pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Armed Bandits with Dependent Arms
Authors: Rahul Singh, Fang Liu, Yin Sun, Ness Shroff
Date: 2020-10-13 00:00:00
Tasks: Multi-Armed Bandits

We study a variant of the classical multi-armed bandit problem (MABP) which we call as Multi-Armed Bandits with dependent arms. More specifically, multiple arms are grouped together to form a cluster, and the reward distributions of arms belonging to the same cluster are known functions of an unknown parameter that is a characteristic of the cluster. Thus, pulling an arm $i$ not only reveals information about its own reward distribution, but also about all those arms that share the same cluster with arm $i$. This "correlation" amongst the arms complicates the exploration-exploitation trade-off that is encountered in the MABP because the observation dependencies allow us to test simultaneously multiple hypotheses regarding the optimality of an arm. We develop learning algorithms based on the UCB principle which utilize these additional side observations appropriately while performing exploration-exploitation trade-off. We show that the regret of our algorithms grows as $O(K\log T)$, where $K$ is the number of clusters. In contrast, for an algorithm such as the vanilla UCB that is optimal for the classical MABP and does not utilize these dependencies, the regret scales as $O(M\log T)$ where $M$ is the number of arms.
Information-Theoretic Bounds on Transfer Generalization Gap Based on Jensen-Shannon Divergence
Authors: Sharu Theresa Jose, Osvaldo Simeone
Date: 2020-10-13 00:00:00
Tasks: Transfer Learning

In transfer learning, training and testing data sets are drawn from different data distributions. The transfer generalization gap is the difference between the population loss on the target data distribution and the training loss. The training data set generally includes data drawn from both source and target distributions. This work presents novel information-theoretic upper bounds on the average transfer generalization gap that capture $(i)$ the domain shift between the target data distribution $P'_Z$ and the source distribution $P_Z$ through a two-parameter family of generalized $(\alpha_1,\alpha_2)$-Jensen-Shannon (JS) divergences; and $(ii)$ the sensitivity of the transfer learner output $W$ to each individual sample of the data set $Z_i$ via the mutual information $I(W;Z_i)$. For $\alpha_1 \in (0,1)$, the $(\alpha_1,\alpha_2)$-JS divergence can be bounded even when the support of $P_Z$ is not included in that of $P'_Z$. This contrasts the Kullback-Leibler (KL) divergence $D_{KL}(P_Z||P'_Z)$-based bounds of Wu et al. [1], which are vacuous under this assumption. Moreover, the obtained bounds hold for unbounded loss functions with bounded cumulant generating functions, unlike the $\phi$-divergence based bound of Wu et al. [1]. We also obtain new upper bounds on the average transfer excess risk in terms of the $(\alpha_1,\alpha_2)$-JS divergence for empirical weighted risk minimization (EWRM), which minimizes the weighted average training losses over source and target data sets. Finally, we provide a numerical example to illustrate the merits of the introduced bounds.
Mycorrhiza: Genotype Assignment usingPhylogenetic Networks
Authors: Jeremy Georges-Filteau, Richard C. Hamelin, Mathieu Blanchette
Date: 2020-10-14 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction, Population Assignment

Motivation The genotype assignment problem consists of predicting, from the genotype of an individual, which of a known set of populations it originated from. The problem arises in a variety of contexts, including wildlife forensics, invasive species detection and biodiversity monitoring. Existing approaches perform well under ideal conditions but are sensitive to a variety of common violations of the assumptions they rely on. Results In this article, we introduce Mycorrhiza, a machine learning approach for the genotype assignment problem. Our algorithm makes use of phylogenetic networks to engineer features that encode the evolutionary relationships among samples. Those features are then used as input to a Random Forests classifier. The classification accuracy was assessed on multiple published empirical SNP, microsatellite or consensus sequence datasets with wide ranges of size, geographical distribution and population structure and on simulated datasets. It compared favorably against widely used assessment tests or mixture analysis methods such as STRUCTURE and Admixture, and against another machine-learning based approach using principal component analysis for dimensionality reduction. Mycorrhiza yields particularly significant gains on datasets with a large average fixation index (FST) or deviation from the Hardy-Weinberg equilibrium. Moreover, the phylogenetic network approach estimates mixture proportions with good accuracy.
Determination of the Interface between Amorphous Insulator and Crystalline 4H-SiC in Transmission Electron Microscope Image by using Convolutional Neural Network
Authors: Hironori Yoshioka, Tomonori Honda
Date: 2020-10-14 00:00:00

