pwc-archive/papers-with-abstracts huggingface dataset:
Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO
Authors: Gabriel Garcia, Gladston Moreira, David Menotti, Eduardo Luz
Date: 2017-09-05 00:00:00
Proceeding: Scientific Reports 2017 9
Tasks: Arrhythmia Detection, ECG Classification, Electrocardiography (ECG), feature selection, General Classification, Heartbeat Classification

Classifying arrhythmias can be a tough task for a human being and automating this task is highly desirable. Nevertheless fully automatic arrhythmia classification through Electrocardiogram (ECG) signals is a challenging task when the inter-patient paradigm is considered. For the inter-patient paradigm, classifiers are evaluated on signals of unknown subjects, resembling the real world scenario. In this work, we explore a novel ECG representation based on vectorcardiogram (VCG), called temporal vectorcardiogram (TVCG), along with a complex network for feature extraction. We also fine-tune the SVM classifier and perform feature selection with a particle swarm optimization (PSO) algorithm. Results for the inter-patient paradigm show that the proposed method achieves the results comparable to state-of-the-art in MIT-BIH database (53% of Positive predictive (+P) for the Supraventricular ectopic beat (S) class and 87.3% of Sensitivity (Se) for the Ventricular ectopic beat (V) class) that TVCG is a richer representation of the heartbeat and that it could be useful for problems involving the cardiac signal and pattern recognition.*

Source code available from http://www.decom.ufop.br/csilab/site_media/uploads/code/tvcg_pso.zip
Analysis and classification of heart diseases using heartbeat features and machine learning algorithms
Authors: Fajr Ibrahem Alarsan, Mamoon Younes
Date: 2019-08-31 00:00:00
Proceeding: Journal of Big Data 2019 2019 8
Tasks: BIG-bench Machine Learning, Binary Classification, Classification, ECG Classification, Electrocardiography (ECG), General Classification, Heartbeat Classification, Multi-class Classification

This study proposed an ECG (Electrocardiogram) classification approach using machine learning based on several ECG features. An electrocardiogram (ECG) is a signal that measures the electric activity of the heart. The proposed approach is implemented using ML-libs and Scala language on Apache Spark framework; MLlib is Apache Spark’s scalable machine learning library. The key challenge in ECG classification is to handle the irregularities in the ECG signals which is very important to detect the patient status. Therefore, we have proposed an efficient approach to classify ECG signals with high accuracy Each heartbeat is a combination of action impulse waveforms produced by different specialized cardiac heart tissues. Heartbeats classification faces some difficulties because these waveforms differ from person to another, they are described by some features. These features are the inputs of machine learning algorithm. In general, using Spark–Scala tools simplifies the usage of many algorithms such as machine-learning (ML) algorithms. On other hand, Spark–Scala is preferred to be used more than other tools when size of processing data is too large. In our case, we have used a dataset with 205,146 records to evaluate the performance of our approach. Machine learning libraries in Spark–Scala provide easy ways to implement many classification algorithms (Decision Tree, Random Forests, Gradient-Boosted Trees (GDB), etc.). The proposed method is evaluated and validated on baseline MIT-BIH Arrhythmia and MIT-BIH Supraventricular Arrhythmia database. The results show that our approach achieved an overall accuracy of 96.75% using GDB Tree algorithm and 97.98% using random Forest for binary classification. For multi class classification, it achieved to 98.03% accuracy using Random Forest, Gradient Boosting tree supports only binary classification.
Application of deep convolutional neural network for automated detection of myocardial infarction using ecg signals
Authors: U Rajendra Acharya. Hamido Fujita, Hamido Fujita, Shu Lih Oh, Yuki Hagiwara, Jen Hong Tan, Muhammad Adam
Date: 2017-06-01 00:00:00
Proceeding: Information Sciences 2017 6
Tasks: Diagnostic, Electrocardiography (ECG), Myocardial infarction detection

