pwc-archive/papers-with-abstracts huggingface dataset:
SARS-Cov-2 RNA Sequence Classification Based on Territory Information
Authors: Jingwei Liu
Date: 2021-01-09 00:00:00
Tasks: Classification, General Classification

CovID-19 genetics analysis is critical to determine virus type,virus variant and evaluate vaccines. In this paper, SARS-Cov-2 RNA sequence analysis relative to region or territory is investigated. A uniform framework of sequence SVM model with various genetics length from short to long and mixed-bases is developed by projecting SARS-Cov-2 RNA sequence to different dimensional space, then scoring it according to the output probability of pre-trained SVM models to explore the territory or origin information of SARS-Cov-2. Different sample size ratio of training set and test set is also discussed in the data analysis. Two SARS-Cov-2 RNA classification tasks are constructed based on GISAID database, one is for mainland, Hongkong and Taiwan of China, and the other is a 6-class classification task (Africa, Asia, Europe, North American, South American\& Central American, Ocean) of 7 continents. For 3-class classification of China, the Top-1 accuracy rate can reach 82.45\% (train 60\%, test=40\%); For 2-class classification of China, the Top-1 accuracy rate can reach 97.35\% (train 80\%, test 20\%); For 6-class classification task of world, when the ratio of training set and test set is 20\% : 80\% , the Top-1 accuracy rate can achieve 30.30\%. And, some Top-N results are also given.
FakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarios
Authors: Vineet Mehta, Parul Gupta, Ramanathan Subramanian, Abhinav Dhall
Date: 2021-01-09 00:00:00
Tasks: Face Swapping

This paper proposes a new DeepFake detector FakeBuster for detecting impostors during video conferencing and manipulated faces on social media. FakeBuster is a standalone deep learning based solution, which enables a user to detect if another person's video is manipulated or spoofed during a video conferencing based meeting. This tool is independent of video conferencing solutions and has been tested with Zoom and Skype applications. It uses a 3D convolutional neural network for predicting video segment-wise fakeness scores. The network is trained on a combination of datasets such as Deeperforensics, DFDC, VoxCeleb, and deepfake videos created using locally captured (for video conferencing scenarios) images. This leads to different environments and perturbations in the dataset, which improves the generalization of the deepfake network.
Identifying Decision Points for Safe and Interpretable Reinforcement Learning in Hypotension Treatment
Authors: Kristine Zhang, Yuanheng Wang, Jianzhun Du, Brian Chu, Leo Anthony Celi, Ryan Kindle, Finale Doshi-Velez
Date: 2021-01-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

Many batch RL health applications first discretize time into fixed intervals. However, this discretization both loses resolution and forces a policy computation at each (potentially fine) interval. In this work, we develop a novel framework to compress continuous trajectories into a few, interpretable decision points --places where the batch data support multiple alternatives. We apply our approach to create recommendations from a cohort of hypotensive patients dataset. Our reduced state space results in faster planning and allows easy inspection by a clinical expert.
A Reconfigurable Convolution-in-Pixel CMOS Image Sensor Architecture
Authors: Ruibing Song, Kejie Huang, Zongsheng Wang, Haibin Shen
Date: 2021-01-09 00:00:00

The separation of the data capture and analysis in modern vision systems has led to a massive amount of data transfer between the end devices and cloud computers, resulting in long latency, slow response, and high power consumption. Efficient hardware architectures are under focused development to enable Artificial Intelligence (AI) at the resource-limited end sensing devices. One of the most promising solutions is to enable Processing-in-Pixel (PIP) scheme. However, the conventional schemes suffer from the low fill-factor issue. This paper proposes a PIP based CMOS sensor architecture, which allows convolution operation before the column readout circuit to significantly improve the image reading speed with much lower power consumption. The simulation results show that the proposed architecture could support the computing efficiency up to 11.65 TOPS/W at the 8-bit weight configuration, which is three times as high as the conventional schemes. The transistors required for each pixel are only 2.5T, significantly improving the fill-factor.
LightXML: Transformer with Dynamic Negative Sampling for High-Performance Extreme Multi-label Text Classification
Authors: Ting Jiang, Deqing Wang, Leilei Sun, Huayi Yang, Zhengyang Zhao, Fuzhen Zhuang
Date: 2021-01-09 00:00:00
Tasks: General Classification, Multi Label Text Classification, Multi-Label Text Classification, text-classification, Text Classification

