemes, the precision on ML-based ECG biometric authentication mechanisms is increased in consequence. ECG time slicing technique with the R-peak anchoring is utilized in this framework to acquire ML training data with good quality. In the proposed framework four new measure metrics are introduced to evaluate the quality of ML training and testing data. In addition, a Matlab toolbox, containing all proposed mechanisms, metrics and sample data with demonstrations using various ML techniques, is developed and made publicly available for further investigation. For developing ML-based ECG biometric authentication, the proposed framework can guide researchers to prepare the proper ML setups and the ML training datasets along with three identified user case scenarios. For researchers adopting ML techniques to design new schemes in other research domains, the proposed framework is still useful for generating ML-based training and testing datasets with good quality and utilizing new measure metrics.
Using Structured Input and Modularity for Improved Learning
Authors: Zehra Sura, Tong Chen, Hyojin Sung
Date: 2019-03-29 00:00:00

We describe a method for utilizing the known structure of input data to make
learning more efficient. Our work is in the domain of programming languages,
and we use deep neural networks to do program analysis. Computer programs
include a lot of structural information (such as loop nests, conditional
blocks, and data scopes), which is pertinent to program analysis. In this case,
the neural network has to learn to recognize the structure, and also learn the
target function for the problem. However, the structural information in this
domain is readily accessible to software with the availability of compiler
tools and parsers for well-defined programming languages.
  Our method for utilizing the known structure of input data includes: (1)
pre-processing the input data to expose relevant structures, and (2)
constructing neural networks by incorporating the structure of the input data
as an integral part of the network design. The method has the effect of
modularizing the neural network which helps break down complexity, and results
in more efficient training of the overall network. We apply this method to an
example code analysis problem, and show that it can achieve higher accuracy
with a smaller network size and fewer training examples. Further, the method is
robust, performing equally well on input data with different distributions.
Attention-Augmented End-to-End Multi-Task Learning for Emotion Prediction from Speech
Authors: Zixing Zhang, Bingwen Wu, Bjoern Schuller
Date: 2019-03-29 00:00:00
Tasks: Emotion Recognition, Multi-Task Learning, Speech Emotion Recognition

Despite the increasing research interest in end-to-end learning systems for
speech emotion recognition, conventional systems either suffer from the
overfitting due in part to the limited training data, or do not explicitly
consider the different contributions of automatically learnt representations
for a specific task. In this contribution, we propose a novel end-to-end
framework which is enhanced by learning other auxiliary tasks and an attention
mechanism. That is, we jointly train an end-to-end network with several
different but related emotion prediction tasks, i.e., arousal, valence, and
dominance predictions, to extract more robust representations shared among
various tasks than traditional systems with the hope that it is able to relieve
the overfitting problem. Meanwhile, an attention layer is implemented on top of
the layers for each task, with the aim to capture the contribution distribution
of different segment parts for each individual task. To evaluate the
effectiveness of the proposed system, we conducted a set of experiments on the
widely used database IEMOCAP. The empirical results show that the proposed
systems significantly outperform corresponding baseline systems.
Crowd Sourced Data Analysis: Mapping of Programming Concepts to Syntactical Patterns
Authors: Deepak Thukral, Darvesh Punia
Date: 2019-03-28 00:00:00
Tasks: Code Search, TAG

Since programming concepts do not match their syntactic representations, code
search is a very tedious task. For instance in Java or C, array doesn't match
[], so using "array" as a query, one cannot find what they are looking for.
Often developers have to search code whether to understand any code, or to
reuse some part of that code, or just to read it, without natural language
searching, developers have to often scroll back and forth or use variable names
as their queries. In our work, we have used Stackoverflow (SO) question and
answers to make a mapping of programming concepts with their respective natural
language keywords, and then tag these natural language terms to every line of
code, which can further we used in searching using natural language keywords.
Evaluating Built-in ECC of FPGA on-chip Memories for the Mitigation of Undervolting Faults
Authors: Behzad Salami, Osman S. Unsal, Adrian Cristal Kestelman
Date: 2019-03-29 00:00:00