The electrocardiogram (ECG) is a useful diagnostic tool to diagnose various cardiovascular diseases (CVDs) such as myocardial infarction (MI). The ECG records the heart's electrical activity and these signals are able to reflect the abnormal activity of the heart. However, it is challenging to visually interpret the ECG signals due to its small amplitude and duration. Therefore, we propose a novel approach to automatically detect the MI using ECG signals. In this study, we implemented a convolutional neural network (CNN) algorithm for the automated detection of a normal and MI ECG beats (with noise and without noise). We achieved an average accuracy of 93.53% and 95.22% using ECG beats with noise and without noise removal respectively. Further, no feature extraction or selection is performed in this work. Hence, our proposed algorithm can accurately detect the unknown ECG signals even with noise. So, this system can be introduced in clinical settings to aid the clinicians in the diagnosis of MI.
A New Pattern Recognition Method for Detection and Localization of Myocardial Infarction Using T-Wave Integral and Total Integral as Extracted Features from One Cycle of ECG Signal
Authors: Naser Safdarian, Nader Jafarnia Dabanloo, Gholamreza Attarodi
Date: 2014-08-01 00:00:00
Proceeding: JBiSE  Vol.7 No.10 , August 2014 2014 8
Tasks: General Classification, Myocardial infarction detection

In this paper we used two new features i.e. T-wave integral and total integral as extracted feature from one cycle of normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our previous work we used some features of body surface potential map data for this aim. But we know the standard ECG is more popular, so we focused our detection and localization of MI on standard ECG. We use the T-wave integral because this feature is important impression of T-wave in MI. The second feature in this research is total integral of one ECG cycle, because we believe that the MI affects the morphology of the ECG signal which leads to total integral changes. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI, because this method has very good accuracy for classification of normal signal and abnormal signal. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 76% for accuracy in test data for localization and over 94% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve the accuracy of classification by adding more features in this method. A simple method based on using only two features which were extracted from standard ECG is presented and has good accuracy in MI localization.
Interpretability Analysis of Heartbeat Classification Based on Heartbeat Activity’s Global Sequence Features and BiLSTM-Attention Neural Network
Authors: Runchuan Li, Xingjin Zhang, Honghua Dai, Bing Zhou, Zongmin Wang
Date: 2019-08-07 00:00:00
Proceeding: IEEE Access 2019 8
Tasks: Arrhythmia Detection, Electrocardiography (ECG), General Classification, Heartbeat Classification

Arrhythmia is a disease that threatens human life. Therefore, timely diagnosis of arrhythmia is of great significance in preventing heart disease and sudden cardiac death. The BiLSTM-Attention neural network model with heartbeat activity's global sequence features can effectively improve the accuracy of heartbeat classification. Firstly, the noise is removed by the continuous wavelet transform method. Secondly, the peak of the R wave is detected by the tagged database, and then the P-QRS-T wave morphology and the RR interval are extracted. This feature set is heartbeat activity's global sequence features, which combines single heartbeat morphology and 21 consecutive RR intervals. Finally, the Bi-LSTM algorithm and the BiLSTM-Attention algorithm are used to identify heartbeat category respectively, and the MIT-BIH arrhythmia database is used to verify the algorithm. The results show that the BiLSTM-Attention model combined with heartbeat activity's global sequence features has higher interpretability than other methods discussed in this paper.
Denoising Time Series Data Using Asymmetric Generative Adversarial Networks
Authors: Sunil Gandhi, Tim Oates, Tinoosh Mohsenin, David Hairston
Date: 2018-06-17 00:00:00
Proceeding: Advances in Knowledge Discovery and Data Mining. PAKDD 2018 2018 6
Tasks: Denoising, EEG, EEG Denoising, Electroencephalogram (EEG), Generative Adversarial Network, Time Series, Time Series Analysis