Extreme Multi-label text Classification (XMC) is a task of finding the most relevant labels from a large label set. Nowadays deep learning-based methods have shown significant success in XMC. However, the existing methods (e.g., AttentionXML and X-Transformer etc) still suffer from 1) combining several models to train and predict for one dataset, and 2) sampling negative labels statically during the process of training label ranking model, which reduces both the efficiency and accuracy of the model. To address the above problems, we proposed LightXML, which adopts end-to-end training and dynamic negative labels sampling. In LightXML, we use generative cooperative networks to recall and rank labels, in which label recalling part generates negative and positive labels, and label ranking part distinguishes positive labels from these labels. Through these networks, negative labels are sampled dynamically during label ranking part training by feeding with the same text representation. Extensive experiments show that LightXML outperforms state-of-the-art methods in five extreme multi-label datasets with much smaller model size and lower computational complexity. In particular, on the Amazon dataset with 670K labels, LightXML can reduce the model size up to 72% compared to AttentionXML.
An Unsupervised Normalization Algorithm for Noisy Text: A Case Study for Information Retrieval and Stance Detection
Authors: Anurag Roy, Shalmoli Ghosh, Kripabandhu Ghosh, Saptarshi Ghosh
Date: 2021-01-09 00:00:00
Tasks: Information Retrieval, Optical Character Recognition (OCR), Retrieval, Stance Detection, Text Normalization

A large fraction of textual data available today contains various types of 'noise', such as OCR noise in digitized documents, noise due to informal writing style of users on microblogging sites, and so on. To enable tasks such as search/retrieval and classification over all the available data, we need robust algorithms for text normalization, i.e., for cleaning different kinds of noise in the text. There have been several efforts towards cleaning or normalizing noisy text; however, many of the existing text normalization methods are supervised and require language-dependent resources or large amounts of training data that is difficult to obtain. We propose an unsupervised algorithm for text normalization that does not need any training data / human intervention. The proposed algorithm is applicable to text over different languages, and can handle both machine-generated and human-generated noise. Experiments over several standard datasets show that text normalization through the proposed algorithm enables better retrieval and stance detection, as compared to that using several baseline text normalization methods. Implementation of our algorithm can be found at https://github.com/ranarag/UnsupClean.
Robust Blockchained Federated Learning with Model Validation and Proof-of-Stake Inspired Consensus
Authors: Hang Chen, Syed Ali Asif, Jihong Park, Chien-Chung Shen, Mehdi Bennis
Date: 2021-01-09 00:00:00
Tasks: Federated Learning

Federated learning (FL) is a promising distributed learning solution that only exchanges model parameters without revealing raw data. However, the centralized architecture of FL is vulnerable to the single point of failure. In addition, FL does not examine the legitimacy of local models, so even a small fraction of malicious devices can disrupt global training. To resolve these robustness issues of FL, in this paper, we propose a blockchain-based decentralized FL framework, termed VBFL, by exploiting two mechanisms in a blockchained architecture. First, we introduced a novel decentralized validation mechanism such that the legitimacy of local model updates is examined by individual validators. Second, we designed a dedicated proof-of-stake consensus mechanism where stake is more frequently rewarded to honest devices, which protects the legitimate local model updates by increasing their chances of dictating the blocks appended to the blockchain. Together, these solutions promote more federation within legitimate devices, enabling robust FL. Our emulation results of the MNIST classification corroborate that with 15% of malicious devices, VBFL achieves 87% accuracy, which is 7.4x higher than Vanilla FL.
FlashP: An Analytical Pipeline for Real-time Forecasting of Time-Series Relational Data
Authors: Shuyuan Yan, Bolin Ding, Wei Guo, Jingren Zhou, Zhewei Wei, Xiaowei Jiang, Sheng Xu
Date: 2021-01-09 00:00:00
Tasks: Time Series, Time Series Analysis

