pwc-archive/papers-with-abstracts huggingface dataset:
Multi-level Stress Assessment Using Multi-domain Fusion of ECG Signal
Authors: Zeeshan Ahmad, Naimul Khan
Date: 2020-08-12 00:00:00

Stress analysis and assessment of affective states of mind using ECG as a physiological signal is a burning research topic in biomedical signal processing. However, existing literature provides only binary assessment of stress, while multiple levels of assessment may be more beneficial for healthcare applications. Furthermore, in present research, ECG signal for stress analysis is examined independently in spatial domain or in transform domains but the advantage of fusing these domains has not been fully utilized. To get the maximum advantage of fusing diferent domains, we introduce a dataset with multiple stress levels and then classify these levels using a novel deep learning approach by converting ECG signal into signal images based on R-R peaks without any feature extraction. Moreover, We made signal images multimodal and multidomain by converting them into time-frequency and frequency domain using Gabor wavelet transform (GWT) and Discrete Fourier Transform (DFT) respectively. Convolutional Neural networks (CNNs) are used to extract features from different modalities and then decision level fusion is performed for improving the classification accuracy. The experimental results on an in-house dataset collected with 15 users show that with proposed fusion framework and using ECG signal to image conversion, we reach an average accuracy of 85.45%.
DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation
Authors: Jie Yang, Kaichun Mo, Yu-Kun Lai, Leonidas J. Guibas, Lin Gao
Date: 2020-08-12 00:00:00
Tasks: 3D Shape Generation

D shape generation is a fundamental operation in computer graphics. While significant progress has been made, especially with recent deep generative models, it remains a challenge to synthesize high-quality shapes with rich geometric details and complex structure, in a controllable manner. To tackle this, we introduce DSG-Net, a deep neural network that learns a disentangled structured and geometric mesh representation for 3D shapes, where two key aspects of shapes, geometry, and structure, are encoded in a synergistic manner to ensure plausibility of the generated shapes, while also being disentangled as much as possible. This supports a range of novel shape generation applications with disentangled control, such as interpolation of structure (geometry) while keeping geometry (structure) unchanged. To achieve this, we simultaneously learn structure and geometry through variational autoencoders (VAEs) in a hierarchical manner for both, with bijective mappings at each level. In this manner, we effectively encode geometry and structure in separate latent spaces, while ensuring their compatibility: the structure is used to guide the geometry and vice versa. At the leaf level, the part geometry is represented using a conditional part VAE, to encode high-quality geometric details, guided by the structure context as the condition. Our method not only supports controllable generation applications but also produces high-quality synthesized shapes, outperforming state-of-the-art methods. The code has been released at https://github.com/IGLICT/DSG-Net.
An Exploratory Study of COVID-19 Information on Twitter in the Greater Region
Authors: Ninghan Chen, Zhiqiang Zhong, Jun Pang
Date: 2020-08-12 00:00:00
Tasks: Representation Learning

The outbreak of the COVID-19 leads to a burst of information in major online social networks (OSNs). Facing this constantly changing situation, OSNs have become an essential platform for people expressing opinions and seeking up-to-the-minute information. Thus, discussions on OSNs may become a reflection of reality. This paper aims to figure out the distinctive characteristics of the Greater Region (GR) through conducting a data-driven exploratory study of Twitter COVID-19 information in the GR and related countries using machine learning and representation learning methods. We find that tweets volume and COVID-19 cases in GR and related countries are correlated, but this correlation only exists in a particular period of the pandemic. Moreover, we plot the changing of topics in each country and region from 2020-01-22 to 2020-06-05, figuring out the main differences between GR and related countries.
GraphKKE: Graph Kernel Koopman Embedding for Human Microbiome Analysis
Authors: Kateryna Melnyk, Stefan Klus, Grégoire Montavon, Tim Conrad
Date: 2020-08-12 00:00:00

