pwc-archive/papers-with-abstracts huggingface dataset:
Using Latent Codes for Class Imbalance Problem in Unsupervised Domain Adaptation
Authors: Boris Chidlovskii
Date: 2019-09-17 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

We address the problem of severe class imbalance in unsupervised domain adaptation, when the class spaces in source and target domains diverge considerably. Till recently, domain adaptation methods assumed the aligned class spaces, such that reducing distribution divergence makes the transfer between domains easier. Such an alignment assumption is invalidated in real world scenarios where some source classes are often under-represented or simply absent in the target domain. We revise the current approaches to class imbalance and propose a new one that uses latent codes in the adversarial domain adaptation framework. We show how the latent codes can be used to disentangle the silent structure of the target domain and to identify under-represented classes. We show how to learn the latent code reconstruction jointly with the domain invariant representation and use them to accurately estimate the target labels.
AdaFair: Cumulative Fairness Adaptive Boosting
Authors: Vasileios Iosifidis, Eirini Ntoutsi
Date: 2019-09-17 00:00:00
Tasks: Attribute, Decision Making, Fairness

The widespread use of ML-based decision making in domains with high societal impact such as recidivism, job hiring and loan credit has raised a lot of concerns regarding potential discrimination. In particular, in certain cases it has been observed that ML algorithms can provide different decisions based on sensitive attributes such as gender or race and therefore can lead to discrimination. Although, several fairness-aware ML approaches have been proposed, their focus has been largely on preserving the overall classification accuracy while improving fairness in predictions for both protected and non-protected groups (defined based on the sensitive attribute(s)). The overall accuracy however is not a good indicator of performance in case of class imbalance, as it is biased towards the majority class. As we will see in our experiments, many of the fairness-related datasets suffer from class imbalance and therefore, tackling fairness requires also tackling the imbalance problem. To this end, we propose AdaFair, a fairness-aware classifier based on AdaBoost that further updates the weights of the instances in each boosting round taking into account a cumulative notion of fairness based upon all current ensemble members, while explicitly tackling class-imbalance by optimizing the number of ensemble members for balanced classification error. Our experiments show that our approach can achieve parity in true positive and true negative rates for both protected and non-protected groups, while it significantly outperforms existing fairness-aware methods up to 25% in terms of balanced error.
Instantiation-Net: 3D Mesh Reconstruction from Single 2D Image for Right Ventricle
Authors: Zhao-Yang Wang, Xiao-Yun Zhou, Peichao Li, Celia Riga, Guang-Zhong Yang
Date: 2019-09-16 00:00:00

3D shape instantiation which reconstructs the 3D shape of a target from limited 2D images or projections is an emerging technique for surgical intervention. It improves the currently less-informative and insufficient 2D navigation schemes for robot-assisted Minimally Invasive Surgery (MIS) to 3D navigation. Previously, a general and registration-free framework was proposed for 3D shape instantiation based on Kernel Partial Least Square Regression (KPLSR), requiring manually segmented anatomical structures as the pre-requisite. Two hyper-parameters including the Gaussian width and component number also need to be carefully adjusted. Deep Convolutional Neural Network (DCNN) based framework has also been proposed to reconstruct a 3D point cloud from a single 2D image, with end-to-end and fully automatic learning. In this paper, an Instantiation-Net is proposed to reconstruct the 3D mesh of a target from its a single 2D image, by using DCNN to extract features from the 2D image and Graph Convolutional Network (GCN) to reconstruct the 3D mesh, and using Fully Connected (FC) layers to connect the DCNN to GCN. Detailed validation was performed to demonstrate the practical strength of the method and its potential clinical use.
Acoustic scene analysis with multi-head attention networks
Authors: Weimin Wang, Weiran Wang, Ming Sun, Chao Wang
Date: 2019-09-16 00:00:00
Tasks: Acoustic Scene Classification, General Classification, Scene Classification

Acoustic Scene Classification (ASC) is a challenging task, as a single scene may involve multiple events that contain complex sound patterns. For example, a cooking scene may contain several sound sources including silverware clinking, chopping, frying, etc. What complicates ASC more is that classes of different activities could have overlapping sounds patterns (e.g. both cooking and dishwashing could have silverware clinking sound). In this paper, we propose a multi-head attention network to model the complex temporal input structures for ASC. The proposed network takes the audio's time-frequency representation as input, and it leverages standard VGG plus LSTM layers to extract high-level feature representation. Further more, it applies multiple attention heads to summarize various patterns of sound events into fixed dimensional representation, for the purpose of final scene classification. The whole network is trained in an end-to-end fashion with back-propagation. Experimental results confirm that our model discovers meaningful sound patterns through the attention mechanism, without using explicit supervision in the alignment. We evaluated our proposed model using DCASE 2018 Task 5 dataset, and achieved competitive performance on par with previous winner's results.
Spoken Speech Enhancement using EEG
Authors: Gautam Krishna, Co Tran, Yan Han, Mason Carnahan, Ahmed H. Tewfik
Date: 2019-09-13 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Generative Adversarial Network, regression, Speech Enhancement