A rough interface seems to be one of the possible reasons for low channel mobility (conductivity) in SiC MOSFETs. To evaluate the mobility by interface roughness, we drew a boundary line between amorphous insulator and crystalline 4H-SiC in a cross-sectional image obtained by a transmission electron microscope (TEM), by using the deep learning approach of convolutional neural network (CNN). We show that the CNN model recognizes the interface very well, even when the interface is too rough to draw the boundary line manually. Power spectral density of interface roughness was calculated.
Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19
Authors: Ding Wang, Fan Zuo, Jingqin Gao, Yueshuai He, Zilin Bian, Suzana Duran Bernardes, Chaekuk Na, Jingxing Wang, John Petinos, Kaan Ozbay, Joseph Y. J. Chow, Shri Iyer, Hani Nassif, Xuegang Jeff Ban
Date: 2020-09-23 00:00:00

The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. This edition of the white paper updates travel trends and highlights an agent-based simulation model's results to predict the impact of proposed phased reopening strategies. It also introduces a real-time video processing method to measure social distancing through cameras on city streets.
Rapidly Deploying a Neural Search Engine for the COVID-19 Open Research Dataset
Authors: Edwin Zhang, Nikhil Gupta, Rodrigo Nogueira, Kyunghyun Cho, Jimmy Lin
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 7
Tasks: Decision Making

The Neural Covidex is a search engine that exploits the latest neural ranking architectures to provide information access to the COVID-19 Open Research Dataset (CORD-19) curated by the Allen Institute for AI. It exists as part of a suite of tools we have developed to help domain experts tackle the ongoing global pandemic. We hope that improved information access capabilities to the scientific literature can inform evidence-based decision making and insight generation.
Self-supervised context-aware COVID-19 document exploration through atlas grounding
Authors: Dusan Grujicic, Gorjan Radevski, Tinne Tuytelaars, Matthew Blaschko
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 7
Tasks: Retrieval

In this paper, we aim to develop a self-supervised grounding of Covid-related medical text based on the actual spatial relationships between the referred anatomical concepts. More specifically, we learn to project sentences into a physical space defined by a three-dimensional anatomical atlas, allowing for a visual approach to navigating Covid-related literature. We design a straightforward and empirically effective training objective to reduce the curated data dependency issue. We use BERT as the main building block of our model and perform a quantitative analysis that demonstrates that the model learns a context-aware mapping. We illustrate two potential use-cases for our approach, one in interactive, 3D data exploration, and the other in document retrieval. To accelerate research in this direction, we make public all trained models, codebase and the developed tools, which can be accessed at https://github.com/gorjanradevski/macchina/.
Information Retrieval and Extraction on COVID-19 Clinical Articles Using Graph Community Detection and Bio-BERT Embeddings
Authors: Debasmita Das, Yatin Katyal, Janu Verma, Shashank Dubey, AakashDeep Singh, Kushagra Agarwal, Sourojit Bhaduri, RajeshKumar Ranjan
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 7
Tasks: Articles, Community Detection, Extractive Summarization, Information Retrieval, Retrieval, Sentence, Sentence Embeddings