Voltage underscaling below the nominal level is an effective solution for
improving energy efficiency in digital circuits, e.g., Field Programmable Gate
Arrays (FPGAs). However, further undervolting below a safe voltage level and
without accompanying frequency scaling leads to timing related faults,
potentially undermining the energy savings. Through experimental voltage
underscaling studies on commercial FPGAs, we observed that the rate of these
faults exponentially increases for on-chip memories, or Block RAMs (BRAMs). To
mitigate these faults, we evaluated the efficiency of the built-in
Error-Correction Code (ECC) and observed that more than 90% of the faults are
correctable and further 7% are detectable (but not correctable). This
efficiency is the result of the single-bit type of these faults, which are then
effectively covered by the Single-Error Correction and Double-Error Detection
(SECDED) design of the built-in ECC. Finally, motivated by the above
experimental observations, we evaluated an FPGA-based Neural Network (NN)
accelerator under low-voltage operations, while built-in ECC is leveraged to
mitigate undervolting faults and thus, prevent NN significant accuracy loss. In
consequence, we achieve 40% of the BRAM power saving through undervolting below
the minimum safe voltage level, with a negligible NN accuracy loss, thanks to
the substantial fault coverage by the built-in ECC.
Robust Data Detection for MIMO Systems with One-Bit ADCs: A Reinforcement Learning Approach
Authors: Yo-Seb Jeon, Namyoon Lee, H. Vincent Poor
Date: 2019-03-29 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The use of one-bit analog-to-digital converters (ADCs) at a receiver is a
power-efficient solution for future wireless systems operating with a large
signal bandwidth and/or a massive number of receive radio frequency chains.
This solution, however, induces a high channel estimation error and therefore
makes it difficult to perform the optimal data detection that requires perfect
knowledge of likelihood functions at the receiver. In this paper, we propose a
likelihood function learning method for multiple-input multiple-output (MIMO)
systems with one-bit ADCs using a reinforcement learning approach. The key idea
is to exploit input-output samples obtained from data detection, to compensate
the mismatch in the likelihood function. The underlying difficulty of this idea
is a label uncertainty in the samples caused by a data detection error. To
resolve this problem, we define a Markov decision process (MDP) to maximize the
accuracy of the likelihood function learned from the samples. We then develop a
reinforcement learning algorithm that efficiently finds the optimal policy by
approximating the transition function and the optimal state of the MDP.
Simulation results demonstrate that the proposed method provides significant
performance gains for the optimal data detection methods that suffer from the
mismatch in the likelihood function.
Invariance-Preserving Localized Activation Functions for Graph Neural Networks
Authors: Luana Ruiz, Fernando Gama, Antonio G. Marques, Alejandro Ribeiro
Date: 2019-03-29 00:00:00
Tasks: Authorship Attribution, Recommendation Systems

Graph signals are signals with an irregular structure that can be described by a graph. Graph neural networks (GNNs) are information processing architectures tailored to these graph signals and made of stacked layers that compose graph convolutional filters with nonlinear activation functions. Graph convolutions endow GNNs with invariance to permutations of the graph nodes' labels. In this paper, we consider the design of trainable nonlinear activation functions that take into consideration the structure of the graph. This is accomplished by using graph median filters and graph max filters, which mimic linear graph convolutions and are shown to retain the permutation invariance of GNNs. We also discuss modifications to the backpropagation algorithm necessary to train local activation functions. The advantages of localized activation function architectures are demonstrated in four numerical experiments: source localization on synthetic graphs, authorship attribution of 19th century novels, movie recommender systems and scientific article classification. In all cases, localized activation functions are shown to improve model capacity.
Deep Convolutional Spiking Neural Networks for Image Classification
Authors: Ruthvik Vaila, John Chiasson, Vishal Saxena
Date: 2019-03-28 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