Interactive response time is important in analytical pipelines for users to explore a sufficient number of possibilities and make informed business decisions. We consider a forecasting pipeline with large volumes of high-dimensional time series data. Real-time forecasting can be conducted in two steps. First, we specify the part of data to be focused on and the measure to be predicted by slicing, dicing, and aggregating the data. Second, a forecasting model is trained on the aggregated results to predict the trend of the specified measure. While there are a number of forecasting models available, the first step is the performance bottleneck. A natural idea is to utilize sampling to obtain approximate aggregations in real time as the input to train the forecasting model. Our scalable real-time forecasting system FlashP (Flash Prediction) is built based on this idea, with two major challenges to be resolved in this paper: first, we need to figure out how approximate aggregations affect the fitting of forecasting models, and forecasting results; and second, accordingly, what sampling algorithms we should use to obtain these approximate aggregations and how large the samples are. We introduce a new sampling scheme, called GSW sampling, and analyze error bounds for estimating aggregations using GSW samples. We introduce how to construct compact GSW samples with the existence of multiple measures to be analyzed. We conduct experiments to evaluate our solution and compare it with alternatives on real data.
Estimation of Missing Data in Intelligent Transportation System
Authors: Bahareh Najafi, Saeedeh Parsaeefard, Alberto Leon-Garcia
Date: 2021-01-09 00:00:00
Tasks: Matrix Completion

Missing data is a challenge in many applications, including intelligent transportation systems (ITS). In this paper, we study traffic speed and travel time estimations in ITS, where portions of the collected data are missing due to sensor instability and communication errors at collection points. These practical issues can be remediated by missing data analysis, which are mainly categorized as either statistical or machine learning(ML)-based approaches. Statistical methods require the prior probability distribution of the data which is unknown in our application. Therefore, we focus on an ML-based approach, Multi-Directional Recurrent Neural Network (M-RNN). M-RNN utilizes both temporal and spatial characteristics of the data. We evaluate the effectiveness of this approach on a TomTom dataset containing spatio-temporal measurements of average vehicle speed and travel time in the Greater Toronto Area (GTA). We evaluate the method under various conditions, where the results demonstrate that M-RNN outperforms existing solutions,e.g., spline interpolation and matrix completion, by up to 58% decreases in Root Mean Square Error (RMSE).
Entropy-Based Uncertainty Calibration for Generalized Zero-Shot Learning
Authors: Zhi Chen, Zi Huang, Jingjing Li, Zheng Zhang
Date: 2021-01-09 00:00:00
Tasks: Generalized Zero-Shot Learning, Triplet, Zero-Shot Learning

Compared to conventional zero-shot learning (ZSL) where recognising unseen classes is the primary or only aim, the goal of generalized zero-shot learning (GZSL) is to recognise both seen and unseen classes. Most GZSL methods typically learn to synthesise visual representations from semantic information on the unseen classes. However, these types of models are prone to overfitting the seen classes, resulting in distribution overlap between the generated features of the seen and unseen classes. The overlapping region is filled with uncertainty as the model struggles to determine whether a test case from within the overlap is seen or unseen. Further, these generative methods suffer in scenarios with sparse training samples. The models struggle to learn the distribution of high dimensional visual features and, therefore, fail to capture the most discriminative inter-class features. To address these issues, in this paper, we propose a novel framework that leverages dual variational autoencoders with a triplet loss to learn discriminative latent features and applies the entropy-based calibration to minimize the uncertainty in the overlapped area between the seen and unseen classes. Specifically, the dual generative model with the triplet loss synthesises inter-class discriminative latent features that can be mapped from either visual or semantic space. To calibrate the uncertainty for seen classes, we calculate the entropy over the softmax probability distribution from a general classifier. With this approach, recognising the seen samples within the seen classes is relatively straightforward, and there is less risk that a seen sample will be misclassified into an unseen class in the overlapped region. Extensive experiments on six benchmark datasets demonstrate that the proposed method outperforms state-of-the-art approaches.
Combating Hostility: Covid-19 Fake News and Hostile Post Detection in Social Media
Authors: Omar Sharif, Eftekhar Hossain, Mohammed Moshiul Hoque
Date: 2021-01-09 00:00:00
Tasks: Binary Classification, Classification, Fake News Detection, General Classification, Multi-class Classification