In this paper, we present an information retrieval system on a corpus of scientific articles related to COVID-19. We build a similarity network on the articles where similarity is determined via shared citations and biological domain-specific sentence embeddings. Ego-splitting community detection on the article network is employed to cluster the articles and then the queries are matched with the clusters. Extractive summarization using BERT and PageRank methods is used to provide responses to the query. We also provide a Question-Answer bot on a small set of intents to demonstrate the efficacy of our model for an information extraction module.
Jennifer for COVID-19: An NLP-Powered Chatbot Built for the People and by the People to Combat Misinformation
Authors: Yunyao Li, Gr, Tyrone ison, Patricia Silveyra, Ali Douraghy, Xinyu Guan, Thomas Kieselbach, Chengkai Li, Haiqi Zhang
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 7
Tasks: Chatbot, Misinformation

Just as SARS-CoV-2, a new form of coronavirus continues to infect a growing number of people around the world, harmful misinformation about the outbreak also continues to spread. With the goal of combating misinformation, we designed and built Jennifer{--}a chatbot maintained by a global group of volunteers. With Jennifer, we hope to learn whether public information from reputable sources could be more effectively organized and shared in the wake of a crisis as well as to understand issues that the public were most immediately curious about. In this paper, we introduce Jennifer and describe the design of this proof-of-principle system. We also present lessons learned and discuss open challenges. Finally, to facilitate future research, we release COVID-19 Question Bank, a dataset of 3,924 COVID-19-related questions in 944 groups, gathered from our users and volunteers.
A Natural Language Processing System for National COVID-19 Surveillance in the US Department of Veterans Affairs
Authors: Alec Chapman, Kelly Peterson, Augie Turano, Tam{\'a}ra Box, Katherine Wallace, Makoto Jones
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 7

Timely and accurate accounting of positive cases has been an important part of the response to the COVID-19 pandemic. While most positive cases within Veterans Affairs (VA) are identified through structured laboratory results, some patients are tested or diagnosed outside VA so their clinical status is documented only in free-text narratives. We developed a Natural Language Processing pipeline for identifying positively diagnosed COVID19 patients and deployed this system to accelerate chart review. As part of the VA national response to COVID-19, this process identified 6,360 positive cases which did not have corresponding laboratory data. These cases accounted for 36.1{\%} of total confirmed positive cases in VA to date. With available data, performance of the system is estimated as 82.4{\%} precision and 94.2{\%} recall. A public-facing implementation is released as open source and available to the community.
Estimating the effect of COVID-19 on mental health: Linguistic indicators of depression during a global pandemic
Authors: JT Wolohan
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 7

This preliminary analysis uses a deep LSTM neural network with fastText embeddings to predict population rates of depression on Reddit in order to estimate the effect of COVID-19 on mental health. We find that year over year, depression rates on Reddit are up 50{\%} , suggesting a 15-million person increase in the number of depressed Americans and a {\$}7.5 billion increase in depression related spending. This finding suggests that utility in NLP approaches to longitudinal public-health surveillance.
Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic
Authors: Anna Kruspe, Matthias H{\"a}berle, Iona Kuhn, Xiao Xiang Zhu
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 7
Tasks: Sentence, Sentence Embeddings, Sentiment Analysis

In this paper, we analyze Twitter messages (tweets) collected during the first months of the COVID-19 pandemic in Europe with regard to their sentiment. This is implemented with a neural network for sentiment analysis using multilingual sentence embeddings. We separate the results by country of origin, and correlate their temporal development with events in those countries. This allows us to study the effect of the situation on people{'}s moods. We see, for example, that lockdown announcements correlate with a deterioration of mood in almost all surveyed countries, which recovers within a short time span.
COVID-19 and Arabic Twitter: How can Arab World Governments and Public Health Organizations Learn from Social Media?
Authors: Lama Alsudias, Paul Rayson
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 7
Tasks: BIG-bench Machine Learning, Rumour Detection, Word Embeddings