More and more diseases have been found to be strongly correlated with disturbances in the microbiome constitution, e.g., obesity, diabetes, or some cancer types. Thanks to modern high-throughput omics technologies, it becomes possible to directly analyze human microbiome and its influence on the health status. Microbial communities are monitored over long periods of time and the associations between their members are explored. These relationships can be described by a time-evolving graph. In order to understand responses of the microbial community members to a distinct range of perturbations such as antibiotics exposure or diseases and general dynamical properties, the time-evolving graph of the human microbial communities has to be analyzed. This becomes especially challenging due to dozens of complex interactions among microbes and metastable dynamics. The key to solving this problem is the representation of the time-evolving graphs as fixed-length feature vectors preserving the original dynamics. We propose a method for learning the embedding of the time-evolving graph that is based on the spectral analysis of transfer operators and graph kernels. We demonstrate that our method can capture temporary changes in the time-evolving graph on both created synthetic data and real-world data. Our experiments demonstrate the efficacy of the method. Furthermore, we show that our method can be applied to human microbiome data to study dynamic processes.
Predicting MOOCs Dropout Using Only Two Easily Obtainable Features from the First Week's Activities
Authors: Ahmed Alamri, Mohammad Alshehri, Alexandra I. Cristea, Filipe D. Pereira, Elaine Oliveira, Lei Shi, Craig Stewart
Date: 2020-08-12 00:00:00

While Massive Open Online Course (MOOCs) platforms provide knowledge in a new and unique way, the very high number of dropouts is a significant drawback. Several features are considered to contribute towards learner attrition or lack of interest, which may lead to disengagement or total dropout. The jury is still out on which factors are the most appropriate predictors. However, the literature agrees that early prediction is vital to allow for a timely intervention. Whilst feature-rich predictors may have the best chance for high accuracy, they may be unwieldy. This study aims to predict learner dropout early-on, from the first week, by comparing several machine-learning approaches, including Random Forest, Adaptive Boost, XGBoost and GradientBoost Classifiers. The results show promising accuracies (82%-94%) using as little as 2 features. We show that the accuracies obtained outperform state of the art approaches, even when the latter deploy several features.
Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students
Authors: Lei Shi, Alexandra I. Cristea, Armando M. Toda, Wilk Oliveira
Date: 2020-08-12 00:00:00
Tasks: Clustering, Diversity

Massive Open Online Courses (MOOCs) exhibit a remarkable heterogeneity of students. The advent of complex "big data" from MOOC platforms is a challenging yet rewarding opportunity to deeply understand how students are engaged in MOOCs. Past research, looking mainly into overall behavior, may have missed patterns related to student diversity. Using a large dataset from a MOOC offered by FutureLearn, we delve into a new way of investigating hidden patterns through both machine learning and statistical modelling. In this paper, we report on clustering analysis of student activities and comparative analysis on both behavioral patterns and demographical patterns between student subpopulations in the MOOC. Our approach allows for a deeper understanding of how MOOC students behave and achieve. Our findings may be used to design adaptive strategies towards an enhanced MOOC experience
Lifelong Property Price Prediction: A Case Study for the Toronto Real Estate Market
Authors: Hao Peng, Jian-Xin Li, Zheng Wang, Renyu Yang, Mingzhe Liu, Mingming Zhang, Philip S. Yu, Lifang He
Date: 2020-08-12 00:00:00

We present Luce, the first life-long predictive model for automated property valuation. Luce addresses two critical issues of property valuation: the lack of recent sold prices and the sparsity of house data. It is designed to operate on a limited volume of recent house transaction data. As a departure from prior work, Luce organizes the house data in a heterogeneous information network (HIN) where graph nodes are house entities and attributes that are important for house price valuation. We employ a Graph Convolutional Network (GCN) to extract the spatial information from the HIN for house-related data like geographical locations, and then use a Long Short Term Memory (LSTM) network to model the temporal dependencies for house transaction data over time. Unlike prior work, Luce can make effective use of the limited house transactions data in the past few months to update valuation information for all house entities within the HIN. By providing a complete and up-to-date house valuation dataset, Luce thus massively simplifies the downstream valuation task for the targeting properties. We demonstrate the benefit of Luce by applying it to large, real-life datasets obtained from the Toronto real estate market. Extensive experimental results show that Luce not only significantly outperforms prior property valuation methods but also often reaches and sometimes exceeds the valuation accuracy given by independent experts when using the actual realization price as the ground truth.
SIDOD: A Synthetic Image Dataset for 3D Object Pose Recognition with Distractors
Authors: Mona Jalal, Josef Spjut, Ben Boudaoud, Margrit Betke
Date: 2020-08-12 00:00:00
Tasks: Object, object-detection, Object Detection, Pose Estimation