Denoising data is a preprocessing step for several time series mining algorithms. This step is especially important if the noise in data originates from diverse sources. Consequently, it is commonly used in biomedical applications that use Electroencephalography (EEG) data. In EEG data noise can occur due to ocular, muscular and cardiac activities. In this paper, we explicitly learn to remove noise from time series data without assuming a prior distribution of noise. We propose an online, fully automated, end-to-end system for denoising time series data. Our model for denoising time series is trained using unpaired training corpora and does not need information about the source of the noise or how it is manifested in the time series. We propose a new architecture called AsymmetricGAN that uses a generative adversarial network for denoising time series data. To analyze our approach, we create a synthetic dataset that is easy to visualize and interpret. We also evaluate and show the effectiveness of our approach on an existing EEG dataset.
Adapting the Tesseract Open Source OCR Engine for Multilingual OCR
Authors: Ray Smith, Daria Antonova, Dar-Shyang Lee
Date: 2009-07-25 00:00:00
Proceeding: ACM 2009 7
Tasks: Optical Character Recognition (OCR)

We describe efforts to adapt the Tesseract open source OCR engine for multiple scripts and languages. Effort has been concentrated on enabling generic multi-lingual operation such that negligible customization is required for a new language beyond providing a corpus of text. Although change was required to various modules, including physical layout analysis, and linguistic post-processing, no change was required to the character classifier beyond changing a few limits. The Tesseract classifier has adapted easily to Simplified Chinese. Test results on English, a mixture of European languages, and Russian, taken from a random sample of books, show a reasonably consistent word error rate between 3.72% and 5.78%, and Simplified Chinese has a character error rate of only 3.77%.
Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment
Authors: Kekai Sheng, Wei-Ming Dong, Chongyang Ma, Xing Mei, Feiyue Huang, Bao-Gang Hu
Date: 2018-10-22 00:00:00
Proceeding: ACM Multimedia Conference 2018 10
Tasks: Aesthetics Quality Assessment

Aggregation structures with explicit information, such as image attributes and scene semantics, are effective and popular for intelligent systems for assessing aesthetics of visual data. However, useful information may not be available due to the high cost of manual annotation and expert design. In this paper, we present a novel multi-patch (MP) aggregation method for image aesthetic assessment. Different from state-of-the-art methods, which augment an MP aggregation network with various visual attributes, we train the model in an end-to-end manner with aesthetic labels only (i.e., aesthetically positive or negative). We achieve the goal by resorting to an attention-based mechanism that adaptively adjusts the weight of each patch during the training process to improve learning efficiency. In addition, we propose a set of objectives with three typical attention mechanisms (i.e., average, minimum, and adaptive) and evaluate their effectiveness on the Aesthetic Visual Analysis (AVA) benchmark. Numerical results show that our approach outperforms existing methods by a large margin. We further verify the effectiveness of the proposed attention-based objectives via ablation studies and shed light on the design of aesthetic assessment systems.
FaceGenderID: Exploiting Gender Information in DCNNs Face Recognition Systems
Authors: Ruben Vera-Rodriguez, Marta Blazquez, Aythami Morales, Ester Gonzalez-Sosa, Joao C. Neves, Hugo Proenca
Date: 2019-06-28 00:00:00
Proceeding: Computer Vision and Pattern Recognition (CVPR), 2019 2019 6
Tasks: Face Recognition, Face Verification

This paper addresses the effect of gender as a covariate in face verification systems. Even  though pre-trained models based on Deep Convolutional Neural Networks (DCNNs), such as VGG-Face or ResNet-50, achieve very high performance, they are trained on very large datasets comprising millions of images, which have biases regarding demographic aspects like the gender and the ethnicity among others. In this work, we first analyse the separate performance of these state-of-the-art models for males and females. We observe a gap between face verification performances obtained by both gender classes. These results suggest that features obtained by biased models are affected by the gender covariate. We propose a gender-dependent training approach to improve the feature representation for both genders, and develop both: i) gender specific DCNNs models, and ii) a gender balanced DCNNs model. Our results show significant and consistent improvements in face verification performance for both genders, individually and in general with our proposed approach. Finally, we announce the availability (at GitHub) of the FaceGenderID DCNNs models proposed in this work, which can support further experiments on this topic.
Gourmet Photography Dataset for Aesthetic Assessment of Food Images
Authors: Kekai Sheng, Wei-Ming Dong, Haibin Huang, Chongyang Ma, Bao-Gang Hu
Date: 2018-12-04 00:00:00
Proceeding: SIGGRAPH Asia 2018 2018 12