This paper illustrates a detail description of the system and its results that developed as a part of the participation at CONSTRAINT shared task in AAAI-2021. The shared task comprises two tasks: a) COVID19 fake news detection in English b) Hostile post detection in Hindi. Task-A is a binary classification problem with fake and real class, while task-B is a multi-label multi-class classification task with five hostile classes (i.e. defame, fake, hate, offense, non-hostile). Various techniques are used to perform the classification task, including SVM, CNN, BiLSTM, and CNN+BiLSTM with tf-idf and Word2Vec embedding techniques. Results indicate that SVM with tf-idf features achieved the highest 94.39% weighted $f_1$ score on the test set in task-A. Label powerset SVM with n-gram features obtained the maximum coarse-grained and fine-grained $f_1$ score of 86.03% and 50.98% on the task-B test set respectively.
How to Train Your Energy-Based Models
Authors: Yang song, Diederik P. Kingma
Date: 2021-01-09 00:00:00

Energy-Based Models (EBMs), also known as non-normalized probabilistic models, specify probability density or mass functions up to an unknown normalizing constant. Unlike most other probabilistic models, EBMs do not place a restriction on the tractability of the normalizing constant, thus are more flexible to parameterize and can model a more expressive family of probability distributions. However, the unknown normalizing constant of EBMs makes training particularly difficult. Our goal is to provide a friendly introduction to modern approaches for EBM training. We start by explaining maximum likelihood training with Markov chain Monte Carlo (MCMC), and proceed to elaborate on MCMC-free approaches, including Score Matching (SM) and Noise Constrastive Estimation (NCE). We highlight theoretical connections among these three approaches, and end with a brief survey on alternative training methods, which are still under active research. Our tutorial is targeted at an audience with basic understanding of generative models who want to apply EBMs or start a research project in this direction.
Unifying Relational Sentence Generation and Retrieval for Medical Image Report Composition
Authors: Fuyu Wang, Xiaodan Liang, Lin Xu, Liang Lin
Date: 2021-01-09 00:00:00
Tasks: Retrieval, Sentence

Beyond generating long and topic-coherent paragraphs in traditional captioning tasks, the medical image report composition task poses more task-oriented challenges by requiring both the highly-accurate medical term diagnosis and multiple heterogeneous forms of information including impression and findings. Current methods often generate the most common sentences due to dataset bias for individual case, regardless of whether the sentences properly capture key entities and relationships. Such limitations severely hinder their applicability and generalization capability in medical report composition where the most critical sentences lie in the descriptions of abnormal diseases that are relatively rare. Moreover, some medical terms appearing in one report are often entangled with each other and co-occurred, e.g. symptoms associated with a specific disease. To enforce the semantic consistency of medical terms to be incorporated into the final reports and encourage the sentence generation for rare abnormal descriptions, we propose a novel framework that unifies template retrieval and sentence generation to handle both common and rare abnormality while ensuring the semantic-coherency among the detected medical terms. Specifically, our approach exploits hybrid-knowledge co-reasoning: i) explicit relationships among all abnormal medical terms to induce the visual attention learning and topic representation encoding for better topic-oriented symptoms descriptions; ii) adaptive generation mode that changes between the template retrieval and sentence generation according to a contextual topic encoder. Experimental results on two medical report benchmarks demonstrate the superiority of the proposed framework in terms of both human and metrics evaluation.
Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning
Authors: Yu Tian, Leonardo Zorron Cheng Tao Pu, Yuyuan Liu, Gabriel Maicas, Johan W. Verjans, Alastair D. Burt, Seon Ho Shin, Rajvinder Singh, Gustavo Carneiro
Date: 2021-01-09 00:00:00
Tasks: Anomaly Classification, Classification, General Classification