We present a new, publicly-available image dataset generated by the NVIDIA Deep Learning Data Synthesizer intended for use in object detection, pose estimation, and tracking applications. This dataset contains 144k stereo image pairs that synthetically combine 18 camera viewpoints of three photorealistic virtual environments with up to 10 objects (chosen randomly from the 21 object models of the YCB dataset [1]) and flying distractors. Object and camera pose, scene lighting, and quantity of objects and distractors were randomized. Each provided view includes RGB, depth, segmentation, and surface normal images, all pixel level. We describe our approach for domain randomization and provide insight into the decisions that produced the dataset.
Estimating the Magnitude and Phase of Automotive Radar Signals under Multiple Interference Sources with Fully Convolutional Networks
Authors: Nicolae-Cătălin Ristea, Andrei Anghel, Radu Tudor Ionescu
Date: 2020-08-11 00:00:00
Tasks: Autonomous Driving

Radar sensors are gradually becoming a wide-spread equipment for road vehicles, playing a crucial role in autonomous driving and road safety. The broad adoption of radar sensors increases the chance of interference among sensors from different vehicles, generating corrupted range profiles and range-Doppler maps. In order to extract distance and velocity of multiple targets from range-Doppler maps, the interference affecting each range profile needs to be mitigated. In this paper, we propose a fully convolutional neural network for automotive radar interference mitigation. In order to train our network in a real-world scenario, we introduce a new data set of realistic automotive radar signals with multiple targets and multiple interferers. To our knowledge, we are the first to apply weight pruning in the automotive radar domain, obtaining superior results compared to the widely-used dropout. While most previous works successfully estimated the magnitude of automotive radar signals, we propose a deep learning model that can accurately estimate the phase. For instance, our novel approach reduces the phase estimation error with respect to the commonly-adopted zeroing technique by half, from 12.55 degrees to 6.58 degrees. Considering the lack of databases for automotive radar interference mitigation, we release as open source our large-scale data set that closely replicates the real-world automotive scenario for multiple interference cases, allowing others to objectively compare their future work in this domain. Our data set is available for download at: http://github.com/ristea/arim-v2.
So You Need Datasets for Your COVID-19 Detection Research Using Machine Learning?
Authors: Md Fahimuzzman Sohan
Date: 2020-08-11 00:00:00
Tasks: Articles, BIG-bench Machine Learning

Millions of people are infected by the coronavirus disease 2019 (COVID19) around the world. Machine Learning (ML) techniques are being used for COVID19 detection research from the beginning of the epidemic. This article represents the detailed information on frequently used datasets in COVID19 detection using Machine Learning (ML). We investigated 96 papers on COVID19 detection between January 2020 and June 2020. We extracted the information about used datasets from the articles and represented them here simultaneously. This investigation will help future researchers to find the COVID19 datasets without difficulty.
Deep Model-Based Reinforcement Learning for High-Dimensional Problems, a Survey
Authors: Aske Plaat, Walter Kosters, Mike Preuss
Date: 2020-08-11 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making, Transfer Learning, Vocal Bursts Intensity Prediction