In this study, we present the Gourmet Photography Dataset (GPD), which is the first large-scale dataset for aesthetic assessment of food photographs. We collect 12,000 food images together with human-annotated labels (i.e., aesthetically positive or negative) to build this dataset. We evaluate the performance of several popular machine learning algorithms for aesthetic assessment of food images to verify the effectiveness and importance of our GPD dataset. Experimental results show that deep convolutional neural networks trained on GPD can achieve comparable performance with human experts in this task, even on unseen food photographs. Our experiments also provide insights to support further study and applications related to visual analysis of food images.
Unsupervised Learning for Real-World Super-Resolution
Authors: Andreas Lugmayr, Martin Danelljan, Radu Timofte
Date: 2019-09-20 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Most current super-resolution methods rely on low and high resolution image pairs to train a network in a fully supervised manner. However, such image pairs are not available in real-world applications. Instead of directly addressing this problem, most works employ the popular bicubic downsampling strategy to artificially generate a corresponding low resolution image. Unfortunately, this strategy introduces significant artifacts, removing natural sensor noise and other real-world characteristics. Super-resolution networks trained on such bicubic images therefore struggle to generalize to natural images. In this work, we propose an unsupervised approach for image super-resolution. Given only unpaired data, we learn to invert the effects of bicubic downsampling in order to restore the natural image characteristics present in the data. This allows us to generate realistic image pairs, faithfully reflecting the distribution of real-world images. Our super-resolution network can therefore be trained with direct pixel-wise supervision in the high resolution domain, while robustly generalizing to real input. We demonstrate the effectiveness of our approach in quantitative and qualitative experiments.
On the Convergence of Approximate and Regularized Policy Iteration Schemes
Authors: Elena Smirnova, Elvis Dohmatob
Date: 2019-09-20 00:00:00
Tasks: Q-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Entropy regularized algorithms such as Soft Q-learning and Soft Actor-Critic, recently showed state-of-the-art performance on a number of challenging reinforcement learning (RL) tasks. The regularized formulation modifies the standard RL objective and thus generally converges to a policy different from the optimal greedy policy of the original RL problem. Practically, it is important to control the sub-optimality of the regularized optimal policy. In this paper, we establish sufficient conditions for convergence of a large class of regularized dynamic programming algorithms, unified under regularized modified policy iteration (MPI) and conservative value iteration (VI) schemes. We provide explicit convergence rates to the optimality depending on the decrease rate of the regularization parameter. Our experiments show that the empirical error closely follows the established theoretical convergence rates. In addition to optimality, we demonstrate two desirable behaviours of the regularized algorithms even in the absence of approximations: robustness to stochasticity of environment and safety of trajectories induced by the policy iterates.
Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike Sharing Systems
Authors: Xinghua Zheng, Ming Tang, Hankz Hankui Zhuo, Kevin X. Wen
Date: 2019-09-20 00:00:00

Bike Sharing Systems (BSSs) have been adopted in many major cities of the world due to traffic congestion and carbon emissions. Although there have been approaches to exploiting either bike trailers via crowdsourcing or carrier vehicles to reposition bikes in the ``right'' stations in the ``right'' time, they do not jointly consider the usage of both bike trailers and carrier vehicles. In this paper, we aim to take advantage of both bike trailers and carrier vehicles to reduce the loss of demand with regard to the crowdsourcing of bike trailers and the fuel cost of carrier vehicles. In the experiment, we exhibit that our approach outperforms baselines in several datasets from bike sharing companies.
Optimal Rates for Learning Hidden Tree Structures
Authors: Konstantinos E. Nikolakakis, Dionysios S. Kalogerias, Anand D. Sarwate
Date: 2019-09-20 00:00:00