Spiking neural networks are biologically plausible counterparts of the artificial neural networks, artificial neural networks are usually trained with stochastic gradient descent and spiking neural networks are trained with spike timing dependant plasticity. Training deep convolutional neural networks is a memory and power intensive job. Spiking networks could potentially help in reducing the power usage. There is a large pool of tools for one to chose to train artificial neural networks of any size, on the other hand all the available tools to simulate spiking neural networks are geared towards computational neuroscience applications and they are not suitable for real life applications. In this work we focus on implementing a spiking CNN using Tensorflow to examine behaviour of the network and empirically study the effect of various parameters on learning capabilities and also study catastrophic forgetting in the spiking CNN and weight initialization problem in R-STDP using MNIST and N-MNIST data sets.
An Upper Bound for Minimum True Matches in Graph Isomorphism with Simulated Annealing
Authors: Hashem Ezzati, Mahmood Amintoosi, Hashem Tabasi
Date: 2019-03-29 00:00:00
Tasks: Combinatorial Optimization, Graph Matching, Stochastic Optimization

Graph matching is one of the most important problems in graph theory and
combinatorial optimization, with many applications in various domains. Although
meta-heuristic algorithms have had good performance on many NP-Hard and
NP-Complete problems, for this problem there are not reported superior
solutions by these algorithms. The reason of this inefficiency has not been
investigated yet. In this paper we show that simulated annealing as an
stochastic optimization method is unlikely to be even close to the optimal
solution for this problem. In addition to theoretical discussion, the
experimental results also verified our idea; for example, in two sample graphs,
the probability of reaching to a solution with more than three correct matches
is about $0.02$ in simulated annealing.
Counting with Focus for Free
Authors: Zenglin Shi, Pascal Mettes, Cees G. M. Snoek
Date: 2019-03-28 00:00:00
Proceeding: ICCV 2019 10
Tasks: Density Estimation

This paper aims to count arbitrary objects in images. The leading counting approaches start from point annotations per object from which they construct density maps. Then, their training objective transforms input images to density maps through deep convolutional networks. We posit that the point annotations serve more supervision purposes than just constructing density maps. We introduce ways to repurpose the points for free. First, we propose supervised focus from segmentation, where points are converted into binary maps. The binary maps are combined with a network branch and accompanying loss function to focus on areas of interest. Second, we propose supervised focus from global density, where the ratio of point annotations to image pixels is used in another branch to regularize the overall density estimation. To assist both the density estimation and the focus from segmentation, we also introduce an improved kernel size estimator for the point annotations. Experiments on six datasets show that all our contributions reduce the counting error, regardless of the base network, resulting in state-of-the-art accuracy using only a single network. Finally, we are the first to count on WIDER FACE, allowing us to show the benefits of our approach in handling varying object scales and crowding levels. Code is available at https://github.com/shizenglin/Counting-with-Focus-for-Free
All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation
Authors: Wei-Lun Chang, Hui-Po Wang, Wen-Hsiao Peng, Wei-Chen Chiu
Date: 2019-03-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: All, Domain Adaptation, Image-to-Image Translation, Segmentation, Semantic Segmentation, Synthetic-to-Real Translation, Translation, Unsupervised Domain Adaptation

In this paper we tackle the problem of unsupervised domain adaptation for the
task of semantic segmentation, where we attempt to transfer the knowledge
learned upon synthetic datasets with ground-truth labels to real-world images
without any annotation. With the hypothesis that the structural content of
images is the most informative and decisive factor to semantic segmentation and
can be readily shared across domains, we propose a Domain Invariant Structure
Extraction (DISE) framework to disentangle images into domain-invariant
structure and domain-specific texture representations, which can further
realize image-translation across domains and enable label transfer to improve
segmentation performance. Extensive experiments verify the effectiveness of our
proposed DISE model and demonstrate its superiority over several
state-of-the-art approaches.