Deep reinforcement learning has shown remarkable success in the past few years. Highly complex sequential decision making problems have been solved in tasks such as game playing and robotics. Unfortunately, the sample complexity of most deep reinforcement learning methods is high, precluding their use in some important applications. Model-based reinforcement learning creates an explicit model of the environment dynamics to reduce the need for environment samples. Current deep learning methods use high-capacity networks to solve high-dimensional problems. Unfortunately, high-capacity models typically require many samples, negating the potential benefit of lower sample complexity in model-based methods. A challenge for deep model-based methods is therefore to achieve high predictive power while maintaining low sample complexity. In recent years, many model-based methods have been introduced to address this challenge. In this paper, we survey the contemporary model-based landscape. First we discuss definitions and relations to other fields. We propose a taxonomy based on three approaches: using explicit planning on given transitions, using explicit planning on learned transitions, and end-to-end learning of both planning and transitions. We use these approaches to organize a comprehensive overview of important recent developments such as latent models. We describe methods and benchmarks, and we suggest directions for future work for each of the approaches. Among promising research directions are curriculum learning, uncertainty modeling, and use of latent models for transfer learning.
PneumoXttention: A CNN compensating for Human Fallibility when Detecting Pneumonia through CXR images with Attention
Authors: Sanskriti Singh
Date: 2020-08-11 00:00:00

Automatic Chest Radiograph X-ray (CXR) interpretation by machines is an important research topic of Artificial Intelligence. As part of my journey through the California Science Fair, I have developed an algorithm that can detect pneumonia from a CXR image to compensate for human fallibility. My algorithm, PneumoXttention, is an ensemble of two 13 layer convolutional neural network trained on the RSNA dataset, a dataset provided by the Radiological Society of North America, containing 26,684 frontal X-ray images split into the categories of pneumonia and no pneumonia. The dataset was annotated by many professional radiologists in North America. It achieved an impressive F1 score, 0.82, on the test set (20% random split of RSNA dataset) and completely compensated Human Radiologists on a random set of 25 test images drawn from RSNA and NIH. I don't have a direct comparison but Stanford's Chexnet has a F1 score of 0.435 on the NIH dataset for category Pneumonia.
A Deep Learning Approach for COVID-19 Trend Prediction
Authors: Tong Yang, Long Sha, Justin Li, Pengyu Hong
Date: 2020-08-09 00:00:00
Tasks: Deep Learning, Time Series, Time Series Analysis

In this work, we developed a deep learning model-based approach to forecast the spreading trend of SARS-CoV-2 in the United States. We implemented the designed model using the United States to confirm cases and state demographic data and achieved promising trend prediction results. The model incorporates demographic information and epidemic time-series data through a Gated Recurrent Unit structure. The identification of dominating demographic factors is delivered in the end.
Modeling the Field Value Variations and Field Interactions Simultaneously for Fraud Detection
Authors: Dongbo Xi, Bowen Song, Fuzhen Zhuang, Yongchun Zhu, Shuai Chen, Tianyi Zhang, Yuan Qi, Qing He
Date: 2020-08-08 00:00:00
Tasks: Fraud Detection, Management

With the explosive growth of e-commerce, online transaction fraud has become one of the biggest challenges for e-commerce platforms. The historical behaviors of users provide rich information for digging into the users' fraud risk. While considerable efforts have been made in this direction, a long-standing challenge is how to effectively exploit internal user information and provide explainable prediction results. In fact, the value variations of same field from different events and the interactions of different fields inside one event have proven to be strong indicators for fraudulent behaviors. In this paper, we propose the Dual Importance-aware Factorization Machines (DIFM), which exploits the internal field information among users' behavior sequence from dual perspectives, i.e., field value variations and field interactions simultaneously for fraud detection. The proposed model is deployed in the risk management system of one of the world's largest e-commerce platforms, which utilize it to provide real-time transaction fraud detection. Experimental results on real industrial data from different regions in the platform clearly demonstrate that our model achieves significant improvements compared with various state-of-the-art baseline models. Moreover, the DIFM could also give an insight into the explanation of the prediction results from dual perspectives.
A clarification of misconceptions, myths and desired status of artificial intelligence
Authors: Frank Emmert-Streib, Olli Yli-Harja, Matthias Dehmer
Date: 2020-08-03 00:00:00
Tasks: BIG-bench Machine Learning, Misconceptions