In this paper we demonstrate spoken speech enhancement using electroencephalography (EEG) signals using a generative adversarial network (GAN) based model, gated recurrent unit (GRU) regression based model, temporal convolutional network (TCN) regression model and finally using a mixed TCN GRU regression model. We compare our EEG based speech enhancement results with traditional log minimum mean-square error (MMSE) speech enhancement algorithm and our proposed methods demonstrate significant improvement in speech enhancement quality compared to the traditional method. Our overall results demonstrate that EEG features can be used to clean speech recorded in presence of background noise. To the best of our knowledge this is the first time a spoken speech enhancement is demonstrated using EEG features recorded in parallel with spoken speech.
Variable Population Memetic Search: A Case Study on the Critical Node Problem
Authors: Yangming Zhou, Jin-Kao Hao, Zhang-Hua Fu, Zhe Wang, Xiangjing Lai
Date: 2019-09-12 00:00:00

Population-based memetic algorithms have been successfully applied to solve many difficult combinatorial problems. Often, a population of fixed size was used in such algorithms to record some best solutions sampled during the search. However, given the particular features of the problem instance under consideration, a population of variable size would be more suitable to ensure the best search performance possible. In this work, we propose variable population memetic search (VPMS), where a strategic population sizing mechanism is used to dynamically adjust the population size during the memetic search process. Our VPMS approach starts its search from a small population of only two solutions to focus on exploitation, and then adapts the population size according to the search status to continuously influence the balancing between exploitation and exploration. We illustrate an application of the VPMS approach to solve the challenging critical node problem (CNP). We show that the VPMS algorithm integrating a variable population, an effective local optimization procedure (called diversified late acceptance search) and a backbone-based crossover operator performs very well compared to state-of-the-art CNP algorithms. The algorithm is able to discover new upper bounds for 13 instances out of the 42 popular benchmark instances, while matching 23 previous best-known upper bounds.
Solving Financial Regulatory Compliance Using Software Contracts
Authors: Newres Al Haider, Dilhan Thilakarathne, Joost Bosman
Date: 2019-09-11 00:00:00

Ensuring compliance with various laws and regulations is of utmost priority for financial institutions. Traditional methods in this area have been shown to be inefficient. Manual processing does not scale well. Automated efforts are hindered due to the lack of formalization of domain knowledge and problems of integrating such knowledge into software systems. In this work we propose an approach to tackle these issues by encoding them into software contracts using a Controlled Natural Language. In particular, we encode a portion of the Money Market Statistical Reporting (MMSR) regulations into contracts specified by the clojure.spec framework. We show how various features of a contract framework, in particular clojure.spec, can help to tackle issues that occur when dealing with compliance: validation with explanations and test data generation. We benchmark our proposed solution and show that this approach can effectively solve compliance issues in this particular use case.
To Detect Irregular Trade Behaviors In Stock Market By Using Graph Based Ranking Methods
Authors: Loc Tran, Linh Tran
Date: 2019-09-04 00:00:00
Tasks: BIG-bench Machine Learning

To detect the irregular trade behaviors in the stock market is the important problem in machine learning field. These irregular trade behaviors are obviously illegal. To detect these irregular trade behaviors in the stock market, data scientists normally employ the supervised learning techniques. In this paper, we employ the three graph Laplacian based semi-supervised ranking methods to solve the irregular trade behavior detection problem. Experimental results show that that the un-normalized and symmetric normalized graph Laplacian based semi-supervised ranking methods outperform the random walk Laplacian based semi-supervised ranking method.
Hyper-Graph-Network Decoders for Block Codes
Authors: Eliya Nachmani, Lior Wolf
Date: 2019-09-05 00:00:00
Proceeding: NeurIPS 2019 12

Neural decoders were shown to outperform classical message passing techniques for short BCH codes. In this work, we extend these results to much larger families of algebraic block codes, by performing message passing with graph neural networks. The parameters of the sub-network at each variable-node in the Tanner graph are obtained from a hypernetwork that receives the absolute values of the current message as input. To add stability, we employ a simplified version of the arctanh activation that is based on a high order Taylor approximation of this activation function. Our results show that for a large number of algebraic block codes, from diverse families of codes (BCH, LDPC, Polar), the decoding obtained with our method outperforms the vanilla belief propagation method as well as other learning techniques from the literature.
Neural Att entive Session-based Recommendation
Authors: Jing Li, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Tao Lian, Jun Ma
Date: 2017-11-03 00:00:00
Proceeding: CIKM 2017 11
Tasks: Session-Based Recommendations