In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos. The detection of frames with polyps is formulated as a few-shot anomaly classification problem, where the training set is highly imbalanced with the large majority of frames consisting of normal images and a small minority comprising frames with polyps. Colonoscopy videos may contain blurry images and frames displaying feces and water jet sprays to clean the colon -- such frames can mistakenly be detected as anomalies, so we have implemented a classifier to reject these two types of frames before polyp detection takes place. Next, given a frame containing a polyp, our method localises (with a bounding box around the polyp) and classifies it into five different classes. Furthermore, we study a method to improve the reliability and interpretability of the classification result using uncertainty estimation and classification calibration. Classification uncertainty and calibration not only help improve classification accuracy by rejecting low-confidence and high-uncertain results, but can be used by doctors to decide how to decide on the classification of a polyp. All the proposed detection, localisation and classification methods are tested using large data sets and compared with relevant baseline approaches.
Investigating the Effect of Sensor Modalities in Multi-Sensor Detection-Prediction Models
Authors: Abhishek Mohta, Fang-Chieh Chou, Brian C. Becker, Carlos Vallespi-Gonzalez, Nemanja Djuric
Date: 2021-01-09 00:00:00
Tasks: motion prediction

Detection of surrounding objects and their motion prediction are critical components of a self-driving system. Recently proposed models that jointly address these tasks rely on a number of sensors to achieve state-of-the-art performance. However, this increases system complexity and may result in a brittle model that overfits to any single sensor modality while ignoring others, leading to reduced generalization. We focus on this important problem and analyze the contribution of sensor modalities towards the model performance. In addition, we investigate the use of sensor dropout to mitigate the above-mentioned issues, leading to a more robust, better-performing model on real-world driving data.
Identifying Human Edited Images using a CNN
Authors: Jordan Lee, Willy Lin, Konstantinos Ntalis, Anirudh Shah, William Tung, Maxwell Wulff
Date: 2021-01-09 00:00:00

Most non-professional photo manipulations are not made using propriety software like Adobe Photoshop, which is expensive and complicated to use for the average consumer selfie-taker or meme-maker. Instead, these individuals opt for user friendly mobile applications like FaceTune and Pixlr to make human face edits and alterations. Unfortunately, there is no existing dataset to train a model to classify these type of manipulations. In this paper, we present a generative model that approximates the distribution of human face edits and a method for detecting Facetune and Pixlr manipulations to human faces.
Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETs
Authors: Saeed Kaviani, Bo Ryu, Ejaz Ahmed, Kevin A. Larson, Anh Le, Alex Yahja, Jae H. Kim
Date: 2021-01-09 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

Highly dynamic mobile ad-hoc networks (MANETs) are continuing to serve as one of the most challenging environments to develop and deploy robust, efficient, and scalable routing protocols. In this paper, we present DeepCQ+ routing which, in a novel manner, integrates emerging multi-agent deep reinforcement learning (MADRL) techniques into existing Q-learning-based routing protocols and their variants, and achieves persistently higher performance across a wide range of MANET configurations while training only on a limited range of network parameters and conditions. Quantitatively, DeepCQ+ shows consistently higher end-to-end throughput with lower overhead compared to its Q-learning-based counterparts with the overall gain of 10-15% in its efficiency. Qualitatively and more significantly, DeepCQ+ maintains remarkably similar performance gains under many scenarios that it was not trained for in terms of network sizes, mobility conditions, and traffic dynamics. To the best of our knowledge, this is the first successful demonstration of MADRL for the MANET routing problem that achieves and maintains a high degree of scalability and robustness even in the environments that are outside the trained range of scenarios. This implies that the proposed hybrid design approach of DeepCQ+ that combines MADRL and Q-learning significantly increases its practicality and explainability because the real-world MANET environment will likely vary outside the trained range of MANET scenarios.