The field artificial intelligence (AI) has been founded over 65 years ago. Starting with great hopes and ambitious goals the field progressed though various stages of popularity and received recently a revival in the form of deep neural networks. Some problems of AI are that so far neither 'intelligence' nor the goals of AI are formally defined causing confusion when comparing AI to other fields. In this paper, we present a perspective on the desired and current status of AI in relation to machine learning and statistics and clarify common misconceptions and myths. Our discussion is intended to uncurtain the veil of vagueness surrounding AI to see its true countenance.
A Study of fastText Word Embedding Effects in Document Classification in Bangla Language
Authors: Pritom Mojumder, Mahmudul Hasan, Md. Faruque Hossain, K. M. Azharul Hasan
Date: 2020-07-30 00:00:00
Proceeding: International Conference on Cyber Security and Computer Science 2020 7
Tasks: Classification, Document Classification, General Classification, Lemmatization, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Natural Language Inference, Opinion Mining, Sentiment Analysis

Natural language processing is the current topic due to many important tasks like document classification, named entity recognition, opinion mining, sentiment analysis, textual entailment, etc. Such types of task in the Bangla language is also important. This research work endeavored to find out the word embedding of the Bengali language. Leveraging the fastText word embedding, it has shown significant performance in Bangla document classification without any prepossessing like lemmatization, stemming, and others. For the extrinsic evaluation of our word vectors, a classification problem-solving strategy has been used which showed an outstanding result. In the classification module, attempts have been made to classify 40 thousand News samples into 12 categories. For this purpose, three deep learning techniques have been used: Convolutional Neural Network (CNN), Bi-Directional LSTM (BLSTM) and Convolutional Bi-Directional LSTM (CBLSTM) alongside fastText. From the analogous study of all the parameters of every classifier implemented here, we found that the BLSTM technique is the most promising technique for this task. This technique achieved 91.49%, 87.87%, and 85.5% accuracies for Training, Testing, and Validation set, respectively.
Multi-Modal Neuroimaging Analysis and Visualization Tool (MMVT)
Authors: O. Felsenstein, N. Peled, E. Hahn, A. P. Rockhill, D. Frank, A M. Libster, Y. Nossenson, L. Folsom, T. Gholipour, K. Macadams, N. Rozengard, A. C. Paulk, D. Dougherty, S. S. Cash, A. S. Widge, M. Hämäläinen, S. Stufflebeam
Date: 2019-12-20 00:00:00
Tasks: Data Visualization, EEG, Electroencephalogram (EEG)

Sophisticated visualization tools are essential for the presentation and exploration of human neuroimaging data. While two-dimensional orthogonal views of neuroimaging data are conventionally used to display activity and statistical analysis, three-dimensional (3D) representation is useful for showing the spatial distribution of a functional network, as well as its temporal evolution. For these purposes, there is currently no open-source, 3D neuroimaging tool that can simultaneously visualize desired combinations of MRI, CT, EEG, MEG, fMRI, PET, and intracranial EEG (i.e., ECoG, depth electrodes, and DBS). Here we present the Multi-Modal Visualization Tool (MMVT), which is designed for researchers to interact with their neuroimaging functional and anatomical data through simultaneous visualization of these existing imaging modalities. MMVT contains two separate modules: The first includes complete stand-alone pre-processing pipelines, from raw data to statistical maps. The second module is an add-on to the open-source, 3D-rendering program Blender. It is an interactive graphical interface that enables users to simultaneously visualize multi-modality functional and statistical data on the cortex and in subcortical surfaces as well as the activity of invasive electrodes. This tool also enables highly accurate 3D visualization of neuroanatomy, including the location of invasive electrodes relative to brain structures. Each of the modules and module features can be integrated, separate from the tool, into existing data pipelines. MMVT leverages open-source software to build a comprehensive tool for data visualization and exploration This gives the tool a distinct advantage in both clinical and research domains as each has highly specialized visual and processing needs.