Given e-commerce scenarios that user profiles are invisible, sessionbased recommendation is proposed to generate recommendation
results from short sessions. Previous work only considers the
user’s sequential behavior in the current session, whereas the
user’s main purpose in the current session is not emphasized. In
this paper, we propose a novel neural networks framework, i.e.,
Neural Attentive Recommendation Machine (NARM), to tackle
this problem. Specifically, we explore a hybrid encoder with an
attention mechanism to model the user’s sequential behavior and
capture the user’s main purpose in the current session, which
are combined as a unified session representation later. We then
compute the recommendation scores for each candidate item with
a bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session
representations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental
results show that NARM outperforms state-of-the-art baselines on
both datasets. Furthermore, we also find that NARM achieves a
significant improvement on long sessions, which demonstrates its
advantages in modeling the user’s sequential behavior and main
purpose simultaneously
A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection
Authors: Miquel Alfaras, Miguel C. Soriano, Silvia Ortín
Date: 2019-07-18 00:00:00
Proceeding: Frontiers in Physics 2019 7
Tasks: Arrhythmia Detection, BIG-bench Machine Learning, Electrocardiography (ECG), feature selection, General Classification, Heartbeat Classification, Sensitivity

We present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. Our classifier has a low-demanding feature processing that only requires a single ECG lead. Its training and validation follows an inter-patient procedure. Our approach is compatible with an online classification that aligns well with recent advances in health-monitoring wireless devices and wearables. The use of a combination of ensembles allows us to exploit parallelism to train the classifier with remarkable speeds. The heartbeat classifier is evaluated over two ECG databases, the MIT-BIH AR and the AHA. In the MIT-BIH AR database, our classification approach provides a sensitivity of 92.7% and positive predictive value of 86.1% for the ventricular ectopic beats, using the single lead II, and a sensitivity of 95.7% and positive predictive value of 75.1% when using the lead V1'. These results are comparable with the state of the art in fully automatic ECG classifiers and even outperform other ECG classifiers that follow more complex feature-selection approaches.
A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals
Authors: Huifang Huang, Jie Liu, Qiang Zhu, Ruiping Wang, Guangshu Hu
Date: 2014-06-30 00:00:00
Proceeding: BioMedical Engineering OnLine 2014 6
Tasks: Classification, General Classification, Heartbeat Classification

Background
The inter-patient classification schema and the Association for the Advancement of Medical Instrumentation (AAMI) standards are important to the construction and evaluation of automated heartbeat classification systems. The majority of previously proposed methods that take the above two aspects into consideration use the same features and classification method to classify different classes of heartbeats. The performance of the classification system is often unsatisfactory with respect to the ventricular ectopic beat (VEB) and supraventricular ectopic beat (SVEB).

Methods
Based on the different characteristics of VEB and SVEB, a novel hierarchical heartbeat classification system was constructed. This was done in order to improve the classification performance of these two classes of heartbeats by using different features and classification methods. First, random projection and support vector machine (SVM) ensemble were used to detect VEB. Then, the ratio of the RR interval was compared to a predetermined threshold to detect SVEB. The optimal parameters for the classification models were selected on the training set and used in the independent testing set to assess the final performance of the classification system. Meanwhile, the effect of different lead configurations on the classification results was evaluated.

Results
Results showed that the performance of this classification system was notably superior to that of other methods. The VEB detection sensitivity was 93.9% with a positive predictive value of 90.9%, and the SVEB detection sensitivity was 91.1% with a positive predictive value of 42.2%. In addition, this classification process was relatively fast.

Conclusions
A hierarchical heartbeat classification system was proposed based on the inter-patient data division to detect VEB and SVEB. It demonstrated better classification performance than existing methods. It can be regarded as a promising system for detecting VEB and SVEB of unknown patients in clinical practice.
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network
Authors: Awni Y. Hannun, Pranav Rajpurkar, Masoumeh Haghpanahi, Geoffrey H. Tison, Codie Bourn, Mintu P. Turakhia, Andrew Y. Ng
Date: 2019-01-07 00:00:00
Proceeding: Nature Medicine 2019 1
Tasks: Arrhythmia Detection, Diagnostic, Electrocardiography (ECG), General Classification, Rhythm, Sensitivity, Specificity

Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning present an opportunity to substantially improve the accuracy and scalability of automated ECG analysis. However, a comprehensive evaluation of an end-to-end deep learning approach for ECG analysis across a wide variety of diagnostic classes has not been previously reported. Here, we develop a deep neural network (DNN) to classify 12 rhythm classes using 91,232 single-lead ECGs from 53,549 patients who used a single-lead ambulatory ECG monitoring device. When validated against an independent test dataset annotated by a consensus committee of board-certified practicing cardiologists, the DNN achieved an average area under the receiver operating characteristic curve (ROC) of 0.97. The average F1 score, which is the harmonic mean of the positive predictive value and sensitivity, for the DNN (0.837) exceeded that of average cardiologists (0.780). With specificity fixed at the average specificity achieved by cardiologists, the sensitivity of the DNN exceeded the average cardiologist sensitivity for all rhythm classes. These findings demonstrate that an end-to-end deep learning approach can classify a broad range of distinct arrhythmias from single-lead ECGs with high diagnostic performance similar to that of cardiologists. If confirmed in clinical settings, this approach could reduce the rate of misdiagnosed computerized ECG interpretations and improve the efficiency of expert human ECG interpretation by accurately triaging or prioritizing the most urgent conditions.

Dataset available from: https://irhythm.github.io/cardiol_test_set/
Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG
Authors: Fernando Andreotti, Oliver Carr, Marco A. F. Pimentel, Adam Mahdi, Maarten De Vos
Date: 2017-09-24 00:00:00
Proceeding: 2017 Computing in Cardiology (CinC) 2017 9
Tasks: Arrhythmia Detection, Electrocardiography (ECG), Management

The diagnosis of cardiovascular diseases such as atrial fibrillation (AF) is a lengthy and expensive procedure that often requires visual inspection of ECG signals by experts. In order to improve patient management and reduce healthcare costs, automated detection of these pathologies is of utmost importance. In this study, we classify short segments of ECG into four classes (AF, normal, other rhythms or noise) as part of the Physionet/Computing in Cardiology Challenge 2017. We compare a state-of-the-art feature-based classifier with a convolutional neural network approach. Both methods were trained using the challenge data, supplemented with an additional database derived from Physionet. The feature-based classifier obtained an F1 score of 72.0% on the training set (5-fold cross-validation), and 79% on the hidden test set. Similarly, the convolutional neural network scored 72.1% on the augmented database and 83% on the test set. The latter method resulted on a final score of 79% at the competition. Developed routines and pre-trained models are freely available under a GNU GPLv3 license.
An Open-source Toolbox for Analysing and Processing PhysioNet Databases in MATLAB and Octave
Authors: Ikaro Silva, George Moody
Date: 2014-09-24 00:00:00
Proceeding: Journal of Open Research Software 2014 9
Tasks: Arrhythmia Detection, EEG, Electrocardiography (ECG), Electroencephalogram (EEG), Seizure Detection, Sleep Stage Detection

The WaveForm DataBase (WFDB) Toolbox for MATLAB/Octave enables integrated access to PhysioNet's software and databases. Using the WFDB Toolbox for MATLAB/Octave, users have access to over 50 physiological databases in PhysioNet. The toolbox provides access over 4 TB of biomedical signals including ECG, EEG, EMG, and PLETH. Additionally, most signals are accompanied by metadata such as medical annotations of clinical events: arrhythmias, sleep stages, seizures, hypotensive episodes, etc. Users of this toolbox should easily be able to reproduce, validate, and compare results published based on PhysioNet's software and databases.
A convolutional neural network approach to detect congestive heart failure
Authors: Mihaela Porumb, Ernesto Iadanza, Sebastiano Massaro, Leandro Pecchia
Date: 2019-09-03 00:00:00
Proceeding: Biomedical Signal Processing and Control Volume 2019 9
Tasks: Congestive Heart Failure detection, Electrocardiography (ECG), Heartbeat Classification, Heart Rate Variability

Congestive Heart Failure (CHF) is a severe pathophysiological condition associated with high prevalence, high mortality rates, and sustained healthcare costs, therefore demanding efficient methods for its detection. Despite recent research has provided methods focused on advanced signal processing and machine learning, the potential of applying Convolutional Neural Network (CNN) approaches to the automatic detection of CHF has been largely overlooked thus far. This study addresses this important gap by presenting a CNN model that accurately identifies CHF on the basis of one raw electrocardiogram (ECG) heartbeat only, also juxtaposing existing methods typically grounded on Heart Rate Variability. We trained and tested the model on publicly available ECG datasets, comprising a total of 490,505 heartbeats, to achieve 100% CHF detection accuracy. Importantly, the model also identifies those heartbeat sequences and ECG’s morphological characteristics which are class-discriminative and thus prominent for CHF detection. Overall, our contribution substantially advances the current methodology for detecting CHF and caters to clinical practitioners’ needs by providing an accurate and fully transparent tool to support decisions concerning CHF detection.