We provide high probability finite sample complexity guarantees for hidden non-parametric structure learning of tree-shaped graphical models, whose hidden and observable nodes are discrete random variables with either finite or countable alphabets. We study a fundamental quantity called the (noisy) information threshold, which arises naturally from the error analysis of the Chow-Liu algorithm and, as we discuss, provides explicit necessary and sufficient conditions on sample complexity, by effectively summarizing the difficulty of the tree-structure learning problem. Specifically, we show that the finite sample complexity of the Chow-Liu algorithm for ensuring exact structure recovery from noisy data is inversely proportional to the information threshold squared (provided it is positive), and scales almost logarithmically relative to the number of nodes over a given probability of failure. Conversely, we show that, if the number of samples is less than an absolute constant times the inverse of information threshold squared, then no algorithm can recover the hidden tree structure with probability greater than one half. As a consequence, our upper and lower bounds match with respect to the information threshold, indicating that it is a fundamental quantity for the problem of learning hidden tree-structured models. Further, the Chow-Liu algorithm with noisy data as input achieves the optimal rate with respect to the information threshold. Lastly, as a byproduct of our analysis, we resolve the problem of tree structure learning in the presence of non-identically distributed observation noise, providing conditions for convergence of the Chow-Liu algorithm under this setting, as well.
Bayesian Optimization for Iterative Learning
Authors: Vu Nguyen, Sebastian Schulze, Michael A. Osborne
Date: 2019-09-20 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Bayesian Optimization, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The performance of deep (reinforcement) learning systems crucially depends on the choice of hyperparameters. Their tuning is notoriously expensive, typically requiring an iterative training process to run for numerous steps to convergence. Traditional tuning algorithms only consider the final performance of hyperparameters acquired after many expensive iterations and ignore intermediate information from earlier training steps. In this paper, we present a Bayesian optimization (BO) approach which exploits the iterative structure of learning algorithms for efficient hyperparameter tuning. We propose to learn an evaluation function compressing learning progress at any stage of the training process into a single numeric score according to both training success and stability. Our BO framework is then balancing the benefit of assessing a hyperparameter setting over additional training steps against their computation cost. We further increase model efficiency by selectively including scores from different training steps for any evaluated hyperparameter set. We demonstrate the efficiency of our algorithm by tuning hyperparameters for the training of deep reinforcement learning agents and convolutional neural networks. Our algorithm outperforms all existing baselines in identifying optimal hyperparameters in minimal time.
Understanding Architectures Learnt by Cell-based Neural Architecture Search
Authors: Yao Shu, Wei Wang, Shaofeng Cai
Date: 2019-09-20 00:00:00
Proceeding: ICLR 2020 1
Tasks: image-classification, Image Classification, Language Modeling, Language Modelling, Neural Architecture Search

Neural architecture search (NAS) searches architectures automatically for given tasks, e.g., image classification and language modeling. Improving the search efficiency and effectiveness have attracted increasing attention in recent years. However, few efforts have been devoted to understanding the generated architectures. In this paper, we first reveal that existing NAS algorithms (e.g., DARTS, ENAS) tend to favor architectures with wide and shallow cell structures. These favorable architectures consistently achieve fast convergence and are consequently selected by NAS algorithms. Our empirical and theoretical study further confirms that their fast convergence derives from their smooth loss landscape and accurate gradient information. Nonetheless, these architectures may not necessarily lead to better generalization performance compared with other candidate architectures in the same search space, and therefore further improvement is possible by revising existing NAS algorithms.