In March 2020, the World Health Organization announced the COVID-19 outbreak as a pandemic. Most previous social media related research has been on English tweets and COVID-19. In this study, we collect approximately 1 million Arabic tweets from the Twitter streaming API related to COVID-19. Focussing on outcomes that we believe will be useful for Public Health Organizations, we analyse them in three different ways: identifying the topics discussed during the period, detecting rumours, and predicting the source of the tweets. We use the k-means algorithm for the first goal with k=5. The topics discussed can be grouped as follows: COVID-19 statistics, prayers for God, COVID-19 locations, advise and education for prevention, and advertising. We sample 2000 tweets and label them manually for false information, correct information, and unrelated. Then, we apply three different machine learning algorithms, Logistic Regression, Support Vector Classification, and Na{\"\i}ve Bayes with two sets of features, word frequency approach and word embeddings. We find that Machine Learning classifiers are able to correctly identify the rumour related tweets with 84{\%} accuracy. We also try to predict the source of the rumour related tweets depending on our previous model which is about classifying tweets into five categories: academic, media, government, health professional, and public. Around (60{\%}) of the rumour related tweets are classified as written by health professionals and academics.
NLP-based Feature Extraction for the Detection of COVID-19 Misinformation Videos on YouTube
Authors: Juan Carlos Medina Serrano, Orestis Papakyriakopoulos, Simon Hegelich
Date: 2020-07-01 00:00:00
Proceeding: ACL 2020 7
Tasks: Misinformation, Transfer Learning, Video Classification

We present a simple NLP methodology for detecting COVID-19 misinformation videos on YouTube by leveraging user comments. We use transfer learning pre-trained models to generate a multi-label classifier that can categorize conspiratorial content. We use the percentage of misinformation comments on each video as a new feature for video classification.
An afferent white matter pathway from the pulvinar to the amygdala facilitates fear recognition
Authors: Jessica McFadyen, Jason B Mattingley, Marta I Garrido
Date: 2019-01-16 00:00:00
Tasks: Face Recognition

Our ability to rapidly detect threats is thought to be subserved by a subcortical pathway that quickly conveys visual information to the amygdala. This neural shortcut has been demonstrated in animals but has rarely been shown in the human brain. Importantly, it remains unclear whether such a pathway might influence neural activity and behavior. We conducted a multimodal neuroimaging study of 622 participants from the Human Connectome Project. We applied probabilistic tractography to diffusion-weighted images, reconstructing a subcortical pathway to the amygdala from the superior colliculus via the pulvinar. We then computationally modeled the flow of haemodynamic activity during a face-viewing task and found evidence for a functionally afferent pulvinar-amygdala pathway. Critically, individuals with greater fibre density in this pathway also had stronger dynamic coupling and enhanced fearful face recognition. Our findings provide converging evidence for the recruitment of an afferent subcortical pulvinar connection to the amygdala that facilitates fear recognition.
GEV Beamforming Supported by DOA-based Masks Generated on Pairs of Microphones
Authors: Francois Grondin, Jean-Samuel Lauzon, Jonathan Vincent, Francois Michaud
Date: 2020-05-19 00:00:00
Tasks: speech-recognition, Speech Recognition, Speech Separation

Distant speech processing is a challenging task, especially when dealing with the cocktail party effect. Sound source separation is thus often required as a preprocessing step prior to speech recognition to improve the signal to distortion ratio (SDR). Recently, a combination of beamforming and speech separation networks have been proposed to improve the target source quality in the direction of arrival of interest. However, with this type of approach, the neural network needs to be trained in advance for a specific microphone array geometry, which limits versatility when adding/removing microphones, or changing the shape of the array. The solution presented in this paper is to train a neural network on pairs of microphones with different spacing and acoustic environmental conditions, and then use this network to estimate a time-frequency mask from all the pairs of microphones forming the array with an arbitrary shape. Using this mask, the target and noise covariance matrices can be estimated, and then used to perform generalized eigenvalue (GEV) beamforming. Results show that the proposed approach improves the SDR from 4.78 dB to 7.69 dB on average, for various microphone array geometries that correspond to commercially available hardware.
Using Convolutional Neural Networks for Classification of Malware represented as Images
Authors: Daniel Gibert, Carles Mateu, Jordi Planes & Ramon Vicens
Date: 2018-08-27 00:00:00
Tasks: General Classification, Malware Classification

The number of malicious files detected every year are counted by millions. One of the main reasons for these high volumes of different files is the fact that, in order to evade detection, malware authors add mutation. This means that malicious files belonging to the same family, with the same malicious behavior, are constantly modified or obfuscated using several techniques, in such a way that they look like different files. In order to be effective in analyzing and classifying such large amounts of files, we need to be able to categorize them into groups and identify their respective families on the basis of their behavior. In this paper, malicious software is visualized as gray scale images since its ability to capture minor changes while retaining the global structure helps to detect variations. Motivated by the visual similarity between malware samples of the same family, we propose a file agnostic deep learning approach for malware categorization to efficiently group malicious software into families based on a set of discriminant patterns extracted from their visualization as images. The suitability of our approach is evaluated against two benchmarks: the MalImg dataset and the Microsoft Malware Classification Challenge dataset. Experimental comparison demonstrates its superior performance with respect to state-of-the-art techniques.
Convolutional Neural Network for Classification of Malware Assembly Code
Authors: Daniel Gibert, Javier Béjar, Carles Mateu, Jordi Planes, Daniel Solis, Ramon Vicens
Date: 2017-10-27 00:00:00
Tasks: Classification, General Classification, Malware Classification, Malware Detection

Traditional signature-based methods have started becoming inadequnate to deal with next generation malware which utilize sophisticated obfuscation (polymorphic and metamorphic) techniques to evade detection. Recently, research efforts have been conducted on malware detection and classification by applying machine learning techniques. Despite them, most methods are build on shallow learning architectures and rely on the extraction of hand-crafted features. In this paper, based on assembly language code extracted from disassembled binary files and embedded into vectors, we present a convolutional neural network architecture to learn a set of discriminative patterns able to cluster malware files amongst families. To demonstrate the suitability of our approach we evaluated our model on the data provided by Microsoft for the BigData Innovators Gathering 2015 Anti-Malware Prediction Challenge. Experiments show that the method achieves competitive results without relying on the manual extraction of features and is resilient to the most common obfuscation techniques.
Classification of Malware by Using Structural Entropy on Convolutional Neural Networks
Authors: Daniel Gibert, Carles Mateu, Jordi Planes, Ramon Vicens
Date: 2018-04-27 00:00:00
Tasks: General Classification, Malware Classification

he number of malicious programs has grown both in number and in sophistication. Analyzing the malicious intent of
vast amounts of data requires huge resources and thus, effective categorization of malware is required. In this paper,
the content of a malicious program is represented as an entropy stream, where each value describes the amount of entropy of a small chunk of code in a specific location of the file. Wavelet transforms are then applied to this entropy signal to
describe the variation in the entropic energy. Motivated by the visual similarity between streams of entropy of malicious
software belonging to the same family, we propose a file agnostic deep learning approach for categorization of malware.
Our method exploits the fact that most variants are generated by using common obfuscation techniques and that compression and encryption algorithms retain some properties present in the original code. This allows us to find discriminative patterns that almost all variants in a family share. Our method has been evaluated using the data provided by Microsoft for the BigData Innovators Gathering Anti-Malware Prediction Challenge, and achieved promising results in comparison with the State of the Art.
The similarity metric
Authors: Ming Li, Xin Chen, Xin Li, Bin Ma, Paul Vitanyi
Date: 2001-11-20 00:00:00

A new class of distances appropriate for measuring similarity relations between sequences, say one type of similarity per distance, is studied. We propose a new ``normalized information distance'', based on the noncomputable notion of Kolmogorov complexity, and show that it is in this class and it minorizes every computable distance in the class (that is, it is universal in that it discovers all computable similarities). We demonstrate that it is a metric and call it the {\em similarity metric}. This theory forms the foundation for a new practical tool. To evidence generality and robustness we give two distinctive applications in widely divergent areas using standard compression programs like gzip and GenCompress. First, we compare whole mitochondrial genomes and infer their evolutionary history. This results in a first completely automatic computed whole mitochondrial phylogeny tree. Secondly, we fully automatically compute the language tree of 52 different languages.